# Raw Responses

Every raw model response content is preserved below. API keys and request bodies are not included.

## T0001 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":"1 of 2","cited_scenario_specific_evidence":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 119, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0056375, "cost_details": {"upstream_inference_completions_cost": 0.003927, "upstream_inference_cost": 0.0056375, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 430}`

## T0002 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":100,"stated_reasoning":"Model B is better on every stated criterion: capability, relevance behavior, cost, and P95 latency. Its dramatically lower cost and latency are especially valuable for high-volume, low-consequence automation.","cited_overall_rank":"Model B ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 148, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0065945, "cost_details": {"upstream_inference_completions_cost": 0.004884, "upstream_inference_cost": 0.0065945, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 459}`

## T0003 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":100,"stated_reasoning":"Model A is superior on every stated criterion, making it the clear choice for high-volume, low-consequence automations.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":"Model A has slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), dramatically lower benchmark cost ($1.35 vs $86.55), and lower P95 latency (2.3s vs 7.56s)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 166, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 28}, "cost": 0.0071885, "cost_details": {"upstream_inference_completions_cost": 0.005478, "upstream_inference_cost": 0.0071885, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 477}`

## T0004 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency. These advantages are especially important for high-volume, routine, low-consequence automations.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 137, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0062315, "cost_details": {"upstream_inference_completions_cost": 0.004521, "upstream_inference_cost": 0.0062315, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 448}`

## T0005 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is superior or effectively tied across every stated requirement: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower cost, and much lower P95 latency.","cited_overall_rank":"Model A ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 146, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0065285, "cost_details": {"upstream_inference_completions_cost": 0.004818, "upstream_inference_cost": 0.0065285, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 457}`

## T0006 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":100,"stated_reasoning":"Model A is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower cost, and much lower P95 latency.","cited_overall_rank":"Model A ranks first overall because it strictly dominates Model B across all four metrics.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_p95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 149, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0066275, "cost_details": {"upstream_inference_completions_cost": 0.004917, "upstream_inference_cost": 0.0066275, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 460}`

## T0007 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":100,"stated_reasoning":"Model B is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":"Model B ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0064625, "cost_details": {"upstream_inference_completions_cost": 0.004752, "upstream_inference_cost": 0.0064625, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 455}`

## T0008 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower cost, and much lower P95 latency. These advantages strongly fit a high-volume, low-consequence automation environment.","cited_overall_rank":"Model A ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 157, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0068915, "cost_details": {"upstream_inference_completions_cost": 0.005181, "upstream_inference_cost": 0.0068915, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 468}`

## T0009 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":100,"stated_reasoning":"Model A is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower cost, and much lower P95 latency. This makes it clearly preferable for high-volume, low-consequence automations.","cited_overall_rank":"Model A ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_p95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 158, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0069245, "cost_details": {"upstream_inference_completions_cost": 0.005214, "upstream_inference_cost": 0.0069245, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 469}`

## T0010 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":100,"stated_reasoning":"Model A is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":"Model A ranks first overall among the two profiles.","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0064625, "cost_details": {"upstream_inference_completions_cost": 0.004752, "upstream_inference_cost": 0.0064625, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 455}`

## T0011 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A outperforms Model B on every stated criterion, with especially large advantages in cost, tail latency, and relevance behavior.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":"Single-turn capability 85.361 vs 85.177; relevance/irrelevance 84.215 vs 73.61; benchmark cost $1.35 vs $86.55; P95 latency 2.3s vs 7.56s."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 173, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 45}, "cost": 0.0074195, "cost_details": {"upstream_inference_completions_cost": 0.005709, "upstream_inference_cost": 0.0074195, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 484}`

## T0012 - OpenAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every stated criterion, making it the clear choice for high-volume, low-consequence automations.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 129, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0059675, "cost_details": {"upstream_inference_completions_cost": 0.004257, "upstream_inference_cost": 0.0059675, "upstream_inference_prompt_cost": 0.0017105}, "is_byok": false, "prompt_tokens": 311, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 440}`

## T0013 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is the better fit for high-volume, low-consequence automations because it has slightly stronger single-turn capability, substantially better relevance behavior, dramatically lower cost, and much lower P95 latency. These scenario-specific advantages outweigh its weaker overall BFCL rank.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 215, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 31}, "cost": 0.0089595, "cost_details": {"upstream_inference_completions_cost": 0.007095, "upstream_inference_cost": 0.0089595, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 554}`

## T0014 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every scenario-specific criterion, making it better suited to high-volume, low-consequence automations despite its lower general BFCL rank.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 206, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 43}, "cost": 0.0086625, "cost_details": {"upstream_inference_completions_cost": 0.006798, "upstream_inference_cost": 0.0086625, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 545}`

## T0015 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is better suited to the high-volume, low-consequence workflow because it slightly exceeds Model B in single-turn capability while substantially outperforming it in relevance/irrelevance behavior, cost, and P95 latency.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 208, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 33}, "cost": 0.0087285, "cost_details": {"upstream_inference_completions_cost": 0.006864, "upstream_inference_cost": 0.0087285, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 547}`

## T0016 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is better suited to this high-volume, low-consequence workflow because it slightly leads in single-turn capability, substantially improves relevance/irrelevance behavior, and has dramatically lower cost and tail latency. These scenario-specific advantages outweigh Model B's stronger overall BFCL rank.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 231, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 45}, "cost": 0.0094875, "cost_details": {"upstream_inference_completions_cost": 0.007623, "upstream_inference_cost": 0.0094875, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 570}`

## T0017 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is superior on every scenario-specific criterion, making it the better fit for high-volume, low-consequence automations despite its much lower overall BFCL rank.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 209, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 44}, "cost": 0.0087615, "cost_details": {"upstream_inference_completions_cost": 0.006897, "upstream_inference_cost": 0.0087615, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 548}`

## T0018 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is the better fit for high-volume, low-consequence automations because it slightly outperforms Model A on single-turn capability, substantially improves relevance behavior, and has dramatically lower cost and P95 latency. These scenario-specific advantages outweigh its weaker general BFCL rank.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 221, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 34}, "cost": 0.0091575, "cost_details": {"upstream_inference_completions_cost": 0.007293, "upstream_inference_cost": 0.0091575, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 560}`

## T0019 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better suited to high-volume, low-consequence automation because it slightly exceeds Model A in single-turn capability, substantially improves relevance behavior, and has dramatically lower cost and P95 latency. These scenario-specific advantages outweigh its weaker general BFCL rank.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 213, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 30}, "cost": 0.0088935, "cost_details": {"upstream_inference_completions_cost": 0.007029, "upstream_inference_cost": 0.0088935, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 552}`

## T0020 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is the better fit for high-volume, low-consequence automation because it slightly exceeds Model A in single-turn capability, substantially improves relevance behavior, and has dramatically lower cost and P95 latency. These scenario-specific advantages outweigh its weaker general BFCL rank.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 221, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 37}, "cost": 0.0091575, "cost_details": {"upstream_inference_completions_cost": 0.007293, "upstream_inference_cost": 0.0091575, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 560}`

## T0021 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better suited to high-volume, low-consequence automations because it slightly outperforms on single-turn calls, substantially outperforms on relevance/irrelevance behavior, and is dramatically cheaper and faster. These scenario-specific advantages outweigh its lower overall BFCL rank.","cited_overall_rank":{"Model B":47,"Model A":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model B":85.361,"Model A":85.177},"relevance_irrelevance_behavior":{"Model B":84.215,"Model A":73.61},"benchmark_cost":{"Model B":"$1.35","Model A":"$86.55"},"measured_P95_latency":{"Model B":"2.3 seconds","Model A":"7.56 seconds"}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 225, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 36}, "cost": 0.0092895, "cost_details": {"upstream_inference_completions_cost": 0.007425, "upstream_inference_cost": 0.0092895, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 564}`

## T0022 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":98,"stated_reasoning":"Model A is the better fit for high-volume, low-consequence automation: it slightly exceeds Model B in single-turn capability, substantially outperforms it on relevance/irrelevance behavior, and is dramatically cheaper and faster at P95 latency. These scenario-specific advantages outweigh Model B's stronger overall BFCL rank.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost":{"Model A":"$1.35","Model B":"$86.55"},"measured_P95_latency":{"Model A":"2.3 seconds","Model B":"7.56 seconds"}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 223, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 27}, "cost": 0.0092235, "cost_details": {"upstream_inference_completions_cost": 0.007359, "upstream_inference_cost": 0.0092235, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 562}`

## T0023 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better suited to this high-volume, low-consequence workflow because it matches or exceeds Model A on every scenario-specific criterion while being dramatically cheaper and faster. The general BFCL rank does not outweigh the directly relevant evidence.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost":{"Model A":"$86.55","Model B":"$1.35"},"measured_P95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 209, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 30}, "cost": 0.0087615, "cost_details": {"upstream_inference_completions_cost": 0.006897, "upstream_inference_cost": 0.0087615, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 548}`

## T0024 - OpenAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":98,"stated_reasoning":"Model A is better suited to this high-volume, low-consequence workflow because it slightly exceeds Model B in single-turn capability, substantially outperforms it on relevance/irrelevance behavior, and has dramatically lower cost and P95 latency.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 209, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 30}, "cost": 0.0087615, "cost_details": {"upstream_inference_completions_cost": 0.006897, "upstream_inference_cost": 0.0087615, "upstream_inference_prompt_cost": 0.0018645}, "is_byok": false, "prompt_tokens": 339, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 548}`

## T0025 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 151, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 32}, "cost": 0.0067045, "cost_details": {"upstream_inference_completions_cost": 0.004983, "upstream_inference_cost": 0.0067045, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 464}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":98,"stated_reasoning":"Model B remains the stronger fit for this high-volume, low-consequence workload because it outperforms Model A on every scenario-specific criterion. Model A's superior general BFCL rank does not outweigh Model B's directly measured advantages for this use case.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 219, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 29}, "cost": 0.0101035, "cost_details": {"upstream_inference_completions_cost": 0.007227, "upstream_inference_cost": 0.0101035, "upstream_inference_prompt_cost": 0.0028765}, "is_byok": false, "prompt_tokens": 523, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 742}`

## T0026 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 158, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0069355, "cost_details": {"upstream_inference_completions_cost": 0.005214, "upstream_inference_cost": 0.0069355, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 471}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B remains the better fit despite its lower overall BFCL rank because it outperforms Model A on every scenario-specific requirement, with especially large advantages in relevance behavior, cost, and P95 latency.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.177,"Model B":85.361},"relevance_irrelevance_behavior":{"Model A":73.61,"Model B":84.215},"benchmark_cost_usd":{"Model A":86.55,"Model B":1.35},"measured_p95_latency_seconds":{"Model A":7.56,"Model B":2.3}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 216, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 35}, "cost": 0.01023, "cost_details": {"upstream_inference_completions_cost": 0.007128, "upstream_inference_cost": 0.01023, "upstream_inference_prompt_cost": 0.003102}, "is_byok": false, "prompt_tokens": 564, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 780}`

## T0027 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower cost, and much lower P95 latency.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 182, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 25}, "cost": 0.0077275, "cost_details": {"upstream_inference_completions_cost": 0.006006, "upstream_inference_cost": 0.0077275, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 495}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A remains the better fit because it outperforms Model B on every scenario-specific criterion, including dramatically lower cost and tail latency. Model B's superior general BFCL rank does not outweigh Model A's direct advantages for this high-volume, low-consequence workload.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"p95_latency_seconds":{"Model A":2.3,"Model B":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 221, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 30}, "cost": 0.0103785, "cost_details": {"upstream_inference_completions_cost": 0.007293, "upstream_inference_cost": 0.0103785, "upstream_inference_prompt_cost": 0.0030855}, "is_byok": false, "prompt_tokens": 561, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 782}`

## T0028 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency. This is especially suitable for high-volume, low-consequence automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 172, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0073975, "cost_details": {"upstream_inference_completions_cost": 0.005676, "upstream_inference_cost": 0.0073975, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 485}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A remains the better fit despite its lower BFCL overall rank because it outperforms Model B on every scenario-specific requirement: single-turn capability, relevance/irrelevance behavior, benchmark cost, and P95 latency. Its roughly 64x lower cost and substantially lower tail latency are especially important at high call volume.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 257, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 53}, "cost": 0.01166, "cost_details": {"upstream_inference_completions_cost": 0.008481, "upstream_inference_cost": 0.01166, "upstream_inference_prompt_cost": 0.003179}, "is_byok": false, "prompt_tokens": 578, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 835}`

## T0029 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is slightly stronger in single-turn capability, substantially better in relevance/irrelevance behavior, dramatically cheaper, and much faster at P95 latency. It dominates Model B on every stated criterion.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 147, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 24}, "cost": 0.0065725, "cost_details": {"upstream_inference_completions_cost": 0.004851, "upstream_inference_cost": 0.0065725, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 460}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":98,"stated_reasoning":"Model A remains the better fit for this high-volume, low-consequence workload. Despite its much lower general BFCL rank, it outperforms Model B on every stated scenario criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, far lower cost, and much lower P95 latency.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"Model A":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3},"Model B":{"single_turn_capability":85.177,"relevance_irrelevance_behavior":73.61,"benchmark_cost_usd":86.55,"measured_p95_latency_seconds":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 259, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 51}, "cost": 0.0114455, "cost_details": {"upstream_inference_completions_cost": 0.008547, "upstream_inference_cost": 0.0114455, "upstream_inference_prompt_cost": 0.0028985}, "is_byok": false, "prompt_tokens": 527, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 786}`

## T0030 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is slightly stronger in single-turn capability, substantially better in relevance/irrelevance behavior, dramatically cheaper, and much faster at P95, making it the clear fit for high-volume, low-consequence automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_P95_latency":"2.3s vs 7.56s"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 192, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 44}, "cost": 0.0080575, "cost_details": {"upstream_inference_completions_cost": 0.006336, "upstream_inference_cost": 0.0080575, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 505}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Despite Model A's much better general BFCL rank, Model B dominates every stated scenario-specific criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, far lower cost, and lower P95 latency. These factors are more directly relevant to high-volume, low-consequence automations.","cited_overall_rank":{"Model B":47,"Model A":1},"cited_scenario_specific_evidence":{"single_turn_capability":"Model B 85.361 vs Model A 85.177","relevance_irrelevance_behavior":"Model B 84.215 vs Model A 73.61","benchmark_cost":"Model B $1.35 vs Model A $86.55","measured_P95_latency":"Model B 2.3 seconds vs Model A 7.56 seconds"},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 227, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 28}, "cost": 0.010527, "cost_details": {"upstream_inference_completions_cost": 0.007491, "upstream_inference_cost": 0.010527, "upstream_inference_prompt_cost": 0.003036}, "is_byok": false, "prompt_tokens": 552, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 779}`

## T0031 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency. This strongly favors Model A for high-volume, low-consequence automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 173, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0074305, "cost_details": {"upstream_inference_completions_cost": 0.005709, "upstream_inference_cost": 0.0074305, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 486}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A remains the better fit for this specific high-volume, low-consequence workload. Despite its much worse general BFCL overall rank, it leads on every stated scenario criterion, especially relevance behavior, cost, and tail latency.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 213, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 28}, "cost": 0.0102135, "cost_details": {"upstream_inference_completions_cost": 0.007029, "upstream_inference_cost": 0.0102135, "upstream_inference_prompt_cost": 0.0031845}, "is_byok": false, "prompt_tokens": 579, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0032 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency. This strongly fits a high-volume, low-consequence automation environment.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model B":85.361,"Model A":85.177},"relevance_irrelevance_behavior":{"Model B":84.215,"Model A":73.61},"benchmark_cost_usd":{"Model B":1.35,"Model A":86.55},"measured_p95_latency_seconds":{"Model B":2.3,"Model A":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 171, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0073645, "cost_details": {"upstream_inference_completions_cost": 0.005643, "upstream_inference_cost": 0.0073645, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 484}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":98,"stated_reasoning":"Model B remains the better fit despite its lower general BFCL rank because it outperforms Model A on every scenario-specific criterion: capability, relevance behavior, cost, and tail latency. Its much lower cost and P95 latency are especially important for high-volume, low-consequence automations.","cited_overall_rank":{"Model B":47,"Model A":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model B":85.361,"Model A":85.177},"relevance_irrelevance_behavior":{"Model B":84.215,"Model A":73.61},"benchmark_cost_usd":{"Model B":1.35,"Model A":86.55},"measured_p95_latency_seconds":{"Model B":2.3,"Model A":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 225, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 27}, "cost": 0.0105985, "cost_details": {"upstream_inference_completions_cost": 0.007425, "upstream_inference_cost": 0.0105985, "upstream_inference_prompt_cost": 0.0031735}, "is_byok": false, "prompt_tokens": 577, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 802}`

## T0033 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A is superior on every stated criterion: slightly higher single-turn capability, substantially better relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency. This strongly fits high-volume, low-consequence automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 170, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0073315, "cost_details": {"upstream_inference_completions_cost": 0.00561, "upstream_inference_cost": 0.0073315, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 483}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":98,"stated_reasoning":"Keep Model A because it outperforms Model B on every scenario-specific requirement, especially cost, latency, and relevance behavior. Model B's higher general BFCL rank does not outweigh these directly applicable measurements for high-volume, low-consequence automation.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost_usd":{"Model A":1.35,"Model B":86.55},"measured_p95_latency_seconds":{"Model A":2.3,"Model B":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 224, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 35}, "cost": 0.01056, "cost_details": {"upstream_inference_completions_cost": 0.007392, "upstream_inference_cost": 0.01056, "upstream_inference_prompt_cost": 0.003168}, "is_byok": false, "prompt_tokens": 576, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 800}`

## T0034 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":{"Model B":85.361,"Model A":85.177},"relevance_irrelevance_behavior":{"Model B":84.215,"Model A":73.61},"benchmark_cost_usd":{"Model B":1.35,"Model A":86.55},"measured_p95_latency_seconds":{"Model B":2.3,"Model A":7.56}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 158, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0069355, "cost_details": {"upstream_inference_completions_cost": 0.005214, "upstream_inference_cost": 0.0069355, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 471}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":98,"stated_reasoning":"Keep Model B because it outperforms Model A on every scenario-specific requirement, especially cost, latency, and relevance behavior. Model A's superior general BFCL rank does not outweigh the directly measured criteria for this high-volume, low-consequence workload.","cited_overall_rank":{"Model B":47,"Model A":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model B":85.361,"Model A":85.177},"relevance_irrelevance_behavior":{"Model B":84.215,"Model A":73.61},"benchmark_cost_usd":{"Model B":1.35,"Model A":86.55},"measured_p95_latency_seconds":{"Model B":2.3,"Model A":7.56}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 233, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 43}, "cost": 0.010791, "cost_details": {"upstream_inference_completions_cost": 0.007689, "upstream_inference_cost": 0.010791, "upstream_inference_prompt_cost": 0.003102}, "is_byok": false, "prompt_tokens": 564, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 797}`

## T0035 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":99,"stated_reasoning":"Model B is better on every stated criterion: slightly higher single-turn capability, substantially stronger relevance behavior, dramatically lower cost, and much lower P95 latency.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 196, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 81}, "cost": 0.0081895, "cost_details": {"upstream_inference_completions_cost": 0.006468, "upstream_inference_cost": 0.0081895, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 509}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":98,"stated_reasoning":"Model B remains the stronger fit for this high-volume, low-consequence workflow. It slightly exceeds Model A in single-turn capability, substantially outperforms it on relevance/irrelevance behavior, costs far less, and has much lower P95 latency. Model A's superior general BFCL rank does not outweigh Model B's advantages on every scenario-specific criterion.","cited_overall_rank":{"Model A":1,"Model B":47},"cited_scenario_specific_evidence":{"Model A":{"single_turn_capability":85.177,"relevance_irrelevance_behavior":73.61,"benchmark_cost_usd":86.55,"measured_p95_latency_seconds":7.56},"Model B":{"single_turn_capability":85.361,"relevance_irrelevance_behavior":84.215,"benchmark_cost_usd":1.35,"measured_p95_latency_seconds":2.3}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 242, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 25}, "cost": 0.0108405, "cost_details": {"upstream_inference_completions_cost": 0.007986, "upstream_inference_cost": 0.0108405, "upstream_inference_prompt_cost": 0.0028545}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 761}`

## T0036 - OpenAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":99,"stated_reasoning":"Model A slightly leads in single-turn capability, substantially outperforms on relevance/irrelevance behavior, and is dramatically cheaper and faster at P95 latency, making it the clear fit for high-volume, low-consequence automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"single_turn_capability":"85.361 vs 85.177","relevance_irrelevance_behavior":"84.215 vs 73.61","benchmark_cost":"$1.35 vs $86.55","measured_p95_latency":"2.3 seconds vs 7.56 seconds"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 194, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 44}, "cost": 0.0081235, "cost_details": {"upstream_inference_completions_cost": 0.006402, "upstream_inference_cost": 0.0081235, "upstream_inference_prompt_cost": 0.0017215}, "is_byok": false, "prompt_tokens": 313, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 507}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":98,"stated_reasoning":"Keep Model A. Although Model B has the top general BFCL rank, Model A is better on every scenario-specific criterion: slightly stronger single-turn capability, substantially better relevance/irrelevance behavior, far lower cost, and much lower P95 latency.","cited_overall_rank":{"Model A":47,"Model B":1},"cited_scenario_specific_evidence":{"single_turn_capability":{"Model A":85.361,"Model B":85.177},"relevance_irrelevance_behavior":{"Model A":84.215,"Model B":73.61},"benchmark_cost":{"Model A":"$1.35","Model B":"$86.55"},"measured_p95_latency":{"Model A":"2.3 seconds","Model B":"7.56 seconds"}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 231, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 39}, "cost": 0.01067, "cost_details": {"upstream_inference_completions_cost": 0.007623, "upstream_inference_cost": 0.01067, "upstream_inference_prompt_cost": 0.003047}, "is_byok": false, "prompt_tokens": 554, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 785}`

## T0037 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":84,"stated_reasoning":"Model A is the stronger fit for multi-step agent workflows because it leads on multi-turn completion, memory, web search, and P95 latency. Model B has better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary in this environment.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"multi_turn_performance":"68.38 vs 68","memory_performance":"73.76 vs 55.7","web_search_performance":"84.5 vs 77.5","p95_latency_seconds":"7.56 vs 13.50","tradeoffs":"Model A trails in relevance/irrelevance behavior (73.61 vs 79.98) and costs more ($86.55 vs $4.64)."}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 253, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 66}, "cost": 0.0103345, "cost_details": {"upstream_inference_completions_cost": 0.008349, "upstream_inference_cost": 0.0103345, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 614}`

## T0038 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":96,"stated_reasoning":"Model B is stronger on the core agent-workflow criteria: multi-turn completion, memory, web search, and latency. Model A leads only in relevance/irrelevance behavior and cost, which is explicitly secondary.","cited_overall_rank":["Model B","Model A"],"cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68.00","memory":"73.76 vs 55.70","web_search":"84.5 vs 77.5","p95_latency_seconds":"7.56 vs 13.50","tradeoffs":"Model A has better relevance/irrelevance behavior (79.98 vs 73.61) and much lower cost ($4.64 vs $86.55)."}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 239, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 63}, "cost": 0.0098725, "cost_details": {"upstream_inference_completions_cost": 0.007887, "upstream_inference_cost": 0.0098725, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 600}`

## T0039 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":94,"stated_reasoning":"Model A is stronger on the core agent-workflow criteria and has substantially lower P95 latency. Model B is cheaper and better on relevance/irrelevance behavior, but cost is secondary and its memory, web-search, and latency results are materially worse.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68.00","memory":"73.76 vs 55.70","web_search":"84.50 vs 77.50","relevance_irrelevance":"73.61 vs 79.98","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost":"$86.55 vs $4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 219, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 45}, "cost": 0.0092125, "cost_details": {"upstream_inference_completions_cost": 0.007227, "upstream_inference_cost": 0.0092125, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 580}`

## T0040 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"Model B is stronger on most primary requirements: multi-turn completion, memory, web search, and latency. Model A leads in relevance behavior and cost, but cost is secondary and its latency is substantially worse.","cited_overall_rank":"1st: Model B; 2nd: Model A","cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68.00","memory":"73.76 vs 55.70","web_search":"84.50 vs 77.50","relevance_irrelevance":"73.61 vs 79.98","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost":"$86.55 vs $4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 236, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 64}, "cost": 0.0097735, "cost_details": {"upstream_inference_completions_cost": 0.007788, "upstream_inference_cost": 0.0097735, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 597}`

## T0041 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":89,"stated_reasoning":"Model A is stronger on the most important capabilities for multi-step agent workflows: memory, web search, and latency, while also having a slight multi-turn advantage. Model B has better relevance/irrelevance behavior and substantially lower cost, but cost is secondary and does not outweigh Model A's workflow-critical strengths.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"multi_turn_performance":"68.38 vs 68","memory_performance":"73.76 vs 55.7","web_search_performance":"84.5 vs 77.5","p95_latency_seconds":"7.56 vs 13.50","tradeoffs":"Model A has lower relevance/irrelevance performance (73.61 vs 79.98) and much higher benchmark cost ($86.55 vs $4.64)."}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 277, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 78}, "cost": 0.0111265, "cost_details": {"upstream_inference_completions_cost": 0.009141, "upstream_inference_cost": 0.0111265, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 638}`

## T0042 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":86,"stated_reasoning":"Model B is better suited to multi-step agent workflows because it leads in multi-turn completion, memory, web search, and latency. Although Model A has better relevance/irrelevance behavior and dramatically lower cost, cost is secondary in this environment.","cited_overall_rank":"No overall rank was provided.","cited_scenario_specific_evidence":"Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 227, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 52}, "cost": 0.0094765, "cost_details": {"upstream_inference_completions_cost": 0.007491, "upstream_inference_cost": 0.0094765, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 588}`

## T0043 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":87,"stated_reasoning":"Model B is better suited to multi-step agent workflows because it has substantially stronger memory and web-search performance, slightly better multi-turn completion, and much lower P95 latency. Model A leads on relevance/irrelevance behavior and cost, but cost is secondary in this environment.","cited_overall_rank":"Not provided","cited_scenario_specific_evidence":"Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 246, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 68}, "cost": 0.0101035, "cost_details": {"upstream_inference_completions_cost": 0.008118, "upstream_inference_cost": 0.0101035, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 607}`

## T0044 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":91,"stated_reasoning":"Model A is better suited to multi-step agent workflows because it leads on multi-turn completion, memory, web search, and P95 latency. These primary advantages outweigh Model B's stronger relevance/irrelevance score and much lower cost, since cost is secondary.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"multi_turn_performance":68.38,"memory_performance":73.76,"web_search_performance":84.5,"relevance_irrelevance_behavior":73.61,"p95_latency_seconds":7.56,"benchmark_cost_dollars":86.55,"key_tradeoff":"Model A substantially improves memory, web search, and latency; Model B is better on relevance/irrelevance behavior (79.98) and cost ($4.64)."}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 254, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 62}, "cost": 0.0103675, "cost_details": {"upstream_inference_completions_cost": 0.008382, "upstream_inference_cost": 0.0103675, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 615}`

## T0045 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"Model A is stronger on most primary criteria, especially memory, web search, and latency, while multi-turn performance is slightly better. Model B leads on relevance behavior and cost, but cost is secondary.","cited_overall_rank":"1st of 2 for this work environment","cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68.00","memory":"73.76 vs 55.70","web_search":"84.50 vs 77.50","p95_latency_seconds":"7.56 vs 13.50","relevance_irrelevance":"73.61 vs 79.98","benchmark_cost":"$86.55 vs $4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 263, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 95}, "cost": 0.0106645, "cost_details": {"upstream_inference_completions_cost": 0.008679, "upstream_inference_cost": 0.0106645, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 624}`

## T0046 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":91,"stated_reasoning":"Model B is stronger on most primary criteria: multi-turn completion, memory, web search, and latency. Model A leads relevance/irrelevance behavior and is far cheaper, but cost is secondary and does not outweigh Model B's broader workflow advantages.","cited_overall_rank":"No overall rank was provided; Model B leads 4 of the 6 reported metrics.","cited_scenario_specific_evidence":"Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 289, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 101}, "cost": 0.0115225, "cost_details": {"upstream_inference_completions_cost": 0.009537, "upstream_inference_cost": 0.0115225, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 650}`

## T0047 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":91,"stated_reasoning":"Model A is better suited to multi-step agent workflows because it leads on memory, web search, latency, and slightly on multi-turn completion. Model B has stronger relevance/irrelevance behavior and much lower cost, but cost is secondary and A wins more of the primary criteria.","cited_overall_rank":"1st of 2","cited_scenario_specific_evidence":{"multi_turn_performance":68.38,"memory_performance":73.76,"web_search_performance":84.5,"relevance_irrelevance_behavior":73.61,"p95_latency_seconds":7.56,"benchmark_cost_usd":86.55}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 210, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 53}, "cost": 0.0089155, "cost_details": {"upstream_inference_completions_cost": 0.00693, "upstream_inference_cost": 0.0089155, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 571}`

## T0048 - OpenAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"Model B is stronger on the primary agent-workflow requirements: multi-turn completion, memory, web search, and latency. Model A leads in relevance behavior and is far cheaper, but cost is secondary in this environment.","cited_overall_rank":"1st of 2 for the stated work environment","cited_scenario_specific_evidence":{"multi_turn_performance":"68.38 vs 68","memory_performance":"73.76 vs 55.7","web_search_performance":"84.5 vs 77.5","p95_latency_seconds":"7.56 vs 13.50","tradeoffs":"Model B has lower relevance/irrelevance behavior (73.61 vs 79.98) and much higher cost ($86.55 vs $4.64)."}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 266, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 81}, "cost": 0.0107635, "cost_details": {"upstream_inference_completions_cost": 0.008778, "upstream_inference_cost": 0.0107635, "upstream_inference_prompt_cost": 0.0019855}, "is_byok": false, "prompt_tokens": 361, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 627}`

## T0049 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":88,"stated_reasoning":"Model A is stronger for multi-step enterprise agents because it leads substantially in memory and web search, slightly in multi-turn completion, and has much lower P95 latency. Model B is far cheaper and better on relevance/irrelevance behavior, but cost is secondary and does not outweigh Model A's workflow-critical advantages.","cited_overall_rank":"Model A: BFCL overall rank 1; Model B: rank 4","cited_scenario_specific_evidence":"Model A vs Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A trails on relevance/irrelevance behavior (73.61 vs 79.98) and cost ($86.55 vs $4.64)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 255, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 49}, "cost": 0.0105545, "cost_details": {"upstream_inference_completions_cost": 0.008415, "upstream_inference_cost": 0.0105545, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 644}`

## T0050 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"Model B is better suited to multi-step enterprise agent workflows because it leads in overall BFCL rank and delivers substantially stronger memory and web-search performance, slightly better multi-turn completion, and much lower P95 latency. Model A has better relevance/irrelevance behavior and far lower cost, but cost is secondary and does not outweigh Model B's workflow-critical advantages.","cited_overall_rank":"Model B: BFCL overall rank 1; Model A: BFCL overall rank 4","cited_scenario_specific_evidence":"Model B vs Model A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 249, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 34}, "cost": 0.0103565, "cost_details": {"upstream_inference_completions_cost": 0.008217, "upstream_inference_cost": 0.0103565, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 638}`

## T0051 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":91,"stated_reasoning":"Model A is better suited to multi-step enterprise agent workflows because it leads on memory, web search, latency, and slightly on multi-turn completion. Model B has stronger relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and Model A has the stronger overall capability profile.","cited_overall_rank":{"Model A":1,"Model B":4},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68.38,"Model B":68},"memory":{"Model A":73.76,"Model B":55.7},"web_search":{"Model A":84.5,"Model B":77.5},"relevance_irrelevance":{"Model A":73.61,"Model B":79.98},"p95_latency_seconds":{"Model A":7.56,"Model B":13.5},"benchmark_cost":{"Model A":86.55,"Model B":4.64}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 261, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 48}, "cost": 0.0107525, "cost_details": {"upstream_inference_completions_cost": 0.008613, "upstream_inference_cost": 0.0107525, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 650}`

## T0052 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":94,"stated_reasoning":"Model B is the stronger choice for multi-step enterprise agent workflows because it leads on overall BFCL rank and performs better in multi-turn completion, memory, web search, and P95 latency. Model A has better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and does not outweigh Model B's advantages in the core workflow criteria.","cited_overall_rank":{"Model A":4,"Model B":1},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68,"Model B":68.38},"memory":{"Model A":55.7,"Model B":73.76},"web_search":{"Model A":77.5,"Model B":84.5},"relevance_irrelevance":{"Model A":79.98,"Model B":73.61},"p95_latency_seconds":{"Model A":13.5,"Model B":7.56},"benchmark_cost_usd":{"Model A":4.64,"Model B":86.55}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 281, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 53}, "cost": 0.0114125, "cost_details": {"upstream_inference_completions_cost": 0.009273, "upstream_inference_cost": 0.0114125, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 670}`

## T0053 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"Model A is the stronger choice for multi-step enterprise agent workflows because it leads in multi-turn completion, memory, web search, and latency, while also holding the best BFCL overall rank. Model B is substantially cheaper and has better relevance/irrelevance behavior, but cost is secondary and its weaker memory and search performance are more consequential for this environment.","cited_overall_rank":{"Model A":1,"Model B":4},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68.38,"Model B":68},"memory":{"Model A":73.76,"Model B":55.7},"web_search":{"Model A":84.5,"Model B":77.5},"relevance_irrelevance":{"Model A":73.61,"Model B":79.98},"p95_latency_seconds":{"Model A":7.56,"Model B":13.5},"benchmark_cost":{"Model A":86.55,"Model B":4.64}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 257, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 31}, "cost": 0.0106205, "cost_details": {"upstream_inference_completions_cost": 0.008481, "upstream_inference_cost": 0.0106205, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 646}`

## T0054 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":94,"stated_reasoning":"Model A is the stronger choice for multi-step enterprise agent workflows. It leads in multi-turn completion, memory, web search, and latency, while also holding the best overall BFCL rank. Model B is substantially cheaper and has better relevance/irrelevance behavior, but cost is secondary and those advantages do not outweigh Model A's stronger workflow-critical performance.","cited_overall_rank":"Model A: BFCL overall rank 1; Model B: BFCL overall rank 4","cited_scenario_specific_evidence":"Model A vs Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model B leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 254, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 40}, "cost": 0.0105215, "cost_details": {"upstream_inference_completions_cost": 0.008382, "upstream_inference_cost": 0.0105215, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 643}`

## T0055 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":88,"stated_reasoning":"Model B is better suited to multi-step agent workflows because it has substantially stronger memory and web-search performance, slightly better multi-turn completion, and much lower P95 latency. Model A leads in relevance/irrelevance behavior and is far cheaper, but cost is secondary and Model B's broader workflow advantages outweigh those drawbacks.","cited_overall_rank":"Model B: BFCL overall rank 1; Model A: rank 4","cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68","memory":"73.76 vs 55.7","web_search":"84.5 vs 77.5","relevance_irrelevance":"73.61 vs 79.98","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost_usd":"86.55 vs 4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 248, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 51}, "cost": 0.0103235, "cost_details": {"upstream_inference_completions_cost": 0.008184, "upstream_inference_cost": 0.0103235, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 637}`

## T0056 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":91,"stated_reasoning":"Model A is the stronger choice for multi-step enterprise agent workflows because it leads in multi-turn completion, memory, web search, and latency—four core requirements. Model B has better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and does not outweigh Model A's broader performance advantages.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":{"multi_turn_performance":"68.38 vs 68 for Model B","memory_performance":"73.76 vs 55.7","web_search_performance":"84.5 vs 77.5","relevance_irrelevance_behavior":"73.61 vs 79.98 for Model B","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost":"$86.55 vs $4.64 for Model B"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 242, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 39}, "cost": 0.0101255, "cost_details": {"upstream_inference_completions_cost": 0.007986, "upstream_inference_cost": 0.0101255, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 631}`

## T0057 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"Model B is stronger for multi-step agent workflows because it leads substantially in memory and web-search performance, has slightly better multi-turn completion, and delivers much lower P95 latency. Model A has better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and B's advantages align more closely with the primary requirements.","cited_overall_rank":{"Model A":4,"Model B":1},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68,"Model B":68.38},"memory":{"Model A":55.7,"Model B":73.76},"web_search":{"Model A":77.5,"Model B":84.5},"relevance_irrelevance":{"Model A":79.98,"Model B":73.61},"p95_latency_seconds":{"Model A":13.5,"Model B":7.56},"benchmark_cost":{"Model A":4.64,"Model B":86.55}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 263, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 42}, "cost": 0.0108185, "cost_details": {"upstream_inference_completions_cost": 0.008679, "upstream_inference_cost": 0.0108185, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 652}`

## T0058 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"Model B is the stronger choice for multi-step enterprise agent workflows because it leads in overall BFCL rank and in most primary criteria: multi-turn completion, memory, web search, and latency. Model A has better relevance/irrelevance behavior and substantially lower cost, but cost is secondary and its advantages do not outweigh Model B's stronger workflow capabilities.","cited_overall_rank":"Model B: BFCL overall rank 1; Model A: rank 4","cited_scenario_specific_evidence":"Model B: multi-turn 68.38 vs 68, memory 73.76 vs 55.7, web search 84.5 vs 77.5, and P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 260, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 52}, "cost": 0.0107195, "cost_details": {"upstream_inference_completions_cost": 0.00858, "upstream_inference_cost": 0.0107195, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 649}`

## T0059 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"Model A is better suited to multi-step enterprise agent workflows because it has substantially stronger memory and web-search performance, slightly better multi-turn completion, and lower P95 latency. Model B has better relevance/irrelevance behavior and much lower cost, but cost is secondary and Model A leads on most primary criteria.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":{"multi_turn_performance":"68.38 vs 68 for Model B","memory_performance":"73.76 vs 55.7","web_search_performance":"84.5 vs 77.5","relevance_irrelevance_behavior":"73.61 vs 79.98 for Model B","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost":"$86.55 vs $4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 236, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 36}, "cost": 0.0099275, "cost_details": {"upstream_inference_completions_cost": 0.007788, "upstream_inference_cost": 0.0099275, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 625}`

## T0060 - OpenAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":94,"stated_reasoning":"Model B is the stronger choice for multi-step enterprise agent workflows because it leads in multi-turn completion, memory, web search, and latency, while also holding the best overall BFCL rank. Model A is substantially cheaper and has better relevance/irrelevance behavior, but cost is secondary and its weaker memory, search, and latency are more consequential here.","cited_overall_rank":{"Model B":1,"Model A":4},"cited_scenario_specific_evidence":{"multi_turn":{"Model B":68.38,"Model A":68},"memory":{"Model B":73.76,"Model A":55.7},"web_search":{"Model B":84.5,"Model A":77.5},"relevance_irrelevance":{"Model B":73.61,"Model A":79.98},"p95_latency_seconds":{"Model B":7.56,"Model A":13.5},"benchmark_cost_usd":{"Model B":86.55,"Model A":4.64}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 276, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 48}, "cost": 0.0112475, "cost_details": {"upstream_inference_completions_cost": 0.009108, "upstream_inference_cost": 0.0112475, "upstream_inference_prompt_cost": 0.0021395}, "is_byok": false, "prompt_tokens": 389, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 665}`

## T0061 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":88,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and P95 latency, which are central to multi-step agent workflows. Model A has better relevance/irrelevance behavior and far lower cost, but cost is secondary and B's substantial memory and latency advantages favor it overall.","cited_overall_rank":"Not provided or used.","cited_scenario_specific_evidence":{"multi_turn":{"Model A":68,"Model B":68.38},"memory":{"Model A":55.7,"Model B":73.76},"web_search":{"Model A":77.5,"Model B":84.5},"relevance_irrelevance":{"Model A":79.98,"Model B":73.61},"p95_latency_seconds":{"Model A":13.5,"Model B":7.56},"benchmark_cost_usd":{"Model A":4.64,"Model B":86.55}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 268, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 56}, "cost": 0.0108405, "cost_details": {"upstream_inference_completions_cost": 0.008844, "upstream_inference_cost": 0.0108405, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 631}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":94,"stated_reasoning":"Model B remains the stronger choice for multi-step agent workflows. It has the better BFCL overall rank and leads on memory, web search, latency, and slightly on multi-turn performance. Model A offers better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and these advantages do not outweigh Model B's workflow-critical strengths.","cited_overall_rank":{"Model A":4,"Model B":1},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68,"Model B":68.38},"memory":{"Model A":55.7,"Model B":73.76},"web_search":{"Model A":77.5,"Model B":84.5},"relevance_irrelevance":{"Model A":79.98,"Model B":73.61},"p95_latency_seconds":{"Model A":13.5,"Model B":7.56},"benchmark_cost_usd":{"Model A":4.64,"Model B":86.55}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 232, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.011319, "cost_details": {"upstream_inference_completions_cost": 0.007656, "upstream_inference_cost": 0.011319, "upstream_inference_prompt_cost": 0.003663}, "is_byok": false, "prompt_tokens": 666, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 898}`

## T0062 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":84,"stated_reasoning":"Model A is preferable for multi-step enterprise agent workflows because it has substantially better memory and web-search performance, slightly better multi-turn performance, and much lower P95 latency. Model B leads in relevance/irrelevance behavior and cost, but cost is secondary and A's advantages better match the core workflow requirements.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68.38,"Model B":68},"memory":{"Model A":73.76,"Model B":55.7},"web_search":{"Model A":84.5,"Model B":77.5},"relevance_irrelevance":{"Model A":73.61,"Model B":79.98},"p95_latency_seconds":{"Model A":7.56,"Model B":13.5},"benchmark_cost":{"Model A":86.55,"Model B":4.64}}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 265, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 55}, "cost": 0.0107415, "cost_details": {"upstream_inference_completions_cost": 0.008745, "upstream_inference_cost": 0.0107415, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 628}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":94,"stated_reasoning":"Model A's BFCL overall rank of 1 reinforces the provisional choice. It also has substantially stronger memory and web-search performance, slightly better multi-turn performance, and much lower P95 latency. Model B offers better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and its overall rank is lower.","cited_overall_rank":{"Model A":1,"Model B":4},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68.38,"Model B":68},"memory":{"Model A":73.76,"Model B":55.7},"web_search":{"Model A":84.5,"Model B":77.5},"relevance_irrelevance":{"Model A":73.61,"Model B":79.98},"p95_latency_seconds":{"Model A":7.56,"Model B":13.5},"benchmark_cost":{"Model A":86.55,"Model B":4.64}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 226, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01111, "cost_details": {"upstream_inference_completions_cost": 0.007458, "upstream_inference_cost": 0.01111, "upstream_inference_prompt_cost": 0.003652}, "is_byok": false, "prompt_tokens": 664, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 890}`

## T0063 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":88,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and P95 latency—the primary requirements for agent workflows. Model A has better relevance behavior and much lower cost, but cost is secondary and its memory and latency are substantially worse.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. A leads relevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 235, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 65}, "cost": 0.0097515, "cost_details": {"upstream_inference_completions_cost": 0.007755, "upstream_inference_cost": 0.0097515, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 598}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B remains the stronger choice for multi-step agent workflows. It leads in multi-turn performance, memory, web search, and latency, while its BFCL overall rank of 1 reinforces the selection. Model A offers better relevance behavior and far lower cost, but cost is secondary and does not outweigh Model B's advantages on the primary criteria.","cited_overall_rank":"Model B: 1; Model A: 4","cited_scenario_specific_evidence":"Model B vs Model A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance behavior 79.98 vs 73.61 and cost $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 210, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.010362, "cost_details": {"upstream_inference_completions_cost": 0.00693, "upstream_inference_cost": 0.010362, "upstream_inference_prompt_cost": 0.003432}, "is_byok": false, "prompt_tokens": 624, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 834}`

## T0064 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":91,"stated_reasoning":"Model A is stronger on multi-turn completion, memory, web search, and latency—the primary requirements for multi-step agent workflows. Model B leads on relevance/irrelevance behavior and cost, but cost is secondary and its latency and memory are substantially worse.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. B leads relevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 215, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 44}, "cost": 0.0090915, "cost_details": {"upstream_inference_completions_cost": 0.007095, "upstream_inference_cost": 0.0090915, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 578}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A remains the stronger choice because it leads on most primary workflow requirements, particularly memory, web search, and latency, while nearly matching Model B on multi-turn performance. Its BFCL overall rank of 1 versus Model B's rank of 4 further supports the selection. Model B's advantages in relevance behavior and cost do not outweigh Model A's broader performance lead, especially because cost is secondary.","cited_overall_rank":"Model A: 1; Model B: 4","cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model B leads relevance/irrelevance behavior at 79.98 vs 73.61 and benchmark cost at $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 223, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0107965, "cost_details": {"upstream_inference_completions_cost": 0.007359, "upstream_inference_cost": 0.0107965, "upstream_inference_prompt_cost": 0.0034375}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 848}`

## T0065 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":84,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and P95 latency, which are central to multi-step agent workflows. Model A has better relevance/irrelevance behavior and much lower cost, but cost is secondary and B's capability advantages are more consequential.","cited_overall_rank":"Not provided or used","cited_scenario_specific_evidence":{"multi_turn":"B 68.38 vs A 68","memory":"B 73.76 vs A 55.7","web_search":"B 84.5 vs A 77.5","relevance_irrelevance":"A 79.98 vs B 73.61","p95_latency_seconds":"B 7.56 vs A 13.50","benchmark_cost":"A $4.64 vs B $86.55"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 266, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 72}, "cost": 0.0107745, "cost_details": {"upstream_inference_completions_cost": 0.008778, "upstream_inference_cost": 0.0107745, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 629}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"Model B remains the stronger choice for multi-step agent workflows. It leads in multi-turn performance, memory, web search, and latency, while its BFCL overall rank of 1 versus Model A's rank of 4 reinforces the selection. Model A offers better relevance/irrelevance behavior and substantially lower cost, but cost is secondary and does not outweigh Model B's workflow-critical advantages.","cited_overall_rank":{"Model A":4,"Model B":1},"cited_scenario_specific_evidence":{"multi_turn":"Model B 68.38 vs Model A 68","memory":"Model B 73.76 vs Model A 55.7","web_search":"Model B 84.5 vs Model A 77.5","relevance_irrelevance":"Model A 79.98 vs Model B 73.61","p95_latency_seconds":"Model B 7.56 vs Model A 13.50","benchmark_cost":"Model A $4.64 vs Model B $86.55"},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 239, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.011451, "cost_details": {"upstream_inference_completions_cost": 0.007887, "upstream_inference_cost": 0.011451, "upstream_inference_prompt_cost": 0.003564}, "is_byok": false, "prompt_tokens": 648, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 887}`

## T0066 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":84,"stated_reasoning":"Model A is preferable for multi-step enterprise agents because it has substantially stronger memory and web-search performance, slightly better multi-turn completion, and much lower P95 latency. Model B has better relevance/irrelevance behavior and far lower cost, but cost is secondary.","cited_overall_rank":"None; selection made before seeing any general BFCL overall rank.","cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. B leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 246, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 59}, "cost": 0.0101145, "cost_details": {"upstream_inference_completions_cost": 0.008118, "upstream_inference_cost": 0.0101145, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 609}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"Model A remains the stronger choice for multi-step enterprise agent workflows. Its rank-1 BFCL result reinforces its advantages in memory, web search, and latency, while its multi-turn score is slightly higher. Model B offers better relevance/irrelevance behavior and dramatically lower cost, but cost is secondary and does not outweigh Model A's workflow-critical strengths.","cited_overall_rank":"Model A: rank 1; Model B: rank 4.","cited_scenario_specific_evidence":"Model A vs Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model B leads in relevance/irrelevance behavior, 79.98 vs 73.61, and benchmark cost, $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 219, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0107525, "cost_details": {"upstream_inference_completions_cost": 0.007227, "upstream_inference_cost": 0.0107525, "upstream_inference_prompt_cost": 0.0035255}, "is_byok": false, "prompt_tokens": 641, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 860}`

## T0067 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":87,"stated_reasoning":"Model A is stronger on memory and web search, slightly better on multi-turn completion, and substantially faster at P95 latency. Model B has better relevance/irrelevance behavior and much lower cost, but cost is secondary, so Model A better fits the stated priorities.","cited_overall_rank":"Not provided or considered.","cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; relevance/irrelevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; cost $86.55 vs $4.64."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 233, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 55}, "cost": 0.0096855, "cost_details": {"upstream_inference_completions_cost": 0.007689, "upstream_inference_cost": 0.0096855, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 596}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":94,"stated_reasoning":"Model A remains the stronger choice for multi-step agent workflows. It leads substantially in memory and web-search performance, is marginally better in multi-turn completion, and has much lower P95 latency. Model B offers better relevance/irrelevance behavior and far lower cost, but cost is secondary. Model A's BFCL overall rank of 1 versus Model B's rank of 4 reinforces the selection.","cited_overall_rank":"Model A: 1; Model B: 4","cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; relevance/irrelevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; benchmark cost $86.55 vs $4.64.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 217, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.010637, "cost_details": {"upstream_inference_completions_cost": 0.007161, "upstream_inference_cost": 0.010637, "upstream_inference_prompt_cost": 0.003476}, "is_byok": false, "prompt_tokens": 632, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 849}`

## T0068 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":89,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and P95 latency—four primary criteria for agent workflows. Model A leads in relevance/irrelevance behavior and is substantially cheaper, but cost is secondary.","cited_overall_rank":"Not provided or considered.","cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. A leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 226, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 54}, "cost": 0.0094545, "cost_details": {"upstream_inference_completions_cost": 0.007458, "upstream_inference_cost": 0.0094545, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 589}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":96,"stated_reasoning":"Model B remains the stronger choice because it leads on multi-turn completion, memory, web search, and latency, while also holding the better BFCL overall rank. Model A leads on relevance/irrelevance behavior and is much cheaper, but cost is secondary and its memory and latency are substantially worse.","cited_overall_rank":"Model B ranks 1st overall; Model A ranks 4th overall.","cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance behavior, 79.98 vs 73.61, and benchmark cost, $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 207, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.010274, "cost_details": {"upstream_inference_completions_cost": 0.006831, "upstream_inference_cost": 0.010274, "upstream_inference_prompt_cost": 0.003443}, "is_byok": false, "prompt_tokens": 626, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 833}`

## T0069 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":88,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and P95 latency—four primary criteria for agent workflows. Model A leads on relevance/irrelevance behavior and is far cheaper, but cost is secondary.","cited_overall_rank":"Not provided or considered","cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. A leads relevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 228, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 59}, "cost": 0.0095205, "cost_details": {"upstream_inference_completions_cost": 0.007524, "upstream_inference_cost": 0.0095205, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 591}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":94,"stated_reasoning":"Model B remains the stronger choice because it leads on multi-turn performance, memory, web search, and latency, while also holding the better BFCL overall rank. Model A offers better relevance behavior and much lower cost, but cost is secondary and its other deficits are more consequential for multi-step agent workflows.","cited_overall_rank":"Model B ranks 1st; Model A ranks 4th.","cited_scenario_specific_evidence":"Model B vs Model A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model A leads relevance/irrelevance behavior 79.98 vs 73.61 and cost $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 204, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0101585, "cost_details": {"upstream_inference_completions_cost": 0.006732, "upstream_inference_cost": 0.0101585, "upstream_inference_prompt_cost": 0.0034265}, "is_byok": false, "prompt_tokens": 623, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 827}`

## T0070 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":88,"stated_reasoning":"Model B is stronger on multi-turn completion, memory, web search, and latency—core requirements for agent workflows. Model A leads only in relevance/irrelevance behavior and cost, which is secondary.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. A leads relevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 198, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 37}, "cost": 0.0085305, "cost_details": {"upstream_inference_completions_cost": 0.006534, "upstream_inference_cost": 0.0085305, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 561}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B remains the stronger choice because it leads on most primary workflow criteria and has substantially lower latency. Its BFCL overall rank of 1 versus Model A's rank of 4 reinforces the scenario-specific evidence. Model A's advantages in relevance behavior and cost do not outweigh Model B's stronger memory and web-search performance, especially since cost is secondary.","cited_overall_rank":"Model B: 1; Model A: 4","cited_scenario_specific_evidence":"B vs A: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; relevance/irrelevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; cost $86.55 vs $4.64.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 206, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0101805, "cost_details": {"upstream_inference_completions_cost": 0.006798, "upstream_inference_cost": 0.0101805, "upstream_inference_prompt_cost": 0.0033825}, "is_byok": false, "prompt_tokens": 615, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 821}`

## T0071 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":84,"stated_reasoning":"Model A is stronger on multi-turn completion, memory, web search, and P95 latency—the capabilities most central to multi-step agent workflows. Model B has better relevance/irrelevance behavior and much lower cost, but cost is secondary and does not outweigh Model A’s operational advantages.","cited_overall_rank":null,"cited_scenario_specific_evidence":{"multi_turn":"68.38 vs 68","memory":"73.76 vs 55.7","web_search":"84.5 vs 77.5","relevance_irrelevance":"73.61 vs 79.98","p95_latency_seconds":"7.56 vs 13.50","benchmark_cost_usd":"86.55 vs 4.64"}}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 267, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 91}, "cost": 0.0108075, "cost_details": {"upstream_inference_completions_cost": 0.008811, "upstream_inference_cost": 0.0108075, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 630}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"Model A's BFCL overall rank of 1 reinforces the provisional choice. It also leads on the scenario-critical measures of multi-turn performance, memory, web search, and P95 latency. Model B offers better relevance/irrelevance behavior and substantially lower cost, but cost is secondary and these advantages do not outweigh Model A's broader workflow strengths.","cited_overall_rank":{"Model A":1,"Model B":4},"cited_scenario_specific_evidence":{"multi_turn":{"Model A":68.38,"Model B":68},"memory":{"Model A":73.76,"Model B":55.7},"web_search":{"Model A":84.5,"Model B":77.5},"relevance_irrelevance":{"Model A":73.61,"Model B":79.98},"p95_latency_seconds":{"Model A":7.56,"Model B":13.5},"benchmark_cost_usd":{"Model A":86.55,"Model B":4.64}},"changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 233, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.011154, "cost_details": {"upstream_inference_completions_cost": 0.007689, "upstream_inference_cost": 0.011154, "upstream_inference_prompt_cost": 0.003465}, "is_byok": false, "prompt_tokens": 630, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 863}`

## T0072 - OpenAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":87,"stated_reasoning":"Model A is stronger on most primary criteria: multi-turn completion, memory, web search, and P95 latency. Its substantially higher cost and weaker relevance/irrelevance score are drawbacks, but cost is secondary and the overall profile better fits complex agent workflows.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A vs B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. B leads relevance/irrelevance 79.98 vs 73.61 and cost $4.64 vs $86.55."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 240, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 65}, "cost": 0.0099165, "cost_details": {"upstream_inference_completions_cost": 0.00792, "upstream_inference_cost": 0.0099165, "upstream_inference_prompt_cost": 0.0019965}, "is_byok": false, "prompt_tokens": 363, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 603}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":94,"stated_reasoning":"Model A remains the better choice because it leads on memory, web search, latency, and slightly on multi-turn performance. Its BFCL overall rank of 1 versus Model B's rank of 4 further supports the selection. Model B is much cheaper and has better relevance/irrelevance behavior, but cost is secondary and its overall workflow profile is weaker.","cited_overall_rank":"Model A: 1; Model B: 4","cited_scenario_specific_evidence":"Model A vs Model B: multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s. Model B leads relevance/irrelevance behavior 79.98 vs 73.61 and cost $4.64 vs $86.55.","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 213, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0104885, "cost_details": {"upstream_inference_completions_cost": 0.007029, "upstream_inference_cost": 0.0104885, "upstream_inference_prompt_cost": 0.0034595}, "is_byok": false, "prompt_tokens": 629, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 842}`

## T0073 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B matches or beats Model A on every stated criterion. Single-turn capability is essentially tied (85.361 vs 85.177), but Model B is far stronger on relevance/irrelevance behavior (84.215 vs 73.61), which matters for high-volume triage where the model must correctly decide when not to call a tool. Cost is decisive at high call volume: $1.35 vs $86.55 (~64x cheaper). Tail latency is also much better (2.3s vs 7.56s P95), supporting responsive high-throughput automation. Since individual errors are low consequence and reviewable, there is no accuracy premium that would justify Model A's cost and latency penalties.","cited_overall_rank":"Model B ranks first on this scenario's criteria (dominates on 3 of 4 metrics and ties on the fourth); Model A second.","cited_scenario_specific_evidence":["Relevance/irrelevance: 84.215 (B) vs 73.61 (A) — +10.6 points for B, key for triage where abstaining from tool calls is often correct","Benchmark cost: $1.35 (B) vs $86.55 (A) — roughly 64x cheaper, critical given high call volume","Measured P95 latency: 2.3s (B) vs
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01374, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01374, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 948}`

## T0074 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A matches or beats Model B on every stated criterion. Single-turn capability is essentially tied (85.361 vs 85.177, a 0.18-point gap that is immaterial), while Model A leads decisively on relevance/irrelevance behavior (84.215 vs 73.61, +10.6 points), which matters for high-volume triage where the model must correctly decline or route requests lacking a valid tool. Cost is the decisive differentiator at high call volume: $1.35 vs $86.55 is roughly a 64x difference, and the environment explicitly prizes low benchmark cost. Tail latency also favors Model A (2.3s vs 7.56s P95), supporting responsive CRM and ticket workflows. Because individual errors are reviewable and low consequence, there is no accuracy premium that would justify Model B's cost and latency penalties.","cited_overall_rank":"Not provided in the displayed evidence; selection based solely on the four stated criteria, on which Model A dominates.","cited_scenario_specific_evidence":["Single-turn capability effectively tied: 85.361 (A) vs 85.177 (B), gap of 0.18 is not decision-relevant","Relevance/irrelevance behavior: 84.215 (A) vs 73
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01374, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01374, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 948}`

## T0075 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates on every stated criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). Since errors here are reviewable and low consequence while call volume is high, cost and tail latency matter heavily, and B wins those decisively without any capability tradeoff.","cited_overall_rank":"Model B ranks first on all four displayed metrics; Model A second","cited_scenario_specific_evidence":"High-volume, low-consequence automations favor B's $1.35 benchmark cost and 2.3s P95 latency; B's 84.215 relevance/irrelevance score also reduces spurious tool calls in triage workflows, and its 85.361 single-turn capability meets the single-turn tool-call requirement."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 357, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.011415, "cost_details": {"upstream_inference_completions_cost": 0.008925, "upstream_inference_cost": 0.011415, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 855}`

## T0076 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A dominates Model B on every stated criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket-triage automations, cost and tail latency matter most, and Model A wins those decisively with no capability tradeoff.","cited_overall_rank":"Model A ranks first on all four displayed metrics; Model B ranks second on all four.","cited_scenario_specific_evidence":"High call volume makes the $86.55 vs $1.35 benchmark cost gap and the 7.56s vs 2.3s P95 latency gap decisive; reliable relevance/irrelevance behavior (84.215 vs 73.61) reduces spurious tool calls in triage workflows, while single-turn capability is effectively tied (85.361 vs 85.177)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 391, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.012265, "cost_details": {"upstream_inference_completions_cost": 0.009775, "upstream_inference_cost": 0.012265, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 889}`

## T0077 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A dominates on every stated criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence automations, cost and tail latency matter most, and A wins those decisively without sacrificing accuracy. There is no trade-off to weigh.","cited_overall_rank":"Model A ranks first on all four displayed dimensions; Model B second.","cited_scenario_specific_evidence":"High call volume with reviewable errors favors the cheapest, fastest option: Model A at $1.35 benchmark cost and 2.3s P95 latency versus Model B at $86.55 and 7.56s. Model A's relevance/irrelevance score of 84.215 also reduces spurious tool calls in triage-style routing, exceeding Model B's 73.61, while single-turn capability is effectively equal (85.361 vs 85.177)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 408, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01269, "cost_details": {"upstream_inference_completions_cost": 0.0102, "upstream_inference_cost": 0.01269, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 906}`

## T0078 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A dominates on every stated criterion: slightly higher single-turn capability (85.361 vs 85.177), materially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). Since this is high-volume, low-consequence automation work, cost and tail latency matter greatly, and Model A has no offsetting weakness to trade against.","cited_overall_rank":"Model A ranks first on all four displayed dimensions; Model B ranks second on all four.","cited_scenario_specific_evidence":"High call volume amplifies the cost gap ($1.35 vs $86.55 benchmark cost) and the latency gap (2.3s vs 7.56s P95). Reliable relevance/irrelevance behavior is explicitly required for triage-style routing, where Model A leads by 10.6 points (84.215 vs 73.61). Single-turn tool-call capability, the primary demand in these routine automations, is also marginally higher for Model A (85.361 vs 85.177)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 417, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.012915, "cost_details": {"upstream_inference_completions_cost": 0.010425, "upstream_inference_cost": 0.012915, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 915}`

## T0079 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A matches or exceeds Model B on every stated satisfaction criterion. Single-turn capability is effectively tied (85.361 vs 85.177), but Model A is far better on relevance/irrelevance behavior (84.215 vs 73.61, +10.6 points), which matters for high-volume triage where the model must correctly decline or route when no tool applies. Cost is decisive at high call volume: $1.35 vs $86.55 (~64x cheaper). Tail latency is also much better (2.3s vs 7.56s P95), supporting responsive high-throughput automation. Since individual errors are reviewable and low consequence, there is no quality justification for paying Model B's cost and latency penalty.","cited_overall_rank":"Not provided in the displayed evidence; selection based solely on the four listed criteria, on which Model A dominates.","cited_scenario_specific_evidence":["Relevance/irrelevance: Model A 84.215 vs Model B 73.61 - important for reliable abstention in ticket triage","Benchmark cost: Model A $1.35 vs Model B $86.55 - roughly 64x lower, critical given high call volume","Measured P95 latency: Model A 2.3s vs Model B 7.56s - meets the low tail
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01374, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01374, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 948}`

## T0080 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates Model A on every stated satisfaction criterion: slightly higher single-turn capability (85.361 vs 85.177), materially better relevance/irrelevance behavior (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). In a high-volume, low-consequence-per-error setting, cost and tail latency are decisive, and B wins those without any capability tradeoff.","cited_overall_rank":"Model B ranks first on all four displayed metrics; Model A second on all four.","cited_scenario_specific_evidence":"High call volume favors B's $1.35 benchmark cost and 2.3s P95 latency; reviewable low-consequence errors reduce the value of any capability edge, and B leads capability anyway (85.361) plus far better relevance/irrelevance discrimination (84.215 vs 73.61), which limits spurious tool calls at scale."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 370, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01174, "cost_details": {"upstream_inference_completions_cost": 0.00925, "upstream_inference_cost": 0.01174, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 868}`

## T0081 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates on every stated criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), substantially better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). In a high-volume, low-consequence setting, cost and tail latency scale directly with call volume, and stronger irrelevance handling reduces spurious tool invocations. There is no dimension on which Model A offers a compensating advantage.","cited_overall_rank":"Model B ranked first among the two profiles shown; Model A second","cited_scenario_specific_evidence":"High call volume with reviewable, low-consequence errors makes per-call cost ($1.35 vs $86.55) and P95 latency (2.3s vs 7.56s) decisive; routine CRM/ticket-triage automations depend on correctly declining irrelevant tool calls, where Model B leads 84.215 vs 73.61, while single-turn capability is effectively tied (85.361 vs 85.177)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 433, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013315, "cost_details": {"upstream_inference_completions_cost": 0.010825, "upstream_inference_cost": 0.013315, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 931}`

## T0082 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A dominates Model B on every stated satisfaction criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). Since errors here are low consequence and reviewable but volume is high, cost and tail latency matter heavily, and Model A wins those decisively without sacrificing accuracy. There is no dimension on which Model B is preferable.","cited_overall_rank":"Model A ranks first on all four displayed dimensions; Model B ranks second on all four.","cited_scenario_specific_evidence":"High call volume plus low per-error consequence makes cost per call and P95 latency the deciding factors: Model A at $1.35 and 2.3s P95 versus Model B at $86.55 and 7.56s P95. Reliable relevance/irrelevance behavior is required for triage-style routing, and Model A leads 84.215 vs 73.61 while also edging single-turn capability 85.361 vs 85.177."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 427, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013165, "cost_details": {"upstream_inference_completions_cost": 0.010675, "upstream_inference_cost": 0.013165, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 925}`

## T0083 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates on every stated criterion: marginally higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and roughly 3.3x lower P95 latency (2.3s vs 7.56s). Since errors here are low-consequence and reviewable but volume is high, cost per call and tail latency are decisive, and Model B wins those by wide margins without sacrificing capability.","cited_overall_rank":"Model B ranks first on all four displayed metrics; Model A second on all four.","cited_scenario_specific_evidence":"High call volume with reviewable, low-consequence errors makes the $1.35 vs $86.55 benchmark cost gap and the 2.3s vs 7.56s P95 latency gap the dominant factors; Model B also has the stronger relevance/irrelevance score (84.215 vs 73.61), which governs correct abstention on out-of-scope CRM/ticket requests, and a slightly higher single-turn tool-call score (85.361 vs 85.177) matching the single-turn nature of triage and back-office automations."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 436, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01339, "cost_details": {"upstream_inference_completions_cost": 0.0109, "upstream_inference_cost": 0.01339, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 934}`

## T0084 - Anthropic - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":97,"stated_reasoning":"Model A dominates Model B on every stated satisfaction criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). Since errors here are low consequence and reviewable while call volume is high, cost and tail latency matter heavily, and Model A wins those decisively without any capability tradeoff.","cited_overall_rank":"Not provided in the displayed evidence; selection based solely on the four listed metrics","cited_scenario_specific_evidence":["single-turn capability: A 85.361 vs B 85.177","relevance/irrelevance behavior: A 84.215 vs B 73.61 (~10.6 point advantage, key for high-volume triage where irrelevant tool calls must be avoided)","benchmark cost: A $1.35 vs B $86.55 (critical given high call volume)","measured P95 latency: A 2.3s vs B 7.56s (satisfies low tail latency requirement)"]}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 425, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013115, "cost_details": {"upstream_inference_completions_cost": 0.010625, "upstream_inference_cost": 0.013115, "upstream_inference_prompt_cost": 0.00249}, "is_byok": false, "prompt_tokens": 498, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 923}`

## T0085 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"The satisfaction criteria are explicitly single-turn tool-call capability, relevance/irrelevance handling, cost, and tail latency. Model A matches or beats Model B on every one of these: slightly higher single-turn score (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and much lower P95 latency (2.3s vs 7.56s). Model B's superior general BFCL overall rank (1 vs 47) reflects aggregate ability including multi-turn and other categories that this workload does not require; since errors here are reviewable and low consequence while volume is high, cost and latency dominate, and the general rank does not offset a clean sweep on the stated criteria.","cited_overall_rank":"Model B rank 1, Model A rank 47 (general rank noted but outweighed by scenario-specific metrics)","cited_scenario_specific_evidence":"Single-turn: 85.361 (A) vs 85.177 (B); relevance/irrelevance: 84.215 (A) vs 73.61 (B); cost: $1.35
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 41}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0086 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"All four satisfaction criteria favor Model B. Single-turn capability is essentially tied (85.361 vs 85.177), while Model B is clearly better on relevance/irrelevance (84.215 vs 73.61), roughly 64x cheaper ($1.35 vs $86.55), and has about 3x lower P95 latency (2.3s vs 7.56s). In a high-volume, low-consequence-per-error setting, cost and tail latency dominate, and Model B loses nothing on accuracy dimensions. Model A's superior general BFCL overall rank (1 vs 47) reflects aggregate performance including multi-turn behavior that this workload does not require, so it does not offset Model B's decisive advantages on the stated criteria.","cited_overall_rank":"Model A rank 1, Model B rank 47; overall rank discounted because it aggregates capabilities (e.g., multi-turn) outside the stated satisfaction conditions","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177 (parity); relevance/irrelevance 84.215 vs 73.61 (+10.6 for B); cost $1.35 vs $86.55 (critical at high call volume); P95 latency 2.3s vs 7.56
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0087 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The satisfaction criteria are single-turn tool-call quality, relevance/irrelevance reliability, low cost, and low tail latency. Model B matches Model A on single-turn capability (85.36 vs 85.18, effectively a tie), but is far better on relevance/irrelevance behavior (84.22 vs 73.61, ~10.6 points), roughly 64x cheaper ($1.35 vs $86.55), and about 3.3x faster at P95 (2.3s vs 7.56s). In a high-volume, low-consequence, reviewable workflow, cost and tail latency dominate, and avoiding spurious tool calls on irrelevant inputs matters more than marginal capability. Model A's superior general BFCL overall rank (1 vs 47) reflects aggregate performance including multi-turn and other categories not required here, so it does not outweigh the scenario-specific advantages of Model B.","cited_overall_rank":"Model A ranks 1 and Model B ranks 47 on general BFCL overall; this general rank was deprioritized because the scenario's stated requirements (single-turn, relevance/irrelevance, cost, P95 latency) all favor Model B.","cited
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 22}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0088 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The satisfaction criteria are single-turn tool-call quality, relevance/irrelevance handling, cost, and tail latency \u2014 Model B wins or ties on every one. Single-turn capability is essentially equal (85.361 vs 85.177), relevance/irrelevance is materially better (84.215 vs 73.61), cost is ~64x lower ($1.35 vs $86.55), and P95 latency is ~3.3x lower (2.3s vs 7.56s). In a high-volume, low-consequence, reviewable workload, cost and tail latency dominate, and Model A's rank-1 overall standing likely reflects strengths (e.g., multi-turn/complex reasoning) not required here. Model B is the clear choice.","cited_overall_rank":"Model B is BFCL overall rank 47 vs Model A rank 1; the general rank is not decisive here because the scenario-specific criteria all favor Model B.","cited_scenario_specific_evidence":"Single-turn: 85.361 (B) vs 85.177 (A); relevance/irrelevance: 84.215 (B) vs 73.61 (A); cost: $1.35 (B) vs $86.55 (A); P95 latency: 2.3s (B) vs 7.56
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0089 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The satisfaction criteria are single-turn tool-call quality, relevance/irrelevance handling, cost, and tail latency \u2014 Model B wins or ties on every one. Single-turn capability is essentially equal (85.361 vs 85.177), relevance/irrelevance is over 10 points better (84.215 vs 73.61), benchmark cost is ~64x lower ($1.35 vs $86.55), and P95 latency is ~3.3x faster (2.3s vs 7.56s). In a high-volume, low-consequence setting where individual errors are reviewable, cost and tail latency dominate, so Model B's overall BFCL rank of 47 versus Model A's rank 1 does not offset its clear advantage on the criteria that matter here; the general rank reflects aggregate capabilities (including multi-turn and other categories) not required by this workload.","cited_overall_rank":"Model B: BFCL overall rank 47; Model A: BFCL overall rank 1 (general rank deprioritized as it aggregates capabilities beyond this scenario's stated needs)","cited_scenario_specific_evidence":"single-turn: 85.361 (B) vs 85.177 (A); relevance/irrelevance: 84.215 (B) vs
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0090 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"The stated satisfaction criteria are single-turn tool-call capability, relevance/irrelevance behavior, cost, and tail latency. Model A matches or beats Model B on every one of these: slightly higher single-turn score (85.361 vs 85.177), substantially better relevance/irrelevance discrimination (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). The overall BFCL rank gap (1 vs 47) is driven by dimensions not required here (e.g., multi-turn/agentic composition), and in this environment errors are individually reviewable and low consequence while volume is high, so cost and latency dominate. Model A is the clear choice.","cited_overall_rank":"Model B is BFCL overall rank 1 and Model A is rank 47; the rank was deliberately discounted because it aggregates capabilities (notably multi-turn) outside this scenario's stated satisfaction conditions.","cited_scenario_specific_evidence":"Single-turn: 85.361 (A) vs 85.177 (B); relevance/irrelevance: 84.215 (A) vs 73.61 (B); cost: $1.35
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0091 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"The scenario prioritizes single-turn tool-call quality, relevance/irrelevance discrimination, cost, and tail latency at high call volume with low per-error consequence. Model A matches Model B on single-turn capability (85.36 vs 85.18, effectively a tie), but is decisively better on relevance/irrelevance (84.22 vs 73.61, ~10.6 points), ~64x cheaper ($1.35 vs $86.55), and has ~3.3x lower P95 latency (2.3s vs 7.56s). Model B's superior general BFCL overall rank (1 vs 47) reflects aggregate capability including multi-turn and other categories that this routine automation workload does not stress; it does not offset the cost and latency penalties at high volume, and it loses on the one behavioral axis (irrelevance handling) that most affects unattended automation noise. Since individual errors are reviewable and low consequence, the marginal capability implied by the rank gap has little practical value here.","cited_overall_rank":"Model B ranks 1 and Model A ranks 47 on general BFCL overall, but this general ranking was treated as non-decisive because the scenario's satisfaction conditions are narrower than aggregate BFCL performance.","cited_scenario_specific_
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0092 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The stated satisfaction criteria are single-turn tool-call quality, relevance/irrelevance reliability, low cost, and low tail latency. Model B matches Model A on single-turn capability (85.36 vs 85.18, effectively equal) while being substantially better on relevance/irrelevance (84.2 vs 73.6), ~64x cheaper ($1.35 vs $86.55), and ~3x faster at P95 (2.3s vs 7.56s). In a high-volume, low-consequence-per-error setting, cost and tail latency dominate, and better irrelevance handling reduces spurious tool calls. Model A's superior general BFCL overall rank (1 vs 47) does not translate into any advantage on the specific criteria that define satisfactory performance here.","cited_overall_rank":"Model A ranks 1 and Model B ranks 47 on general BFCL overall, but this general ranking was not decisive because Model A shows no edge on the scenario-relevant metrics.","cited_scenario_specific_evidence":"Single-turn: 85.361 (B) vs 85.177 (A); relevance/irrelevance: 84.215 (B) vs 73.61 (A); cost: $1.35 (B) vs $86.55 (A); P95
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0093 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"Every satisfaction criterion favors Model B: marginally higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), ~64x lower benchmark cost ($1.35 vs $86.55), and far lower P95 latency (2.3s vs 7.56s). In a high-volume, low-consequence setting, cost and tail latency dominate, and Model B loses nothing on accuracy. Model A's superior general BFCL overall rank (1 vs 47) reflects aggregate ability including multi-turn/agentic dimensions not required here, so it does not offset a 64x cost premium and 3x slower tail latency for routine CRM/ticket automations.","cited_overall_rank":"Model A rank 1; Model B rank 47 (general BFCL overall rank noted but deprioritized as it aggregates capabilities beyond this scenario's needs)","cited_scenario_specific_evidence":"Single-turn: 85.361 (B) vs 85.177 (A); relevance/irrelevance: 84.215 (B) vs 73.61 (A); cost: $1.35 (B) vs $86.55 (A); P95 latency: 2.3s (B) vs 7.
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0094 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"The satisfaction criteria are single-turn capability, relevance/irrelevance handling, cost, and tail latency — and Model A wins or ties on every one of them. Single-turn is effectively equal (85.36 vs 85.18), relevance/irrelevance is materially better (84.2 vs 73.6, which matters for triage where the model must decline out-of-scope requests), cost is ~64x lower ($1.35 vs $86.55), and P95 latency is ~3.3x lower (2.3s vs 7.56s). In a high-volume, low-consequence, reviewable workflow, cost and tail latency dominate, so Model B's superior general BFCL overall rank (1 vs 47) does not translate into an advantage on the dimensions this team actually cares about.","cited_overall_rank":"Model B holds BFCL overall rank 1 and Model A rank 47, but the overall rank is aggregated across capabilities (including multi-turn/agentic ones) not required here, so it is outweighed by the scenario-specific metrics.","cited_scenario_specific_evidence":"Single-turn: 85.361 (A) vs 85.177 (B); relevance/irrelevance: 84.215 (A) vs 73.61 (B); benchmark cost: $
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0095 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"All four satisfaction criteria favor Model A. Single-turn capability is essentially tied (85.36 vs 85.18), while Model A is clearly better on relevance/irrelevance behavior (84.2 vs 73.6), ~64x cheaper ($1.35 vs $86.55), and ~3.3x faster at P95 (2.3s vs 7.56s). In a high-volume, low-consequence setting, cost and tail latency dominate, and individual errors are reviewable, so Model B's superior general BFCL overall rank (1 vs 47) does not translate into an advantage on the stated conditions.","cited_overall_rank":"Model B rank 1; Model A rank 47 (general BFCL overall), not decisive here","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177; relevance/irrelevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 latency 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 363, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.011785, "cost_details": {"upstream_inference_completions_cost": 0.009075, "upstream_inference_cost": 0.011785, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 905}`

## T0096 - Anthropic - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"The stated satisfaction criteria are single-turn tool-call capability, relevance/irrelevance behavior, cost, and tail latency. Model A matches or beats Model B on every one of these: slightly higher single-turn score (85.36 vs 85.18), substantially better relevance/irrelevance handling (84.2 vs 73.6), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3x lower P95 latency (2.3s vs 7.56s). The environment is high-volume with low-consequence, reviewable errors, so cost and latency dominate, and Model A shows no capability tradeoff on the dimensions that matter. Model B's overall BFCL rank of 1 reflects aggregate strength (likely multi-turn and other categories not required here) and does not translate into any advantage on the criteria specified.","cited_overall_rank":"Model B is BFCL overall rank 1 and Model A is rank 47, but overall rank is not decisive here because the scenario only requires single-turn and relevance/irrelevance performance, where Model A is equal or better.","cited_scenario_specific_evidence":"Single-turn: 85.361 (A) vs 85.177 (B); relevance/irrelevance: 84.215 (A)
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01396, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01396, "upstream_inference_prompt_cost": 0.00271}, "is_byok": false, "prompt_tokens": 542, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 992}`

## T0097 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":96,"stated_reasoning":"Model B dominates Model A on every stated satisfaction criterion: marginally higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). Since this is a high-volume, low-consequence workload, cost and tail latency carry heavy weight, and there is no tradeoff to justify choosing Model A.","cited_overall_rank":"not consulted (provisional selection made before any BFCL overall rank was shown)","cited_scenario_specific_evidence":["single-turn capability: 85.361 (B) vs 85.177 (A) - parity or slight edge to B on the core tool-calling requirement","relevance/irrelevance behavior: 84.215 (B) vs 73.61 (A) - ~10.6 point advantage, important for avoiding spurious tool calls in CRM/ticket triage","benchmark cost: $1.35 (B) vs $86.55 (A) - decisive at high call volume","measured P95 latency: 2.3s (B) vs 7.56s (A) - satisfies the low meas
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0098 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible ~0.18 gap), markedly better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket/back-office automation, cost and tail latency scale with call volume and are decisive, while reviewable individual errors reduce the value of any marginal capability edge Model B might offer. There is no criterion on which Model B leads, so no trade-off analysis is required.","cited_overall_rank":"none — no general BFCL overall rank was shown or used in this provisional selection","cited_scenario_specific_evidence":"Single-turn capability 85.361 vs 85.177 (parity); relevance/irrelevance 84.215 vs 73.61 (Model A +10.6, key for reliably declining irrelevant tool calls in noisy triage queues); benchmark cost $1.35 vs $86.55 (Model A ~64x cheaper, critical at high call volume); meas
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0099 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), materially better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket-triage automation, cost and tail latency scale directly with call volume, and reliable irrelevance detection reduces spurious tool invocations. There is no criterion on which Model A leads, so no trade-off analysis is needed.","cited_overall_rank":"none - provisional selection made before any general BFCL overall rank was shown","cited_scenario_specific_evidence":["single-turn capability: 85.361 (B) vs 85.177 (A) - effectively tied","relevance/irrelevance behavior: 84.215 (B) vs 73.61 (A) - B better by 10.6 points, important for suppressing unnecessary tool calls at high volume","benchmark cost: $1.35 (B) vs $86.55 (A) - decisive given high call volume","measured P95
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0100 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18 gap), markedly better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket/back-office automation, cost and tail latency scale with call volume and dominate the value calculation, while the relevance/irrelevance advantage reduces spurious tool invocations that would otherwise create reviewer load. No criterion favors Model B, so there is no tradeoff to weigh.","cited_overall_rank":"not consulted; provisional selection made before viewing any general BFCL overall rank","cited_scenario_specific_evidence":["Single-turn tool-call capability effectively tied: 85.361 (A) vs 85.177 (B)","Relevance/irrelevance behavior: 84.215 (A) vs 73.61 (B) — material advantage for A in avoiding inappropriate tool calls","Benchmark cost: $1.35 (A) vs $86
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0101 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), markedly better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket-triage automations, cost and tail latency scale directly with call volume, and stronger irrelevance handling reduces spurious tool calls that would otherwise generate review load. There is no dimension on which Model B offers a meaningful advantage.","cited_overall_rank":"not consulted; provisional selection made before any general BFCL overall rank was shown","cited_scenario_specific_evidence":"Single-turn capability 85.361 vs 85.177 (parity); relevance/irrelevance 84.215 vs 73.61 (Model A +10.6); benchmark cost $1.35 vs $86.55 (decisive at high call volume); P95 latency 2.3s vs 7.56s (better tail behavior for high-throughput automations)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 443, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01362, "cost_details": {"upstream_inference_completions_cost": 0.011075, "upstream_inference_cost": 0.01362, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 952}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"The general BFCL overall rank does not change the decision because it aggregates dimensions (notably multi-turn and agentic categories) that are not part of this environment's satisfaction conditions. On the four stated criteria, Model A is equal or better across the board: single-turn capability is effectively tied (85.361 vs 85.177), relevance/irrelevance is ~10.6 points higher, benchmark cost is ~64x lower ($1.35 vs $86.55), and P95 latency is ~3.3x lower (2.3s vs 7.56s). Model B's rank-1 standing signals broad strength, but its rank advantage is not expressed in any metric that matters here, while its cost and tail latency are severe liabilities at high call volume. Errors are reviewable and low consequence, so the small capability risk is acceptable and the operational savings dominate.","cited_overall_rank":"Model A: 47; Model B: 1 (acknowledged but treated as low weight because overall rank aggregates out-of-scope multi-turn/agentic dimensions)","cited_scenario_specific_evidence":"Single-turn capability 85.361 vs 85.177 (parity on the primary in-scope capability); relevance/irrelevance 84.215 vs 73.61 (fewer
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.016735, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.016735, "upstream_inference_prompt_cost": 0.005485}, "is_byok": false, "prompt_tokens": 1097, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1547}`

## T0102 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B matches or beats Model A on every stated satisfaction criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3x lower P95 latency (2.3s vs 7.56s). Given high call volume with low-consequence, reviewable errors, cost and tail latency dominate, and Model B also has the better accuracy profile, so there is no tradeoff to weigh.","cited_overall_rank":"none available at time of provisional selection","cited_scenario_specific_evidence":["single-turn capability: 85.361 (B) vs 85.177 (A)","relevance/irrelevance behavior: 84.215 (B) vs 73.61 (A) — key for avoiding spurious tool calls at high volume","benchmark cost: $1.35 (B) vs $86.55 (A) — decisive under high call volume","P95 latency: 2.3s (B) vs 7.56s (A) — satisfies the low measured tail latency requirement"]}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 416, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.012945, "cost_details": {"upstream_inference_completions_cost": 0.0104, "upstream_inference_cost": 0.012945, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 925}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The general BFCL overall rank aggregates capabilities (multi-turn, multi-step, agentic categories) that are not part of this environment's satisfaction conditions. On every stated criterion, Model B is equal or better: single-turn capability 85.361 vs 85.177, relevance/irrelevance 84.215 vs 73.61, cost $1.35 vs $86.55, and P95 latency 2.3s vs 7.56s. For high-volume, low-consequence, reviewable tasks, the ~64x cost gap and ~3x tail-latency gap are decisive, and Model A's rank-1 standing does not translate into any measured advantage here. Model A's much weaker relevance/irrelevance score is also a direct liability for triage-style workloads where declining to call a tool matters. I therefore keep Model B despite its rank 47.","cited_overall_rank":"Model B: 47; Model A: 1 (noted but not weighted, as overall rank reflects capabilities outside the stated satisfaction conditions)","cited_scenario_specific_evidence":["single-turn capability: 85.361 (B) vs 85.177 (A) — effectively tied, no rank-implied edge for A","relevance/irrelevance behavior: 84.215 (B) v
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0166, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.0166, "upstream_inference_prompt_cost": 0.00535}, "is_byok": false, "prompt_tokens": 1070, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1520}`

## T0103 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18 gap), materially better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), roughly 64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). In a high-volume, low-consequence-per-error setting, cost and tail latency are heavily weighted, and better irrelevance handling reduces spurious tool calls at scale. There is no dimension on which Model A is meaningfully preferable.","cited_overall_rank":"none - provisional selection made before any general BFCL overall rank was shown","cited_scenario_specific_evidence":["single-turn tool-call capability: 85.361 (B) vs 85.177 (A) - effectively tied","relevance/irrelevance behavior: 84.215 (B) vs 73.61 (A) - B better by ~10.6 points, directly relevant to avoiding unnecessary tool calls at high volume","benchmark cost: $1.35 (B) vs $86.55 (A) - decisive for high call volume","measured P95 lat
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0104 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), far better relevance/irrelevance discrimination (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence CRM/ticket/back-office automation, cost and tail latency are decisive, and Model A gives up nothing in capability to win them. Model B has no compensating advantage on any listed dimension.","cited_overall_rank":"none - provisional selection made before any BFCL overall rank was shown","cited_scenario_specific_evidence":["single-turn tool-call capability: 85.361 (A) vs 85.177 (B) - effectively tied","relevance/irrelevance behavior: 84.215 (A) vs 73.61 (B) - A materially better at avoiding spurious tool calls, which matters at high call volume","benchmark cost: $1.35 (A) vs $86.55 (B) - decisive for high-volume routine automation","measured P95 latency: 2.3s (A) vs
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0105 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates Model A on every stated criterion: slightly higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). Since the environment is high-volume with low-consequence, reviewable errors, cost and tail latency matter heavily, and there is no trade-off to weigh because B is not worse anywhere.","cited_overall_rank":"none — provisional selection made before viewing any general BFCL overall rank","cited_scenario_specific_evidence":"High call volume favors B's $1.35 benchmark cost and 2.3s P95 latency; reliable irrelevance handling for CRM/ticket triage favors B's 84.215 relevance score; single-turn tool-call focus matches B's 85.361 single-turn capability."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 361, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01157, "cost_details": {"upstream_inference_completions_cost": 0.009025, "upstream_inference_cost": 0.01157, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 870}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"The BFCL overall rank aggregates categories (notably multi-turn/agentic performance) that are not part of this environment's satisfaction conditions. On the four stated criteria, Model B is equal-or-better on all: single-turn capability 85.361 vs 85.177, relevance/irrelevance 84.215 vs 73.61, cost $1.35 vs $86.55, P95 latency 2.3s vs 7.56s. Model A's rank-1 standing likely reflects strengths in multi-turn scenarios that this routine, single-turn, reviewable-error workload does not require, and paying ~64x more cost and ~3.3x worse tail latency at high volume is unjustified. Keeping Model B.","cited_overall_rank":"Model A rank 1, Model B rank 47 — noted but treated as low weight because overall rank bundles multi-turn/agentic dimensions outside this scenario's criteria","cited_scenario_specific_evidence":"Single-turn parity-to-advantage (85.361 vs 85.177), materially better irrelevance handling for triage-style false-trigger avoidance (84.215 vs 73.61), and high-volume econom
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 33}, "cost": 0.016325, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.016325, "upstream_inference_prompt_cost": 0.005075}, "is_byok": false, "prompt_tokens": 1015, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1465}`

## T0106 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":97,"stated_reasoning":"Model B dominates Model A on every stated criterion: marginally higher single-turn capability (85.361 vs 85.177), substantially better relevance/irrelevance behavior (84.215 vs 73.61), roughly 64x lower benchmark cost ($1.35 vs $86.55), and about 3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence automations, the cost and tail-latency advantages are decisive, and there is no trade-off to weigh since B is not worse on any dimension.","cited_overall_rank":"none - provisional selection made before any general BFCL overall rank was shown","cited_scenario_specific_evidence":"High call volume with reviewable, low-consequence errors makes per-call cost ($1.35 vs $86.55) and P95 latency (2.3s vs 7.56s) the dominant factors; reliable irrelevance handling (84.215 vs 73.61) reduces spurious tool calls in CRM/ticket-triage flows, while single-turn capability is effectively tied."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 388, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.012245, "cost_details": {"upstream_inference_completions_cost": 0.0097, "upstream_inference_cost": 0.012245, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 897}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":94,"stated_reasoning":"The overall BFCL rank is an aggregate over capabilities (including multi-turn, multi-step, and other categories) that this scenario does not require. On every criterion the team actually specified, Model B is equal or better: single-turn capability is effectively tied (85.361 vs 85.177), relevance/irrelevance behavior is clearly better (84.215 vs 73.61), cost is ~64x lower ($1.35 vs $86.55), and P95 latency is ~3.3x lower (2.3s vs 7.56s). Model A's rank-1 standing presumably comes from strengths outside the satisfaction conditions, and its cost/latency profile is a poor fit for high-volume, low-consequence automations where individual errors are reviewable. I keep Model B.","cited_overall_rank":"Model A rank 1, Model B rank 47 - acknowledged but treated as low weight because the aggregate rank reflects capabilities outside this scenario's stated requirements","cited_scenario_specific_evidence":"High call volume plus reviewable, low-consequence errors makes benchmark cost ($1.35 vs $86.55) and tail latency (2.3s vs 7.56s) the decisive factors; better irrelevance handling (84.215 vs 73.61)
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01646, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01646, "upstream_inference_prompt_cost": 0.00521}, "is_byok": false, "prompt_tokens": 1042, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1492}`

## T0107 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on every stated satisfaction criterion: near-identical single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), substantially better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). Given high call volume with low per-error consequence, cost and tail latency matter disproportionately, and better irrelevance handling reduces spurious tool calls at scale. There is no dimension on which Model A offers a meaningful advantage.","cited_overall_rank":null,"cited_scenario_specific_evidence":["single-turn capability 85.361 (B) vs 85.177 (A) — effectively tied","relevance/irrelevance 84.215 (B) vs 73.61 (A) — B better by ~10.6 points, important for avoiding unnecessary tool calls at high volume","benchmark cost $1.35 (B) vs $86.55 (A) — decisive under high call volume","P95 latency 2.3s (B) vs 7.56s (A) — B meets low tail latency requirement","Low consequence of individual err
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0108 - Anthropic - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated satisfaction criterion: essentially equal single-turn capability (85.361 vs 85.177, a negligible 0.18-point gap), markedly better relevance/irrelevance behavior (84.215 vs 73.61, ~10.6 points), ~64x lower benchmark cost ($1.35 vs $86.55), and ~3.3x lower P95 latency (2.3s vs 7.56s). For high-volume, low-consequence, reviewable CRM/ticket/back-office automation, cost and tail latency are decisive, and better irrelevance handling reduces spurious tool calls at scale. There is no dimension on which Model B offers a meaningful advantage.","cited_overall_rank":null,"cited_scenario_specific_evidence":["single-turn capability: 85.361 (A) vs 85.177 (B) - effectively tied","relevance/irrelevance behavior: 84.215 (A) vs 73.61 (B) - A better by ~10.6 points, important for avoiding unnecessary tool calls at high volume","benchmark cost: $1.35 (A) vs $86.55 (B) - A ~64x cheaper, decisive under high call volume","measured P95 latency: 2.3s (A)
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.013795, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.013795, "upstream_inference_prompt_cost": 0.002545}, "is_byok": false, "prompt_tokens": 509, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 959}`

## T0109 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary criteria are multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model A leads or ties on the primary axes: multi-turn 68.38 vs 68.00, memory 73.76 vs 55.70 (+18.06, decisive for agentic workflows that carry state across account changes and procurement steps), web-search 84.5 vs 77.5, and P95 latency 7.56s vs 13.50s (roughly 44% faster at the tail, which matters for interactive support and orchestration). Model B's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a ~6.4 point gap) and cost ($4.64 vs $86.55). Since cost is explicitly secondary, the large memory gap plus better search and latency outweigh B's relevance edge; A's relevance score of 73.61 is still workable and can be reinforced with tool-gating and prompt constraints.","cited_overall_rank":"Model A ranks first on the weighted set of stated primary criteria (4 of 5 primary metrics); Model B ranks first only on relevance behavior and on the secondary cost criterion.","cited_scenario_specific_evidence":"Memory 73.76 vs 55.70
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0110 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":74,"stated_reasoning":"The stated priorities put multi-turn completion, memory, web-search, relevance, and latency ahead of cost. Model B matches Model A on multi-turn (68.38 vs 68.0), leads decisively on memory (73.76 vs 55.7, +18.1) and web-search (84.5 vs 77.5, +7.0), and nearly halves tail latency (7.56s vs 13.50s P95). Its only weaknesses are relevance/irrelevance behavior (73.61 vs 79.98, -6.4) and a much higher benchmark cost ($86.55 vs $4.64). For multi-step agent workflows like procurement and account changes, durable memory across turns and reliable retrieval are the load-bearing capabilities, and the latency advantage compounds across long tool-calling chains; the relevance gap is a real but narrower deficit that can be mitigated with scoping and guardrails at the orchestration layer. Since cost is explicitly secondary, the ~19x spend is a t
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 47}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0111 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"The stated satisfaction criteria weight multi-turn completion, memory, web-search, relevance behavior, and latency as primary, with cost explicitly secondary. Model A leads on four of five primary dimensions: multi-turn (68.38 vs 68, effectively parity), memory (73.76 vs 55.7, an ~18-point gap that matters most for long agent workflows like account changes and procurement that carry state across turns), web-search (84.5 vs 77.5, relevant to technical support and knowledge-work automation), and P95 latency (7.56s vs 13.50s, nearly half, which directly affects interactive agent usability). Its only deficit is relevance/irrelevance behavior (73.61 vs 79.98, ~6.4 points), meaning slightly weaker discipline in declining or defl
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 88}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0112 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"The stated priorities rank multi-turn completion, memory, web-search, relevance behavior, and latency above cost. Model B leads or ties on nearly all primary criteria: multi-turn (68.38 vs 68.00), memory (73.76 vs 55.70, an 18-point gap that directly affects multi-step agent workflows requiring state retention across account changes and procurement steps), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s, roughly 44% faster). Model A's only advantages are relevance/irrelevance behavior (+6.4 points) and cost ($4.64 vs $86.55, ~19x cheaper), but cost is explicitly secondary. Model A's weak memory score is a material risk for workflow orchestration and multi-turn task completion, and its higher latency degrades interactive support experiences. If cost were promoted to a primary constraint, Model A would become the stronger candidate.","cited_overall_rank":"Model B ranks first on the weighted primary criteria (4 of 5 non-cost dimensions); Model A ranks second overall but first on cost efficiency and relevance discrimination.","cited_scenario_specific_evidence":"Multi-step agent workflows (account changes, procurement
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0113 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A leads on the primary criteria that matter most for multi-step agent workflows: multi-turn completion (68.38 vs 68, essentially tied but not worse), memory behavior (73.76 vs 55.70, an 18-point advantage that directly affects account changes, procurement, and orchestration where state must persist across steps), and web-search behavior (84.5 vs 77.5, relevant to technical support and knowledge-work automation). Latency, an explicit satisfaction condition, also favors Model A (7.56s vs 13.50s P95 — roughly 44% faster). Model B wins only on relevance/irrelevance behavior (79.98 vs 73.61, a ~6-point gap) and on cost ($4.64 vs $86.55), but cost is explicitly a secondary factor in this environment. Trading a large memory deficit and near-doubled tail latency for cost savings would undermine the stated definition of satisfactory performance; Model A is the better fit, with the caveat that its lower relevance/irrelevance score suggests guardrails for out-of-scope requests.","cited_overall_rank":"Model A ranks first on the weighted primary criteria (multi-turn, memory, web-search, latency); Model B ranks first only on cost and relevance/irr
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0114 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary criteria are multi-turn completion, memory, web-search, relevance/irrelevance, and latency, with cost only secondary. Model A leads or ties on the majority of primary dimensions: multi-turn (68.38 vs 68.00), memory (73.76 vs 55.70, an 18-point gap that directly affects multi-step account/procurement workflows requiring state retention), web-search (84.50 vs 77.50), and P95 latency (7.56s vs 13.50s, roughly 44% faster). Model B wins only on relevance/irrelevance (79.98 vs 73.61, a ~6.4-point edge) and on cost ($4.64 vs $86.55, ~19x cheaper). Since cost is explicitly secondary and the memory plus latency advantages are decisive for agentic orchestration reliability and user-facing responsiveness, Model A is the stronger fit. Model B would be the rational pick only if budget were a hard constraint or if false-tool-invocation suppression were the dominant risk.","cited_overall_rank":"Model A ranks first on the weighted set of stated primary criteria (4 of 5 primary dimensions); Model B ranks second overall despite first place on cost and relevance/irrelevance.","cited_scenario
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0115 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":72,"stated_reasoning":"Model B leads on the primary criteria that matter most for multi-step agent workflows: multi-turn completion (68.38 vs 68, essentially tied but not worse), memory behavior (73.76 vs 55.7, an 18-point advantage that directly affects account changes, procurement, and workflow orchestration that span many turns), and web-search behavior (84.5 vs 77.5, important for technical support and knowledge-work automation). It also has materially better latency (7.56s vs 13.50s P95), which is an explicit requirement. Model A's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a ~6.4-point edge) and cost ($4.64 vs $86.55). Cost is stated as a secondary factor, so the large cost gap is acknowledged but not decisive; the relevance gap is the real trade-off, and Model B's 73.61 remains a workable level that can be mitigated with routing/guardrails, whereas Model A's weak memory score cannot easily be engineered around in long multi-step sessions. If budget were a hard constraint rather than secondary, Model A would become the rational pick.","cited_overall_rank":"Model B ranked first on the weighted primary criteria (memory, web-search, latency, multi-turn
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0116 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":76,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance handling, and latency, with cost explicitly secondary. Model B leads on four of the five primary dimensions: multi-turn (68.38 vs 68.0, essentially parity but slightly ahead), memory (73.76 vs 55.7, an 18-point gap that matters most for multi-step agent workflows like account changes and procurement that carry state across turns), web-search (84.5 vs 77.5, relevant for technical support and knowledge-work automation), and P95 latency (7.56s vs 13.50s, nearly half, which is material for interactive agent orchestration). Model A's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a ~6.4-point edge that gives it better guardrails against out-of-scope requests) and a far lower benchmark
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 100}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0117 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":76,"stated_reasoning":"The stated satisfaction criteria weight multi-turn completion, memory, web-search, relevance, and latency as primary, with cost explicitly secondary. Model B leads on four of the five primary dimensions: multi-turn (68.38 vs 68.0, essentially parity but not worse), memory (73.76 vs 55.7, an ~18-point gap that directly drives multi-step agent workflows like account changes and procurement requests spanning many turns), web-search (84.5 vs 77.5, relevant to technical support and knowledge-work automation), and P95 latency (7.56s vs 13.50s, roughly 44% faster, which matters for interactive orchestration). Model A's only primary advantage is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is a real gap in refusing out-of
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 124}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0118 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary criteria are multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model A leads or ties on the primary axes: multi-turn 68.38 vs 68.00 (effectively equal), memory 73.76 vs 55.70 (+18.1, a decisive gap for agent workflows that carry state across account changes and procurement steps), and web-search 84.5 vs 77.5 (+7.0, important for technical support and knowledge-work retrieval). Model A is also markedly faster at P95, 7.56s vs 13.50s, which matters for interactive multi-step orchestration. Model B's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, +6.4) and cost ($4.64 vs $86.55, ~19x cheaper); relevance is a single primary axis where the deficit is modest and partially mitigable via prompt/tool-gating, and cost is explicitly secondary. Model B's weak memory score is the disqualifying factor for stateful agent workflows. If per-request spend were a hard constraint rather than a secondary factor, Model B would become the rational pick.","cited_overall_rank":"Model A ranked first on the stated primary criteria (leading 4 of
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0119 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Primary criteria are weighted toward multi-step agent capability: multi-turn, memory, web-search, relevance behavior, and latency, with cost explicitly secondary. Model A leads or ties on four of the five primary dimensions: multi-turn (68.38 vs 68.0, effectively a tie), memory (73.76 vs 55.7, an 18-point advantage that matters most for long agent workflows like account changes and procurement that require carrying state across turns), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s, nearly 2x faster, which matters for interactive support and orchestration). Model B's only primary-criteria advantage is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is a real but narrower gap addressable with routing guardrails and tool-gating prompts. Model B's large cost advantage ($4.64 vs $86.55) is compelling but is designated secondary, so it cannot override deficits in memory and latency that directly determine task completion in agent workflows.","cited_overall_rank":"Model A ranks first on the weighted primary criteria (leading in memory, web-search, and latency, tied on multi-turn); Model B ranks second overall despite ranking
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0120 - Anthropic - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":74,"stated_reasoning":"Primary criteria favor Model A: multi-turn is effectively tied (68.38 vs 68.00), while Model A leads clearly on memory (73.76 vs 55.70, +18.1) and web-search (84.50 vs 77.50, +7.0), both of which are core to multi-step agent workflows involving account changes, procurement lookups, and support research. Model A also has substantially better P95 latency (7.56s vs 13.50s), which matters for interactive orchestration. Model B's only advantages are relevance/irrelevance behavior (+6.4) and cost (~19x cheaper at $4.64 vs $86.55), but cost is explicitly a secondary factor and the relevance gap is smaller than the memory deficit. Model B's weak memory score is a material risk for long multi-turn task completion, so Model A is the better fit despite higher benchmark cost.","cited_overall_rank":"Model A ranked first on the weighted primary criteria (multi-turn, memory, web-search, latency); Model B ranked first only on relevance/irrelevance and cost.","cited_scenario_specific_evidence":"Memory 73.76 vs 55.70 supports multi-step workflows needing state retention across account-change and procurement steps; web-search 84.50
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014155, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014155, "upstream_inference_prompt_cost": 0.002905}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1031}`

## T0121 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A wins on nearly every primary criterion: comparable multi-turn completion (68.38 vs 68), substantially better memory behavior (73.76 vs 55.7, an ~18-point gap that matters for multi-step account/procurement/support workflows), better web-search behavior (84.5 vs 77.5), and materially lower P95 latency (7.56s vs 13.50s, roughly half). Its only deficits are relevance/irrelevance behavior (73.61 vs 79.98, a ~6-point gap that can be mitigated with routing/guardrails and clearer tool descriptions) and cost ($86.55 vs $4.64). Since cost is explicitly a secondary factor and the stated satisfaction criteria are weighted toward multi-turn completion, memory, search, and latency, Model A is the stronger fit; it is also the higher-ranked model on general BFCL overall (rank 1 vs 4), which corroborates the profile. Model B remains the sensible fallback if the deployment is cost-constrained at scale or if false-trigger avoidance is the dominant risk.","cited_overall_rank":"Model A: BFCL overall rank 1 (vs Model B rank 4)","cited_scenario_specific_evidence":"multi-turn
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0122 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":82,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance/irrelevance, then latency, with cost only secondary. Model A leads or ties on the primary dimensions: multi-turn (68.38 vs 68.00), memory (73.76 vs 55.70, an 18-point gap that matters greatly for multi-step account/procurement/support workflows that carry state across turns), and web-search (84.5 vs 77.5). Model A also has substantially better P95 latency (7.56s vs 13.50s), which is material for interactive agent workflows. Model B's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a moderate ~6-point edge) and cost ($4.64 vs $86.55), but cost is explicitly a secondary factor. Model A's higher overall BFCL rank (1 vs 4) corroborates the scenario-specific evidence rather than driving the decision. The one genuine concern is Model A's weaker irrelevance handling, which can cause unnecessary tool calls on out-of-scope requests; this is mitigable with prompt-level guardrails, whereas Model B's memory deficit and latency are harder to engineer around.","cited_overall_rank":"Model A: B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0123 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A leads on the primary criteria that matter most for multi-step agent workflows: multi-turn completion (68.38 vs 68.00, essentially tied but not worse), memory behavior (73.76 vs 55.70, an 18-point advantage that directly affects account changes, procurement, and workflow orchestration where state must persist across turns), and web-search behavior (84.5 vs 77.5, relevant to technical support and knowledge-work automation). It also has substantially better latency (7.56s vs 13.50s P95), which is an explicit satisfaction condition. Model B's only edges are relevance/irrelevance behavior (79.98 vs 73.61, a ~6-point gap) and cost ($4.64 vs $86.55), but cost is explicitly a secondary factor, and the relevance advantage does not offset the large memory deficit plus nearly double the tail latency. Model A also holds the higher general BFCL overall rank (1 vs 4), which is consistent with the scenario-specific evidence rather than contradicting it. Model B would be the rational pick only if budget were a hard constraint or if the workload were dominated by rejecting out-of-scope requests.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0124 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A wins on the primary criteria that matter most for multi-step agent workflows: slightly higher multi-turn completion (68.38 vs 68), substantially better memory behavior (73.76 vs 55.7, an ~18-point gap that directly affects long account-change and procurement flows), better web-search behavior (84.5 vs 77.5), and much lower P95 latency (7.56s vs 13.50s, roughly half). Model B's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a ~6-point edge) and cost, which is explicitly a secondary factor. Model A also holds the stronger general BFCL overall rank (1 vs 4). The high benchmark cost ($86.55 vs $4.64) is the main drawback and would warrant routing cheap/simple turns to a smaller model, but it does not outweigh the memory, search, and latency gains under the stated satisfaction conditions.","cited_overall_rank":"Model A: BFCL overall rank 1; Model B: BFCL overall rank 4","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68.0; memory 73.76 vs 55.7 (+18.06 for Model A); web-search 84.5 vs
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0125 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":82,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model B leads on multi-turn (68.38 vs 68), memory by a wide margin (73.76 vs 55.70), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s), which matters for interactive agent workflows like account changes and support. Model A wins only on relevance/irrelevance (79.98 vs 73.61) and cost ($4.64 vs $86.55); the relevance gap (~6.4 pts) is smaller than B's memory advantage (~18 pts) and search advantage (7 pts), and cost is explicitly de-prioritized. Model B also holds the top general BFCL overall rank, consistent with its per-criterion advantages.","cited_overall_rank":"Model B: BFCL overall rank 1; Model A: BFCL overall rank 4","cited_scenario_specific_evidence":"Multi-turn: B 68.38 vs A 68.00; Memory: B 73.76 vs A 55.70; Web-search: B 84.5 vs A 77.5; Relevance/irrelevance: A 79.98 vs B 73.
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0126 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":82,"stated_reasoning":"The stated criteria prioritize multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model A leads on four of the five primary dimensions: multi-turn (68.38 vs 68.00), memory (73.76 vs 55.70, an ~18-point gap that matters directly for account changes, procurement, and multi-step orchestration where state must persist), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s, roughly 44% faster for interactive agent work). Model B's only primary-criterion advantage is relevance/irrelevance behavior (79.98 vs 73.61, ~6 points), which is meaningful but narrower than Model A's combined memory, search, and latency edge. Model B's much lower benchmark cost ($4.64 vs $86.55) is a real advantage, but cost is explicitly a secondary factor here, and the memory deficit is the most consequential weakness for long-horizon enterprise workflows
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 78}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0127 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":82,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model B leads on four of the five primary criteria: multi-turn (68.38 vs 68.00, effectively parity), memory (73.76 vs 55.70, a large 18-point advantage that matters most for multi-step account/procurement/support workflows carrying state across turns), web-search (84.50 vs 77.50), and P95 latency (7.56s vs 13.50s, ~44% faster for interactive agent use). Model A's only primary-criterion edge is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is meaningful for tool-call abstention but does not offset the memory and latency gaps in long-horizon orchestration. Model A's much lower benchmark cost ($4.64 vs $86.55) is a genuine advantage but is explicitly a secondary factor here; if budget const
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 69}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0128 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":82,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance/irrelevance, and latency, with cost only secondary. Model A leads on multi-turn (68.38 vs 68), substantially on memory (73.76 vs 55.70, an 18-point gap that matters directly for multi-step agent workflows carrying state across account changes and procurement steps), on web-search (84.5 vs 77.5), and on P95 latency (7.56s vs 13.50s, roughly half). Model B's only advantages are relevance/irrelevance (79.98 vs 73.61, a ~6-point edge) and cost ($4.64 vs $86.55), but cost is explicitly secondary here, and the single behavioral edge does not offset deficits across three primary dimensions plus latency. Model A also holds the higher general BFCL overall rank (1 vs 4), which is consistent with the scenario evidence rather than the deciding factor. If budget constraints were reclassified as primary, Model B would become attractive given its near-parity multi-turn score and ~18x lower cost.","cited_overall_rank":"Model A: BFCL overall rank 1; Model B: BFCL overall rank 4","cited_scenario_specific_evidence":"
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0129 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":82,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance behavior, and latency, with cost only secondary. Model B leads on four of the five primary dimensions: multi-turn (68.38 vs 68.00, effectively parity), memory (73.76 vs 55.70, an ~18-point advantage that matters most for multi-step account/procurement/support workflows that carry state across turns), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s, roughly 44% faster at the tail). Model A's only primary-dimension edge is relevance/irrelevance behavior (79.98 vs 73.61, ~6 points), which is meaningful for avoiding spurious tool calls but is outweighed by B's much stronger memory and lower tail latency for orchestration-heavy work. Model A's large cost advantage ($4.64 vs $86.55) is real but explicitly designated secondary; it would only
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 82}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0130 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"The stated satisfaction criteria weight multi-turn completion, memory, web-search, relevance behavior, and latency ahead of cost. Model B leads on four of the five primary dimensions: multi-turn (68.38 vs 68.00, essentially parity), memory (73.76 vs 55.70, an 18-point gap that matters directly for multi-step account-change and procurement workflows that carry state across turns), web-search (84.5 vs 77.5, relevant to technical support and knowledge-work automation), and P95 latency (7.56s vs 13.50s, nearly half, which is material for interactive agent loops). Model A's only primary-criterion advantage is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is a real but narrower deficit that can be mitigated with tool-gating and prompt-level guardrails, whereas weak memory cannot be patched as easily. Model A's cost advantage is large ($4.64 vs $86.55) but cost is explicitly designated sec
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 80}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0131 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":80,"stated_reasoning":"The stated satisfaction criteria weight multi-turn completion, memory, web-search, relevance handling, and latency ahead of cost. Model B matches Model A on multi-turn (68.38 vs 68.00) and leads decisively on the two capabilities most tied to long agent workflows: memory (73.76 vs 55.70, +18.1) and web-search (84.50 vs 77.50, +7.0). It also nearly halves P95 latency (7.56s vs 13.50s), which matters for interactive account-change, procurement, and support flows. Model A's only capability edge is relevance/irrelevance behavior (79.98 vs 73.61, +6.4), a smaller gap that does not offset an 18-point memory deficit in multi-step orchestration. Model A's large cost advantage ($4.64 vs $86.55) is real but explicitly secondary; if budget were primary the choice would flip. Model B's higher general BFCL rank (1 vs 4) is consistent with, but not the basis
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 85}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0132 - Anthropic - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":82,"stated_reasoning":"Model B wins on the criteria weighted most heavily for multi-step agent workflows: essentially equal multi-turn completion (68.38 vs 68.00), a large memory advantage (73.76 vs 55.70, ~18 points), better web-search behavior (84.5 vs 77.5), and roughly half the P95 latency (7.56s vs 13.50s), which matters for interactive account-change, procurement, and support flows. Model A's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, ~6 points) and much lower benchmark cost ($4.64 vs $86.55), but cost is explicitly a secondary factor here, and the relevance edge does not offset deficits in memory, search, and latency, which are all primary satisfaction conditions. Model B also holds the higher general BFCL overall rank (1 vs 4), which corroborates the scenario-specific evidence rather than contradicting it. Recommendation: select Model B, and mitigate cost by routing simple single-turn or low-stakes tasks to a cheaper model.","cited_overall_rank":"Model B: BFCL overall rank 1 (vs Model A rank 4)","cited_scenario_specific_evidence":"Multi-turn 68.38 vs
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.014375, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.014375, "upstream_inference_prompt_cost": 0.003125}, "is_byok": false, "prompt_tokens": 625, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1075}`

## T0133 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Primary criteria favor Model A: it leads on multi-turn (68.38 vs 68.00, essentially tied), memory (73.76 vs 55.70, an 18-point gap that matters most for multi-step agent workflows requiring state retention across account changes and procurement steps), and web-search (84.5 vs 77.5). Model A also has substantially better P95 latency (7.56s vs 13.50s), which is a stated satisfaction condition. Model B wins only on relevance/irrelevance (79.98 vs 73.61, ~6.4 points) and cost ($4.64 vs $86.55), but cost is explicitly secondary. The memory plus latency advantages are decisive for the described agentic orchestration use case; Model B's ~19x cost advantage is noted as a fallback if budget becomes a hard constraint rather than a secondary factor.","cited_overall_rank":"not available at provisional stage","cited_scenario_specific_evidence":"Memory 73.76 vs 55.70 (critical for multi-step account/procurement workflows); web-search 84.5 vs 77.5 (knowledge-work automation); multi-turn 68.38 vs 68.00 (parity); P95 latency 7.56s vs 13.50s (interact
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0134 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Model A leads on the capabilities most central to multi-step agent workflows: memory (73.76 vs 55.7, an 18-point gap that matters for account changes and long procurement/support threads), web-search (84.5 vs 77.5, relevant for knowledge-work automation and technical support lookups), and multi-turn completion (68.38 vs 68.0, effectively tied but not worse). It also halves P95 latency (7.56s vs 13.50s), which directly affects interactive agent usability in orchestration chains. Model B's only advantage on quality is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is meaningful for avoiding spurious tool calls but can be partly mitigated with prompt/tool-gating and guardrails, whereas weak memory cannot be easily patched. Cost is explicitly a secondary factor, so Model B's much lower benchmark cost ($4.64 vs $86.55) does not override the memory, search, and latency advantages; however, that ~19x cost difference is large enough that if throughput volume is high or if false-trigger tool calls prove costly in production, Model B becomes a defensible alternative.","cited_overall_rank":"none — provisional selection made before any BFCL overall r
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0135 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":74,"stated_reasoning":"Primary criteria are multi-turn completion, memory, web-search, relevance/irrelevance, and latency, with cost only secondary. Model A leads on multi-turn (68.38 vs 68, essentially tied), memory (73.76 vs 55.70, a large ~18-point advantage that matters most for multi-step agent workflows requiring state carryover across account changes and procurement steps), and web-search (84.5 vs 77.5, important for technical support and knowledge-work retrieval). Model A is also substantially faster at P95 (7.56s vs 13.50s), which is the stated latency criterion. Model B wins only on relevance/irrelevance (79.98 vs 73.61, ~6 points) and on cost ($4.64 vs $86.55), but cost is explicitly secondary and the relevance gap is smaller than A's memory and web-search advantages. If irrelevance handling were the dominant failure mode or budget were binding, Model B would be reconsidered.","cited_overall_rank":"none - provisional selection made before any BFCL overall rank was shown","cited_scenario_specific_evidence":["memory performance: 73.76 (A) vs 55.70 (B) - decisive for multi-step, multi-session agent workflows","web-search perform
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0136 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":72,"stated_reasoning":"Multi-turn completion is essentially tied (68.38 vs 68), but Model B leads decisively on the two agentic capabilities that drive multi-step workflow success: memory (73.76 vs 55.7, an 18-point gap) and web search (84.5 vs 77.5). Model B also halves P95 latency (7.56s vs 13.50s), which matters for interactive account-change and support flows. Model A's advantages are relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points) and much lower benchmark cost ($4.64 vs $86.55). Since cost is explicitly a secondary factor and the memory gap is the largest single differentiator for stateful multi-step orchestration, Model B is the provisional pick. The caveat: if the deployment involves high-volume automation where the ~19x cost multiple becomes material, or if false-triggering on irrelevant requests is a safety-critical concern in procurement/account changes, Model A becomes competitive.","cited_overall_rank":"none - provisional selection made before any general BFCL overall rank was shown","cited_scenario_specific_evidence":["memory performance 73.76 vs 55.7 favoring Model B, the largest gap and most relevant to multi-step state
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0137 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":74,"stated_reasoning":"The stated satisfaction criteria prioritize multi-turn completion, memory, web-search, relevance/irrelevance, and latency, with cost only secondary. Model B leads on multi-turn (68.38 vs 68.00, essentially tied), memory (73.76 vs 55.70, a large 18-point gap that matters directly for multi-step agent workflows requiring state across turns), and web-search (84.5 vs 77.5). It also has far better P95 latency (7.56s vs 13.50s), which is important for interactive enterprise agents. Model A wins only on relevance/irrelevance (79.98 vs 73.61, ~6.4 points) and on cost ($4.64 vs $86.55), but cost is explicitly secondary. Since Model B wins the majority of the primary criteria including the decisive memory and latency dimensions, it is the better fit; Model A would be the fallback if budget constraints were reprioritized to primary.","cited_overall_rank":"not_used_no_overall_rank_seen_at_provisional_stage","cited_scenario_specific_evidence":["memory: Model B 73.76 vs Model A 55.70 (+18.06), critical for multi-step account/procurement workflows carrying state","multi-turn: Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0138 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Primary criteria favor Model A: it leads on multi-turn (68.38 vs 68.00, essentially tied), memory (73.76 vs 55.70, an 18-point gap that matters most for multi-step agent workflows carrying account/procurement context across turns), and web-search (84.5 vs 77.5, relevant for technical support and knowledge-work lookups). Model A also has far better P95 latency (7.56s vs 13.50s), which is an explicit satisfaction condition for interactive agent workflows. Model B's only wins are relevance/irrelevance behavior (79.98 vs 73.61, ~6 points, meaning fewer spurious tool calls) and cost ($4.64 vs $86.55, ~19x cheaper) — but cost is explicitly secondary. The 18-point memory deficit plus nearly double the tail latency outweighs B's modest relevance edge for stateful, multi-step orchestration. Model B would become the pick if per-task volume were extreme enough to make the cost delta dominant, or if the workload were mostly single-shot with high need to suppress unnecessary tool invocation.","cited_overall_rank":"not_yet_seen_provisional_selection_made_before_bfcl_overall_rank","cited_sc
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0139 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":74,"stated_reasoning":"Primary criteria favor Model A: it leads on multi-turn (68.38 vs 68, essentially tied), memory (73.76 vs 55.70, an ~18-point gap that matters most for multi-step account/procurement/support workflows requiring state carryover), and web-search (84.5 vs 77.5, relevant for knowledge-work automation and technical support lookups). Model A also has substantially better P95 latency (7.56s vs 13.50s), which is an explicit satisfaction condition for interactive agent workflows. Model B's advantages are relevance/irrelevance behavior (79.98 vs 73.61, ~6 points) and cost ($4.64 vs $86.55), but cost is stated as a secondary factor, and the single relevance edge does not offset deficits across memory, search, and latency. If misfire/over-triggering on irrelevant requests were the dominant failure mode, or if budget were the binding constraint, Model B would become the better pick.","cited_overall_rank":"not_used_no_overall_rank_available_at_provisional_stage","cited_scenario_specific_evidence":"Memory 73.76 vs 55.70 (multi-step account changes and procurement need state retention); multi-turn 68.38 vs 68.00; web
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0140 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Model A leads on the capabilities that matter most for multi-step agent workflows: multi-turn completion (68.38 vs 68.0, effectively a tie), memory (73.76 vs 55.7, an 18-point advantage that is decisive for account changes, procurement, and orchestration that carry state across turns), and web-search (84.5 vs 77.5, important for technical support and knowledge-work automation). It also has substantially better tail latency (7.56s vs 13.50s P95), which directly affects usability in interactive agent loops. Model B's only edge is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points) and cost ($4.64 vs $86.55). Cost is explicitly secondary in the stated criteria, and while Model B's cheaper benchmark cost is a real consideration, the memory gap is a functional deficiency for stateful multi-step tasks rather than a tuning issue, whereas Model A's lower relevance score is a guardrail concern that can be partially mitigated with prompt/tooling constraints and confirmation steps. Selecting Model A, with the caveat that if the deployment mix is dominated by high-volume, cost-sensitive, largely single-turn traffic where false tool invocation is the main ris
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0141 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":76,"stated_reasoning":"Cost is explicitly secondary, so the decision rests on the agentic quality and latency dimensions. Model B leads on four of the five primary criteria: memory (73.76 vs 55.7, an ~18-point gap that matters most for multi-step account/procurement workflows that must carry state across turns), web-search (84.5 vs 77.5, relevant for technical support and knowledge-work automation), multi-turn completion (68.38 vs 68.0, effectively a tie but not a loss), and P95 latency (7.56s vs 13.50s, roughly half, which materially affects interactive agent orchestration). Model A's only real advantage on a primary axis is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which is a genuine risk for spurious tool invocation on out-of-scope requests, plus a large cost advantage ($4.64 vs $86.55). However, a modest relevance
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 91}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0142 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Satisfaction is defined primarily by multi-turn completion, memory, web-search, relevance/irrelevance behavior, and latency, with cost only secondary. Model A leads or ties on four of the five primary criteria: multi-turn (68.38 vs 68.0, essentially tied), memory (73.76 vs 55.7, a large 18-point advantage directly relevant to multi-step agent workflows like account changes and procurement that require carrying state across turns), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s, nearly half of Model B's). Model B's only advantage on a primary criterion is relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points), which matters for refusing out-of-scope requests but is outweighed by A's memory and latency edge for long-horizon orchestration. Model B is far cheaper ($4.64 vs $86.55), an ~18x difference, but cost is explicitly secondary here; that said, the cost gap is large enough that Model B would be the rational pick if budget were reclassified as a hard constraint or if the workload is high-volume, which is why confidence is moderate rather than high.","cited_overall_rank":"none — provisional selection made before any general
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0143 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Model A leads on the criteria weighted most heavily for multi-step agentic workflows: multi-turn completion (68.38 vs 68, essentially tied but nominally ahead), memory behavior (73.76 vs 55.7, an 18-point gap that matters greatly for long account-change and procurement flows), and web-search behavior (84.5 vs 77.5). It also has substantially better P95 latency (7.56s vs 13.50s), which is important for interactive support and orchestration. Model B's only advantages are relevance/irrelevance behavior (79.98 vs 73.61, a ~6.4-point edge that partially offsets by reducing spurious tool calls) and much lower benchmark cost ($4.64 vs $86.55). Since cost is explicitly a secondary factor and latency is a stated requirement, the memory plus search plus latency advantages outweigh the relevance gap and the cost premium. Confidence is moderated because the ~19x cost difference is large enough that a cost-sensitive enterprise deployment could reasonably reverse this, and because weaker irrelevance handling can cause unwanted actions in write-capable workflows like account changes and procurement.","cited_overall_rank":"none — provisional selection made before any BFCL overall rank
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0144 - Anthropic - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Model A leads on the criteria that matter most for multi-step agent workflows: multi-turn completion (68.38 vs 68.0, essentially tied but not worse), memory (73.76 vs 55.7, a large 18-point advantage that directly supports account changes, procurement, and orchestration across steps), and web-search (84.5 vs 77.5, relevant for technical support and knowledge-work automation). It also has markedly better latency (7.56s vs 13.50s P95), which is an explicit satisfaction condition. Model B's only edges are relevance/irrelevance behavior (79.98 vs 73.61, ~6.4 points) and cost ($4.64 vs $86.55). Cost is stated as a secondary factor, so the large price gap does not outweigh A's advantages in memory, search, and latency. The relevance/irrelevance gap is the main risk with A (more spurious tool calls on out-of-scope requests), which can be partially mitigated with prompt-level and policy guardrails.","cited_overall_rank":"not_used_provisional_selection_made_before_seeing_overall_rank","cited_scenario_specific_evidence":["memory: Model A 73.76 vs Model B 55.7 (+18.06) — critical for multi-step
```

- Returned model field present: True
- Usage: `{"completion_tokens": 450, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.01421, "cost_details": {"upstream_inference_completions_cost": 0.01125, "upstream_inference_cost": 0.01421, "upstream_inference_prompt_cost": 0.00296}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1042}`

## T0145 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
"final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0146 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0147 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
"final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 432}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0148 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0149 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0150 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0151 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0152 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0153 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0154 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0155 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0156 - Google - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.00489, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00489, "upstream_inference_prompt_cost": 0.00043}, "is_byok": false, "prompt_tokens": 344, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 790}`

## T0157 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0158 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0159 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0160 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0161 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0162 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0163 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0164 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
"final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0165 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0166 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0167 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
"final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0168 - Google - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0049275, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049275, "upstream_inference_prompt_cost": 0.0004675}, "is_byok": false, "prompt_tokens": 374, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 820}`

## T0169 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0170 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
"provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0171 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0172 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0173 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
"provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0174 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0175 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0176 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
"provisional_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0177 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
"provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0178 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0179 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
"provisional_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0180 - Google - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.0048925, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0048925, "upstream_inference_prompt_cost": 0.0004325}, "is_byok": false, "prompt_tokens": 346, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 792}`

## T0181 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0182 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0183 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0184 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0185 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0186 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0187 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0188 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0189 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0190 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model B
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0191 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0192 - Google - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.004965, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.004965, "upstream_inference_prompt_cost": 0.000505}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 850}`

## T0193 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0194 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0195 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0196 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0197 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0198 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0199 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0200 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
```json
{
  "final_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0201 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0202 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0203 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
  "final_choice_label": "Model A",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0204 - Google - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
```json
{
"final_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 429}, "cost": 0.00500125, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.00500125, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 879}`

## T0205 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0206 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
"provisional_choice_label": "Model A
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0207 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0208 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0209 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0210 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0211 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
"provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0212 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0213 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0214 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
```json
{
  "provisional_choice_label": "
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0215 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model B",
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0216 - Google - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
```json
{
  "provisional_choice_label": "Model
```

- Returned model field present: True
- Usage: `{"completion_tokens": 446, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 0.0049675, "cost_details": {"upstream_inference_completions_cost": 0.00446, "upstream_inference_cost": 0.0049675, "upstream_inference_prompt_cost": 0.0005075}, "is_byok": false, "prompt_tokens": 406, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 852}`

## T0217 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every satisfactory-performance criterion: marginally stronger single-turn tool calling, substantially better relevance/irrelevance, ~64x lower cost, and ~3x lower P95 latency, which matters under high call volume.","cited_overall_rank":"1","cited_scenario_specific_evidence":"single-turn 85.361>85.177; relevance 84.215>73.61; cost $1.35<<$86.55; P95 2.3s<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 479, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 343}, "cost": 0.0036904, "cost_details": {"upstream_inference_completions_cost": 0.002874, "upstream_inference_cost": 0.0036904, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 996}`

## T0218 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label": "A", "confidence_0_100": 92, "stated_reasoning": "Model A matches or beats B on every stated criterion: slightly higher single-turn score, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency. High volume and low-consequence errors make cost and tail latency decisive, so A is the clear fit.", "cited_overall_rank": "A>B", "cited_scenario_specific_evidence": "A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 407, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 247}, "cost": 0.0032584, "cost_details": {"upstream_inference_completions_cost": 0.002442, "upstream_inference_cost": 0.0032584, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 924}`

## T0219 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B matches or exceeds Model A on single-turn capability while substantially outperforming on relevance/irrelevance, benchmark cost, and P95 latency, aligning with high-volume low-consequence tool-calling needs.","cited_overall_rank":"B>A","cited_scenario_specific_evidence":"B single-turn 85.361>A 85.177; B relevance 84.215>A 73.61; B cost $1.35<A $86.55; B P95 2.3s<A 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 458, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 313}, "cost": 0.0035644, "cost_details": {"upstream_inference_completions_cost": 0.002748, "upstream_inference_cost": 0.0035644, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 975}`

## T0220 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated criterion: marginally higher single-turn score, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, which together best match high-volume low-consequence tool-calling needs.","cited_overall_rank":"Model A > Model B","cited_scenario_specific_evidence":"single-turn 85.361>85.177; relevance 84.215>73.61; cost $1.35<<$86.55; P95 2.3s<<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 463, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 322}, "cost": 0.0035944, "cost_details": {"upstream_inference_completions_cost": 0.002778, "upstream_inference_cost": 0.0035944, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 980}`

## T0221 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on all satisfactory criteria: marginally higher single-turn capability, substantially stronger relevance/irrelevance, ~64x lower cost, and ~3x lower P95 latency, matching high-volume low-stakes tool-calling needs.","cited_overall_rank":"B > A","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95; A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 437, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 287}, "cost": 0.0034384, "cost_details": {"upstream_inference_completions_cost": 0.002622, "upstream_inference_cost": 0.0034384, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 954}`

## T0222 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B matches or exceeds A on single-turn capability while substantially outperforming on relevance/irrelevance, benchmark cost, and P95 latency, aligning with high-volume low-consequence tool-calling needs.","cited_overall_rank":"B > A","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 relevance, $1.35 cost, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 439, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 299}, "cost": 0.0034504, "cost_details": {"upstream_inference_completions_cost": 0.002634, "upstream_inference_cost": 0.0034504, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 956}`

## T0223 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated criterion: marginally higher single-turn tool-call score, substantially stronger relevance/irrelevance behavior, dramatically lower benchmark cost, and much lower P95 latency, matching the high-volume low-consequence automation needs.","cited_overall_rank":"A > B","cited_scenario_specific_evidence":"single-turn 85.361>85.177; relevance 84.215>73.61; cost $1.35<<$86.55; P95 latency 2.3s<<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 375, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 234}, "cost": 0.0024136, "cost_details": {"upstream_inference_completions_cost": 0.00225, "upstream_inference_cost": 0.0024136, "upstream_inference_prompt_cost": 0.0001636}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 892}`

## T0224 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on all stated criteria: slightly higher single-turn tool-call score, substantially stronger relevance/irrelevance, ~64x lower cost, and ~3x lower P95 latency, matching high-volume low-consequence CRM/ticket automation needs.","cited_overall_rank":"B>A","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95; A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 406, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 253}, "cost": 0.0032524, "cost_details": {"upstream_inference_completions_cost": 0.002436, "upstream_inference_cost": 0.0032524, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 923}`

## T0225 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on every stated criterion: marginally higher single-turn tool-call score, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, matching the high-volume low-consequence automation needs.","cited_overall_rank":"A > B","cited_scenario_specific_evidence":"single-turn 85.361>85.177; relevance 84.215>73.61; cost $1.35<<$86.55; P95 latency 2.3s<<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 373, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 234}, "cost": 0.0024016, "cost_details": {"upstream_inference_completions_cost": 0.002238, "upstream_inference_cost": 0.0024016, "upstream_inference_prompt_cost": 0.0001636}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 890}`

## T0226 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on all satisfactory criteria: marginally higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, which matters under high call volume with low-consequence errors.","cited_overall_rank":"1","cited_scenario_specific_evidence":"single-turn 85.361>85.177; relevance 84.215>>73.61; cost $1.35<<$86.55; P95 2.3s<<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 459, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 325}, "cost": 0.0035704, "cost_details": {"upstream_inference_completions_cost": 0.002754, "upstream_inference_cost": 0.0035704, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 976}`

## T0227 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":95,"stated_reasoning":"Model B dominates on all stated criteria: slightly higher single-turn tool-call score, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, matching high-volume low-consequence CRM/ticket automation needs.","cited_overall_rank":"B > A","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 relevance, $1.35 cost, 2.3s P95; A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 404, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 257}, "cost": 0.0025876, "cost_details": {"upstream_inference_completions_cost": 0.002424, "upstream_inference_cost": 0.0025876, "upstream_inference_prompt_cost": 0.0001636}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 921}`

## T0228 - xAI - Profile A - EVIDENCE_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A dominates on all stated criteria: slightly higher single-turn score, substantially stronger relevance/irrelevance, ~64x lower cost, and much lower P95 latency, matching high-volume low-consequence needs.","cited_overall_rank":"A>>B","cited_scenario_specific_evidence":"A: 85.361 single-turn, 84.215 rel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 386, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 245}, "cost": 0.0031324, "cost_details": {"upstream_inference_completions_cost": 0.002316, "upstream_inference_cost": 0.0031324, "upstream_inference_prompt_cost": 0.0008164}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 903}`

## T0229 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes cost, tail latency, and reliable relevance alongside strong single-turn tool calling. B matches or beats A on single-turn while substantially excelling on relevance, cost, and P95 latency.","cited_overall_rank":"Model B: BFCL overall rank 47","cited_scenario_specific_evidence":"B: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 517, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 360}, "cost": 0.0039744, "cost_details": {"upstream_inference_completions_cost": 0.003102, "upstream_inference_cost": 0.0039744, "upstream_inference_prompt_cost": 0.0008724}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1062}`

## T0230 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence errors prioritizes cost, tail latency, relevance behavior, and solid single-turn tool calling; Model B dominates on all four scenario metrics despite weaker overall rank.","cited_overall_rank":"Model B: BFCL overall rank 47","cited_scenario_specific_evidence":"B: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 653, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 507}, "cost": 0.0041376, "cost_details": {"upstream_inference_completions_cost": 0.003918, "upstream_inference_cost": 0.0041376, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1198}`

## T0231 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":88,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes strong single-turn tool calling plus reliable relevance, low cost, and low tail latency. Model A matches or beats Model B on single-turn, substantially leads on relevance/irrelevance, and is far superior on cost and P95 latency, fitting the stated satisfactory-performance conditions better despite weaker overall rank.","cited_overall_rank":"Model A: BFCL overall rank 47","cited_scenario_specific_evidence":"Model A single-turn 85.361 > B 85.177; relevance 84.215 > B 73.61; cost $1.35 << B $86.55; P95 2.3s << B 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 569, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 389}, "cost": 0.0042864, "cost_details": {"upstream_inference_completions_cost": 0.003414, "upstream_inference_cost": 0.0042864, "upstream_inference_prompt_cost": 0.0008724}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1114}`

## T0232 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label": "Model A", "confidence_0_100": 88, "stated_reasoning": "High call volume with low-consequence reviewable errors prioritizes strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low P95 latency. Model A matches or exceeds on single-turn, substantially leads on relevance, and is far superior on cost and tail latency.", "cited_overall_rank": 47, "cited_scenario_specific_evidence": "single-turn 85.361 vs 85.177; relevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 latency 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 565, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 409}, "cost": 0.0042624, "cost_details": {"upstream_inference_completions_cost": 0.00339, "upstream_inference_cost": 0.0042624, "upstream_inference_prompt_cost": 0.0008724}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1110}`

## T0233 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low P95 latency. Model A matches or beats Model B on single-turn, substantially leads on relevance/irrelevance, and is far superior on cost and tail latency.","cited_overall_rank":"Model A: BFCL overall rank 47","cited_scenario_specific_evidence":"Model A single-turn 85.361 > B 85.177; relevance/irrelevance 84.215 > B 73.61; cost $1.35 << B $86.55; P95 latency 2.3s << B 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 529, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 355}, "cost": 0.0033936, "cost_details": {"upstream_inference_completions_cost": 0.003174, "upstream_inference_cost": 0.0033936, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1074}`

## T0234 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes strong single-turn capability, reliable relevance/irrelevance, low cost, and low P95 latency. Model A matches or exceeds on single-turn, substantially leads on relevance, and is far superior on cost and tail latency.","cited_overall_rank":"Model A: BFCL overall rank 47","cited_scenario_specific_evidence":"A: single-turn 85.361, relevance 84.215, cost $1.35, P95 2.3s vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 532, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 370}, "cost": 0.0040644, "cost_details": {"upstream_inference_completions_cost": 0.003192, "upstream_inference_cost": 0.0040644, "upstream_inference_prompt_cost": 0.0008724}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1077}`

## T0235 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes strong single-turn tool calling, reliable relevance behavior, low cost, and low tail latency. Model A matches or exceeds on single-turn while substantially beating on relevance, cost, and P95 latency.","cited_overall_rank":"Model A: BFCL overall rank 47","cited_scenario_specific_evidence":"Model A single-turn 85.361 > B 85.177; relevance 84.215 > B 73.61; cost $1.35 << B $86.55; P95 latency 2.3s << B 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 568, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 408}, "cost": 0.0042804, "cost_details": {"upstream_inference_completions_cost": 0.003408, "upstream_inference_cost": 0.0042804, "upstream_inference_prompt_cost": 0.0008724}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1113}`

## T0236 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low tail latency. Model A matches or exceeds on single-turn while substantially beating Model B on relevance, cost, and P95 latency.","cited_overall_rank":"47","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177; relevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 529, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 380}, "cost": 0.0033936, "cost_details": {"upstream_inference_completions_cost": 0.003174, "upstream_inference_cost": 0.0033936, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1074}`

## T0237 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes cost, tail latency, and reliable relevance alongside solid single-turn tool calling. B matches or exceeds A on single-turn while substantially beating A on relevance/irrelevance, cost, and P95 latency.","cited_overall_rank":"47","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177; relevance/irrelevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 469, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 321}, "cost": 0.0030336, "cost_details": {"upstream_inference_completions_cost": 0.002814, "upstream_inference_cost": 0.0030336, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1014}`

## T0238 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence reviewable errors prioritizes cost, tail latency, and reliable relevance alongside solid single-turn tool calling. Model B matches or exceeds A on single-turn, substantially outperforms on relevance/irrelevance, and is far cheaper with much lower P95 latency.","cited_overall_rank":"BFCL overall rank 47","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177; relevance/irrelevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 latency 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 500, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 342}, "cost": 0.0032196, "cost_details": {"upstream_inference_completions_cost": 0.003, "upstream_inference_cost": 0.0032196, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1045}`

## T0239 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence errors prioritizes cost, tail latency, and reliable relevance over marginal single-turn gains or overall rank. Model B dominates on relevance, cost, and P95 latency while matching single-turn capability.","cited_overall_rank":"BFCL overall rank 47","cited_scenario_specific_evidence":"single-turn 85.361 vs 85.177; relevance 84.215 vs 73.61; cost $1.35 vs $86.55; P95 latency 2.3s vs 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 547, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 402}, "cost": 0.0035016, "cost_details": {"upstream_inference_completions_cost": 0.003282, "upstream_inference_cost": 0.0035016, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1092}`

## T0240 - xAI - Profile A - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"High call volume with low-consequence errors prioritizes low cost, low P95 latency, strong single-turn tool calling, and reliable relevance/irrelevance. Model B dominates on cost, latency, and relevance while matching single-turn capability.","cited_overall_rank":"Model B: BFCL overall rank 47","cited_scenario_specific_evidence":"single-turn 85.361 > 85.177; relevance/irrelevance 84.215 >> 73.61; cost $1.35 << $86.55; P95 latency 2.3s << 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 577, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 424}, "cost": 0.0036816, "cost_details": {"upstream_inference_completions_cost": 0.003462, "upstream_inference_cost": 0.0036816, "upstream_inference_prompt_cost": 0.0002196}, "is_byok": false, "prompt_tokens": 545, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1122}`

## T0241 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A matches or beats Model B on every stated criterion: marginally higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower benchmark cost, and much lower P95 latency. High call volume and low-consequence errors further favor the cheaper, faster, more reliable option.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95; B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 482, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 326}, "cost": 0.0037124, "cost_details": {"upstream_inference_completions_cost": 0.002892, "upstream_inference_cost": 0.0037124, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1001}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning","Model A remains superior on every scenario-critical metric (single-turn, relevance/irrelevance, cost, P95 latency) for high-volume low-consequence CRM/ticket work; BFCL overall rank does not override the stated requirements.","cited_overall_rank":"B:1 A:47","cited_scenario_specific_evidence":"A:85.361 single-turn,84.215 rel/irrel,$1.35,2.3s P95 vs B:85.177,73.61,$86.55,7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 328, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 175}, "cost": 0.0026496, "cost_details": {"upstream_inference_completions_cost": 0.001968, "upstream_inference_cost": 0.0026496, "upstream_inference_prompt_cost": 0.0006816}, "is_byok": false, "prompt_tokens": 776, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1104}`

## T0242 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model B","confidence_0_100":92,"stated_reasoning":"Model B matches all satisfactory criteria better: nearly identical single-turn capability, substantially stronger relevance/irrelevance, ~64x lower cost, and ~3x lower P95 latency, which suits high-volume low-consequence CRM/ticket automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95; A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 543, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 393}, "cost": 0.0040784, "cost_details": {"upstream_inference_completions_cost": 0.003258, "upstream_inference_cost": 0.0040784, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1062}`

### final

```text
{"final_choice_label":"Model B","confidence_0_100":93,"stated_reasoning":"Scenario needs strong single-turn, reliable relevance/irrelevance, low cost, and low tail latency for high-volume low-consequence CRM/ticket work. B remains clearly superior on those metrics despite weaker overall BFCL rank; A’s rank-1 status does not offset its much worse relevance, ~64x cost, and higher P95 latency.","cited_overall_rank":"B:47 A:1","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95; A: 85.177, 73.61, $86.55, 7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 436, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 252}, "cost": 0.0039384, "cost_details": {"upstream_inference_completions_cost": 0.002616, "upstream_inference_cost": 0.0039384, "upstream_inference_prompt_cost": 0.0013224}, "is_byok": false, "prompt_tokens": 770, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1206}`

## T0243 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":92,"stated_reasoning":"B dominates on all stated criteria: slightly higher single-turn score, substantially better relevance/irrelevance, ~64x lower cost, and much lower P95 latency; ideal for high-volume low-consequence CRM/ticket automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 509, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 364}, "cost": 0.0038744, "cost_details": {"upstream_inference_completions_cost": 0.003054, "upstream_inference_cost": 0.0038744, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1028}`

### final

```text
{"final_choice_label":"B","confidence_0_100":93,"stated_reasoning","Overall BFCL rank favors A but the stated requirements prioritize single-turn tool calling, relevance/irrelevance, low cost, and low P95 latency; B remains strictly superior on those scenario metrics for high-volume low-consequence CRM/ticket work.","cited_overall_rank":"B:47 A:1","cited_scenario_specific_evidence":"B:85.361 single-turn,84.215 rel/irrel,$1.35,2.3s P95 vs A:85.177,73.61,$86.55,7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 383, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 229}, "cost": 0.0036104, "cost_details": {"upstream_inference_completions_cost": 0.002298, "upstream_inference_cost": 0.0036104, "upstream_inference_prompt_cost": 0.0013124}, "is_byok": false, "prompt_tokens": 765, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1148}`

## T0244 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "Model B", "confidence_0_100": 92, "stated_reasoning": "Model B dominates on all stated criteria: marginally higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, matching high-volume low-consequence CRM/ticket needs.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 497, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 350}, "cost": 0.0031496, "cost_details": {"upstream_inference_completions_cost": 0.002982, "upstream_inference_cost": 0.0031496, "upstream_inference_prompt_cost": 0.0001676}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1016}`

### final

```text
{"final_choice_label": "Model B", "confidence_0_100": 93, "stated_reasoning": "Scenario requires strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low tail latency for high-volume low-consequence CRM/ticket work. Model B remains superior on every stated criterion despite weaker general BFCL rank; overall rank does not override the matching metrics.", "cited_overall_rank": "B:47 A:1", "cited_scenario_specific_evidence": "B: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 397, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 221}, "cost": 0.0036984, "cost_details": {"upstream_inference_completions_cost": 0.002382, "upstream_inference_cost": 0.0036984, "upstream_inference_prompt_cost": 0.0013164}, "is_byok": false, "prompt_tokens": 767, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1164}`

## T0245 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":95,"stated_reasoning":"Model B matches or beats A on every stated criterion: slightly higher single-turn score, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency. These align directly with high-volume, low-consequence CRM/ticket automation needs.","cited_overall_rank":null,"cited_scenario_specific_evidence":"single-turn 85.361>85.177; rel/irrel 84.215>73.61; cost $1.35<<$86.55; P95 2.3s<<7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 418, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 274}, "cost": 0.0033284, "cost_details": {"upstream_inference_completions_cost": 0.002508, "upstream_inference_cost": 0.0033284, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 937}`

### final

```text
{"final_choice_label":"B","confidence_0_100":95,"stated_reasoning":"Scenario requires strong single-turn tool use, reliable relevance/irrelevance, low cost, and low P95 latency for high-volume low-consequence CRM/ticket work. B dominates on all four measured criteria despite much weaker general BFCL rank; overall rank is not a substitute for the stated requirements.","cited_overall_rank":"A:1 B:47","cited_scenario_specific_evidence":"single-turn 85.361>85.177; rel/irrel 84.215>73.61; cost $1.35<<$86.55; P95 2.3s<<7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 300, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 133}, "cost": 0.0024576, "cost_details": {"upstream_inference_completions_cost": 0.0018, "upstream_inference_cost": 0.0024576, "upstream_inference_prompt_cost": 0.0006576}, "is_byok": false, "prompt_tokens": 764, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1064}`

## T0246 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":95,"stated_reasoning":"Model B matches or exceeds A on single-turn capability while substantially outperforming on relevance/irrelevance, benchmark cost, and P95 latency, aligning with high-volume low-consequence CRM/ticket needs.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 471, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 331}, "cost": 0.0029936, "cost_details": {"upstream_inference_completions_cost": 0.002826, "upstream_inference_cost": 0.0029936, "upstream_inference_prompt_cost": 0.0001676}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 990}`

### final

```text
{"final_choice_label":"B","confidence_0_100":95,"stated_reasoning":"Scenario requires strong single-turn, reliable rel/irrel, low cost, and low P95 latency for high-volume low-consequence work; B dominates on the latter three and matches on single-turn, so overall BFCL rank is deprioritized.","cited_overall_rank":"A:1 B:47","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 305, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 139}, "cost": 0.0024796, "cost_details": {"upstream_inference_completions_cost": 0.00183, "upstream_inference_cost": 0.0024796, "upstream_inference_prompt_cost": 0.0006496}, "is_byok": false, "prompt_tokens": 760, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1065}`

## T0247 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "Model A", "confidence_0_100": 95, "stated_reasoning": "Model A matches or beats Model B on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower benchmark cost, and much lower P95 latency. High volume and low-consequence setting further favor the large cost and latency advantages.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "A: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 482, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 325}, "cost": 0.0037124, "cost_details": {"upstream_inference_completions_cost": 0.002892, "upstream_inference_cost": 0.0037124, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1001}`

### final

```text
{"final_choice_label": "Model A", "confidence_0_100": 93, "stated_reasoning": "Scenario requires strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low tail latency for high-volume low-consequence CRM/ticket work. Model A dominates on all four stated metrics despite much weaker general BFCL rank; overall rank is not scenario-specific and does not override the direct evidence.", "cited_overall_rank": "B:1 A:47", "cited_scenario_specific_evidence": "A: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs B: 85.177, 73.61, $86.55, 7.56s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 383, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 199}, "cost": 0.0029816, "cost_details": {"upstream_inference_completions_cost": 0.002298, "upstream_inference_cost": 0.0029816, "upstream_inference_prompt_cost": 0.0006836}, "is_byok": false, "prompt_tokens": 777, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1160}`

## T0248 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":92,"stated_reasoning":"Model A matches all satisfactory criteria better: slightly higher single-turn capability, substantially stronger relevance/irrelevance, far lower cost, and much lower P95 latency; ideal for high-volume low-consequence CRM/ticket automations.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 487, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 342}, "cost": 0.0037424, "cost_details": {"upstream_inference_completions_cost": 0.002922, "upstream_inference_cost": 0.0037424, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1006}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":93,"stated_reasoning":"Despite B holding BFCL overall rank 1, scenario requirements prioritize single-turn capability, relevance/irrelevance, low cost, and low P95 latency; A remains superior on all four while B is far costlier, slower, and weaker on relevance.","cited_overall_rank":"A:47 B:1","cited_scenario_specific_evidence":"A:85.361 single-turn,84.215 rel/irrel,$1.35,2.3s P95 vs B:85.177,73.61,$86.55,7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 397, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 240}, "cost": 0.0030416, "cost_details": {"upstream_inference_completions_cost": 0.002382, "upstream_inference_cost": 0.0030416, "upstream_inference_prompt_cost": 0.0006596}, "is_byok": false, "prompt_tokens": 765, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1162}`

## T0249 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label": "A", "confidence_0_100": 92, "stated_reasoning": "Model A matches all satisfactory criteria better: nearly identical single-turn capability, substantially stronger relevance/irrelevance, ~64x lower cost, and ~3x lower P95 latency. High call volume and reviewable low-consequence errors favor A's cost and tail-latency advantages.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95; B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 494, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 337}, "cost": 0.0037844, "cost_details": {"upstream_inference_completions_cost": 0.002964, "upstream_inference_cost": 0.0037844, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1013}`

### final

```text
{"final_choice_label": "A", "confidence_0_100": 93, "stated_reasoning": "Scenario needs strong single-turn tool use, reliable relevance, low cost, and low P95 latency at high volume. A remains clearly superior on those metrics despite B’s better BFCL overall rank; overall rank is deprioritized relative to stated satisfactory-performance conditions.", "cited_overall_rank": "A:47 B:1", "cited_scenario_specific_evidence": "A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 375, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 204}, "cost": 0.0035864, "cost_details": {"upstream_inference_completions_cost": 0.00225, "upstream_inference_cost": 0.0035864, "upstream_inference_prompt_cost": 0.0013364}, "is_byok": false, "prompt_tokens": 777, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1152}`

## T0250 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label": "A", "confidence_0_100": 92, "stated_reasoning": "Model A matches all satisfactory criteria better: marginally higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower cost, and much lower P95 latency, which matters under high call volume with low-consequence errors.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 473, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 327}, "cost": 0.0036584, "cost_details": {"upstream_inference_completions_cost": 0.002838, "upstream_inference_cost": 0.0036584, "upstream_inference_prompt_cost": 0.0008204}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 992}`

### final

```text
{"final_choice_label": "A", "confidence_0_100": 93, "stated_reasoning": "Scenario requires strong single-turn tool use, reliable relevance/irrelevance, low cost, and low tail latency under high volume. A dominates on all four measured criteria despite weaker general BFCL rank; B’s rank-1 overall score does not offset its large gaps in relevance, cost, and P95 latency.", "cited_overall_rank": "A:47 B:1", "cited_scenario_specific_evidence": "A: 85.361 single-turn, 84.215 rel/irrel, $1.35, 2.3s P95 vs B: 85.177, 73.61, $86.55, 7.56s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 340, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 160}, "cost": 0.0033544, "cost_details": {"upstream_inference_completions_cost": 0.00204, "upstream_inference_cost": 0.0033544, "upstream_inference_prompt_cost": 0.0013144}, "is_byok": false, "prompt_tokens": 766, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1106}`

## T0251 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "Qwen3-14B (Prompt)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":92,"stated_reasoning":"Model B matches or beats A on single-turn capability while substantially outperforming on relevance/irrelevance, benchmark cost, and P95 latency; these align directly with high-volume low-consequence CRM/ticket automation needs.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 476, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 333}, "cost": 0.0030236, "cost_details": {"upstream_inference_completions_cost": 0.002856, "upstream_inference_cost": 0.0030236, "upstream_inference_prompt_cost": 0.0001676}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 995}`

### final

```text
{"final_choice_label":"B","confidence_0_100":93,"stated_reasoning":"Scenario requires strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low tail latency for high-volume low-consequence CRM/ticket work. B is equal/better on single-turn and clearly superior on relevance, cost, and P95 latency; overall BFCL rank does not override these scenario-specific metrics.","cited_overall_rank":"A:1 B:47","cited_scenario_specific_evidence":"B: 85.361 single-turn, 84.215 rel/irrel, $1.35 cost, 2.3s P95 vs A: 85.177, 73.61, $86.55, 7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 421, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 239}, "cost": 0.0031816, "cost_details": {"upstream_inference_completions_cost": 0.002526, "upstream_inference_cost": 0.0031816, "upstream_inference_prompt_cost": 0.0006556}, "is_byok": false, "prompt_tokens": 763, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1184}`

## T0252 - xAI - Profile A - MRI_ORDER

- Label mapping: {"Model A": "Qwen3-14B (Prompt)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":95,"stated_reasoning":"Model A matches or beats Model B on every stated criterion: slightly higher single-turn capability, substantially stronger relevance/irrelevance, dramatically lower benchmark cost, and much lower P95 latency. High volume and low-consequence setting further favor the large cost and latency advantages.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs B: 85.177, 73.61, $86.55, 7.56s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 480, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 327}, "cost": 0.0030476, "cost_details": {"upstream_inference_completions_cost": 0.00288, "upstream_inference_cost": 0.0030476, "upstream_inference_prompt_cost": 0.0001676}, "is_byok": false, "prompt_tokens": 519, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 999}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":92,"stated_reasoning","Scenario requires strong single-turn tool calling, reliable relevance/irrelevance, low cost, and low tail latency for high-volume low-consequence CRM/ticket work. Model A dominates those measured criteria despite much weaker general BFCL rank; overall rank is not scenario-specific and does not outweigh the large cost/latency/relevance gaps.","cited_overall_rank":"A:47 B:1","cited_scenario_specific_evidence":"A: single-turn 85.361, rel/irrel 84.215, cost $1.35, P95 2.3s vs B: 85.177, 73.61, $86.55, 7.56s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 418, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 237}, "cost": 0.0038364, "cost_details": {"upstream_inference_completions_cost": 0.002508, "upstream_inference_cost": 0.0038364, "upstream_inference_prompt_cost": 0.0013284}, "is_byok": false, "prompt_tokens": 773, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1191}`

## T0253 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label": "A", "confidence_0_100": 72, "stated_reasoning": "Prioritizing strong multi-turn, memory, web-search for agent workflows; A leads on memory/web-search/multi-turn/latency while cost is secondary and relevance gap is modest.", "cited_overall_rank": "A>B", "cited_scenario_specific_evidence": "A memory 73.76>B 55.7; A web-search 84.5>B 77.5; multi-turn nearly tied A 68.38~B 68; A latency 7.56s better; B relevance/cost edge secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 576, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 428}, "cost": 0.0043784, "cost_details": {"upstream_inference_completions_cost": 0.003456, "upstream_inference_cost": 0.0043784, "upstream_inference_prompt_cost": 0.0009224}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1146}`

## T0254 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Primary needs favor multi-turn, memory, web-search, and latency; B leads on memory, web-search, multi-turn (slight), and latency while cost is secondary and relevance gap is modest.","cited_overall_rank":"B>A on core agent factors","cited_scenario_specific_evidence":"B memory 73.76>55.7, web-search 84.5>77.5, multi-turn 68.38~68, P95 7.56s<13.50s; A relevance 79.98>73.61 and much lower cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 507, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 353}, "cost": 0.0039644, "cost_details": {"upstream_inference_completions_cost": 0.003042, "upstream_inference_cost": 0.0039644, "upstream_inference_prompt_cost": 0.0009224}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1077}`

## T0255 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"A","confidence_0_100":72,"stated_reasoning":"A leads on multi-turn, memory, web-search and latency which are core for multi-step agents; relevance is acceptable and cost is secondary.","cited_overall_rank":"A>B","cited_scenario_specific_evidence":"multi-turn 68.38>68; memory 73.76>>55.7; web-search 84.5>77.5; latency 7.56s<13.50s; relevance close; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 541, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 416}, "cost": 0.0041684, "cost_details": {"upstream_inference_completions_cost": 0.003246, "upstream_inference_cost": 0.0041684, "upstream_inference_prompt_cost": 0.0009224}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1111}`

## T0256 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary criteria favor A: near-equal multi-turn, substantially stronger memory and web-search, better latency; relevance edge and much lower cost for B are secondary for multi-step agent workflows.","cited_overall_rank":"A>B","cited_scenario_specific_evidence":"multi-turn 68.38>68; memory 73.76>>55.7; web-search 84.5>77.5; latency 7.56s<13.50s; relevance 73.61<79.98 and cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 651, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 509}, "cost": 0.0048284, "cost_details": {"upstream_inference_completions_cost": 0.003906, "upstream_inference_cost": 0.0048284, "upstream_inference_prompt_cost": 0.0009224}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1221}`

## T0257 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label": "A", "confidence_0_100": 72, "stated_reasoning": "A leads on memory, web-search, multi-turn (near-tie), and latency which are core to multi-step agent workflows; relevance is acceptable and cost is secondary.", "cited_overall_rank": "A>B", "cited_scenario_specific_evidence": "memory 73.76>55.7; web-search 84.5>77.5; multi-turn 68.38~68; latency 7.56s<13.50s; relevance 73.61 vs 79.98; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 607, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 464}, "cost": 0.0039116, "cost_details": {"upstream_inference_completions_cost": 0.003642, "upstream_inference_cost": 0.0039116, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1177}`

## T0258 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Enterprise multi-step agent workflows need solid multi-turn completion, memory, web search, and relevance filtering at acceptable cost and latency. Model A and B are nearly tied on multi-turn (~68). B is stronger on memory and web-search and has lower latency, but is far more expensive (~19x) and weaker on relevance/irrelevance. Cost is secondary but still material at this gap; A’s better relevance behavior and much lower cost make it the more practical production choice while remaining competitive on core agent metrics.","cited_overall_rank":"A","cited_scenario_specific_evidence":"multi-turn nearly equal (68 vs 68.38); A better relevance/irrelevance (79.98 vs 73.61) and much lower cost ($4.64 vs $86.55); B better memory (73.76 vs 55.7), web-search (84.5 vs 77.5), and P95 latency (7.56s vs 13.50s)"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 235, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0023324, "cost_details": {"upstream_inference_completions_cost": 0.00141, "upstream_inference_cost": 0.0023324, "upstream_inference_prompt_cost": 0.0009224}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 805}`

## T0259 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A leads on the primary agentic axes of multi-turn, memory, and web-search while delivering lower latency; cost is secondary and relevance gap is modest.","cited_overall_rank":"A>B","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; latency 7.56s vs 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 570, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 440}, "cost": 0.0036896, "cost_details": {"upstream_inference_completions_cost": 0.00342, "upstream_inference_cost": 0.0036896, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1140}`

## T0260 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"B","confidence_0_100":78,"stated_reasoning":"Primary needs favor multi-turn, memory, web-search and acceptable latency for agent workflows; B leads on memory, web-search, multi-turn and latency while cost is secondary despite A advantage on relevance and price.","cited_overall_rank":"B>A","cited_scenario_specific_evidence":"B memory 73.76>A 55.7; B web-search 84.5>A 77.5; B multi-turn 68.38>A 68; B P95 7.56s<A 13.50s; A relevance 79.98>B 73.61 and A cost $4.64<<B $86.55 but cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 653, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 478}, "cost": 0.0041876, "cost_details": {"upstream_inference_completions_cost": 0.003918, "upstream_inference_cost": 0.0041876, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1223}`

## T0261 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary needs favor multi-turn, memory, web-search and acceptable latency; A leads on multi-turn, memory, web-search and latency while relevance gap is modest and cost is secondary.","cited_overall_rank":"A>B on core agent metrics","cited_scenario_specific_evidence":"A multi-turn 68.38>68, memory 73.76>>55.7, web-search 84.5>77.5, latency 7.56s<13.50s; B only better on relevance 79.98>73.61 and cost"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 647, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 497}, "cost": 0.0041516, "cost_details": {"upstream_inference_completions_cost": 0.003882, "upstream_inference_cost": 0.0041516, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1217}`

## T0262 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary needs favor multi-turn, memory, web-search and latency; A leads on memory, web-search and latency with near-parity multi-turn while cost is secondary.","cited_overall_rank":"A>B","cited_scenario_specific_evidence":"A memory 73.76>B 55.7; A web-search 84.5>B 77.5; A multi-turn 68.38~B 68; A P95 7.56s<B 13.50s; relevance secondary to those agent factors"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 635, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 489}, "cost": 0.0040796, "cost_details": {"upstream_inference_completions_cost": 0.00381, "upstream_inference_cost": 0.0040796, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1205}`

## T0263 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Primary agent needs favor B: near-equal multi-turn, substantially stronger memory and web-search, better latency; relevance is acceptable and cost is secondary.","cited_overall_rank":"B>A","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; relevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; cost secondary ($86.55 vs $4.64)"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 588, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 437}, "cost": 0.0037976, "cost_details": {"upstream_inference_completions_cost": 0.003528, "upstream_inference_cost": 0.0037976, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1158}`

## T0264 - xAI - Profile C - EVIDENCE_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Primary needs favor multi-turn, memory, web-search, and latency; B leads on memory, web-search, multi-turn (slight), and P95 latency while cost is secondary. A wins only on relevance and much lower cost.","cited_overall_rank":"B>A","cited_scenario_specific_evidence":"B: multi-turn 68.38, memory 73.76, web-search 84.5, latency 7.56s vs A: 68, 55.7, 77.5, 13.50s; relevance A 79.98>B 73.61; cost secondary A $4.64 vs B $86.55"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 549, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 375}, "cost": 0.0035636, "cost_details": {"upstream_inference_completions_cost": 0.003294, "upstream_inference_cost": 0.0035636, "upstream_inference_prompt_cost": 0.0002696}, "is_byok": false, "prompt_tokens": 570, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1119}`

## T0265 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Multi-step agent workflows prioritize multi-turn completion, memory, and web-search; Model B leads on those plus lower latency while cost is secondary.","cited_overall_rank":"BFCL overall rank 1","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; P95 latency 7.56s vs 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 533, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 404}, "cost": 0.0041764, "cost_details": {"upstream_inference_completions_cost": 0.003198, "upstream_inference_cost": 0.0041764, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1131}`

## T0266 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Multi-step agent workflows prioritize multi-turn, memory, and web-search. B edges multi-turn (68.38 vs 68), substantially leads memory (73.76 vs 55.7) and web-search (84.5 vs 77.5), has better P95 latency (7.56s vs 13.50s), and holds top overall rank; relevance is only modestly lower and cost is secondary.","cited_overall_rank":"Model B: BFCL overall rank 1","cited_scenario_specific_evidence":"B multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s vs A multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, P95 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 700, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 483}, "cost": 0.0051784, "cost_details": {"upstream_inference_completions_cost": 0.0042, "upstream_inference_cost": 0.0051784, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1298}`

## T0267 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":82,"stated_reasoning":"Multi-step agent workflows prioritize multi-turn completion, memory, and web-search; Model A leads on those plus lower latency while cost is secondary, outweighing Model B's relevance and cost edges.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"A multi-turn 68.38 vs B 68; A memory 73.76 vs B 55.7; A web-search 84.5 vs B 77.5; A P95 latency 7.56s vs B 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 554, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 401}, "cost": 0.0043024, "cost_details": {"upstream_inference_completions_cost": 0.003324, "upstream_inference_cost": 0.0043024, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1152}`

## T0268 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Model B edges multi-turn and substantially leads memory and web-search, which are core to multi-step agent workflows, while also offering lower P95 latency; relevance is modestly lower and cost is higher but cost is secondary.","cited_overall_rank":"Model B: BFCL overall rank 1","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; relevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; cost secondary ($86.55 vs $4.64)"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 583, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 411}, "cost": 0.0044764, "cost_details": {"upstream_inference_completions_cost": 0.003498, "upstream_inference_cost": 0.0044764, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1181}`

## T0269 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"B","confidence_0_100":78,"stated_reasoning":"For multi-step enterprise agent workflows, multi-turn completion, memory, and web-search behavior are primary. Model B is slightly better on multi-turn, substantially stronger on memory and web-search, and has lower P95 latency. Model A is cheaper and somewhat better on relevance/irrelevance, but cost is secondary and the agent-critical gaps favor B.","cited_overall_rank":"Model B: BFCL overall rank 1","cited_scenario_specific_evidence":"B multi-turn 68.38 vs A 68; B memory 73.76 vs A 55.7; B web-search 84.5 vs A 77.5; B P95 latency 7.56s vs A 13.50s; A relevance 79.98 vs B 73.61 and A cost $4.64 vs B $86.55 (secondary)."}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 212, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0022504, "cost_details": {"upstream_inference_completions_cost": 0.001272, "upstream_inference_cost": 0.0022504, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 810}`

## T0270 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Primary needs are multi-turn, memory, and web-search for multi-step agents; A leads on those plus lower latency while cost is secondary.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"A multi-turn 68.38 > B 68; A memory 73.76 >> B 55.7; A web-search 84.5 > B 77.5; A P95 latency 7.56s < B 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 511, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 368}, "cost": 0.0038268, "cost_details": {"upstream_inference_completions_cost": 0.003066, "upstream_inference_cost": 0.0038268, "upstream_inference_prompt_cost": 0.0007608}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 256, "video_tokens": 0}, "total_tokens": 1109}`

## T0271 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model B","confidence_0_100":78,"stated_reasoning":"Model B leads on the primary agentic criteria (multi-turn, memory, web-search) and has lower latency; cost is secondary and relevance gap is modest.","cited_overall_rank":"Model B: BFCL overall rank 1","cited_scenario_specific_evidence":"B multi-turn 68.38 > A 68; B memory 73.76 >> A 55.7; B web-search 84.5 > A 77.5; B P95 latency 7.56s < A 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 550, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 404}, "cost": 0.0042784, "cost_details": {"upstream_inference_completions_cost": 0.0033, "upstream_inference_cost": 0.0042784, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1148}`

## T0272 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A edges multi-turn and leads substantially on memory and web-search, with better P95 latency; these matter most for multi-step agent workflows. Relevance is weaker and cost much higher, but cost is secondary and overall profile still favors A.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, cost $86.55, P95 7.56s vs B: 68, 55.7, 77.5, 79.98, $4.64, 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 608, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0046264, "cost_details": {"upstream_inference_completions_cost": 0.003648, "upstream_inference_cost": 0.0046264, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1206}`

## T0273 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A edges multi-turn and leads substantially on memory and web-search, which are core to multi-step agent workflows, while also delivering lower P95 latency; relevance is acceptable and cost is secondary.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"A multi-turn 68.38 vs B 68; A memory 73.76 vs B 55.7; A web-search 84.5 vs B 77.5; A latency 7.56s vs B 13.50s; cost secondary despite A $86.55 vs B $4.64; B relevance 79.98 vs A 73.61"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 652, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 475}, "cost": 0.0046728, "cost_details": {"upstream_inference_completions_cost": 0.003912, "upstream_inference_cost": 0.0046728, "upstream_inference_prompt_cost": 0.0007608}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 256, "video_tokens": 0}, "total_tokens": 1250}`

## T0274 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### final

```text
{"final_choice_label":"B","confidence_0_100":78,"stated_reasoning":"Model B edges multi-turn and substantially leads memory and web-search, which are core to multi-step agent workflows; better P95 latency; cost is secondary despite being higher; relevance is acceptable though slightly lower.","cited_overall_rank":"Model B: BFCL overall rank 1","cited_scenario_specific_evidence":"B multi-turn 68.38 vs A 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; relevance 73.61 vs 79.98; latency 7.56s vs 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 582, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 424}, "cost": 0.0038176, "cost_details": {"upstream_inference_completions_cost": 0.003492, "upstream_inference_cost": 0.0038176, "upstream_inference_prompt_cost": 0.0003256}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1180}`

## T0275 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A edges multi-turn and clearly leads on memory and web-search, which are central to multi-step agent workflows, while also delivering lower P95 latency; relevance is acceptable and cost is secondary.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; latency 7.56s vs 13.50s; cost secondary despite $86.55 vs $4.64"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 585, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0038356, "cost_details": {"upstream_inference_completions_cost": 0.00351, "upstream_inference_cost": 0.0038356, "upstream_inference_prompt_cost": 0.0003256}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1183}`

## T0276 - xAI - Profile C - CENTER_FIRST

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### final

```text
{"final_choice_label":"Model A","confidence_0_100":78,"stated_reasoning":"Model A leads on the primary agent needs of multi-turn, memory, and web-search while delivering lower latency; cost is secondary and relevance gap is modest.","cited_overall_rank":"Model A: BFCL overall rank 1","cited_scenario_specific_evidence":"A multi-turn 68.38 > B 68; A memory 73.76 > B 55.7; A web-search 84.5 > B 77.5; A P95 latency 7.56s < B 13.50s; cost secondary despite A $86.55 > B $4.64; relevance A 73.61 < B 79.98 still acceptable"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 567, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 395}, "cost": 0.0043804, "cost_details": {"upstream_inference_completions_cost": 0.003402, "upstream_inference_cost": 0.0043804, "upstream_inference_prompt_cost": 0.0009784}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1165}`

## T0277 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"A","confidence_0_100":72,"stated_reasoning":"For multi-step agent workflows, prioritize multi-turn, memory, and web-search. A edges multi-turn (68.38 vs 68), substantially leads memory (73.76 vs 55.7) and web-search (84.5 vs 77.5), and has better latency (7.56s vs 13.50s). Relevance is slightly weaker and cost much higher, but cost is secondary and latency/memory gains fit agent needs.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, cost 86.55, P95 7.56s; B: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, cost 4.64, P95 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 656, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 431}, "cost": 0.0048624, "cost_details": {"upstream_inference_completions_cost": 0.003936, "upstream_inference_cost": 0.0048624, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1228}`

### final

```text
{"final_choice_label":"A","confidence_0_100":85,"stated_reasoning":"Scenario needs strong multi-turn, memory, and web-search for agent workflows. A leads or matches on those (multi-turn 68.38, memory 73.76, web-search 84.5), has lower latency, and holds BFCL overall rank 1 vs B rank 4. Cost is higher but secondary; relevance gap is modest.","cited_overall_rank":"A:1; B:4","cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, cost 86.55, P95 7.56s; B: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, cost 4.64, P95 13.50s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 323, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 104}, "cost": 0.0035164, "cost_details": {"upstream_inference_completions_cost": 0.001938, "upstream_inference_cost": 0.0035164, "upstream_inference_prompt_cost": 0.0015784}, "is_byok": false, "prompt_tokens": 898, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1221}`

## T0278 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":68,"stated_reasoning":"B leads on memory, web-search, multi-turn (narrowly), and latency, which are primary for multi-step agent workflows; relevance is lower but still usable and cost is secondary.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: multi-turn 68.38, memory 73.76, web-search 84.5, P95 7.56s vs A: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost $4.64, P95 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 647, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 488}, "cost": 0.0048084, "cost_details": {"upstream_inference_completions_cost": 0.003882, "upstream_inference_cost": 0.0048084, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1219}`

### final

```text
{"final_choice_label":"B","confidence_0_100":82,"stated_reasoning":"B remains preferred for multi-step agents: stronger memory, web-search, slight multi-turn edge, and lower latency; BFCL rank 1 reinforces this over A despite A's better relevance and much lower cost (secondary).","cited_overall_rank":"B:1; A:4","cited_scenario_specific_evidence":"B: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, cost $86.55, P95 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost $4.64, P95 13.50s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 325, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 129}, "cost": 0.0033964, "cost_details": {"upstream_inference_completions_cost": 0.00195, "upstream_inference_cost": 0.0033964, "upstream_inference_prompt_cost": 0.0014464}, "is_byok": false, "prompt_tokens": 832, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1157}`

## T0279 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "B", "confidence_0_100": 72, "stated_reasoning": "Multi-step agent workflows prioritize multi-turn completion, memory, and web-search. B leads clearly on memory (73.76 vs 55.7), web-search (84.5 vs 77.5), and latency (7.56s vs 13.50s), with nearly identical multi-turn scores; relevance is slightly weaker but cost is secondary.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, latency 7.56s, cost $86.55; A: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, latency 13.50s, cost $4.64"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 735, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 528}, "cost": 0.0053364, "cost_details": {"upstream_inference_completions_cost": 0.00441, "upstream_inference_cost": 0.0053364, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1307}`

### final

```text
{"final_choice_label": "B", "confidence_0_100": 85, "stated_reasoning": "B remains best for multi-step agent workflows: superior memory, web-search, multi-turn parity, and much lower latency. BFCL overall rank 1 vs 4 reinforces the provisional choice; cost stays secondary.", "cited_overall_rank": "B:1, A:4", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, latency 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, latency 13.50s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 180, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0026224, "cost_details": {"upstream_inference_completions_cost": 0.00108, "upstream_inference_cost": 0.0026224, "upstream_inference_prompt_cost": 0.0015424}, "is_byok": false, "prompt_tokens": 880, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1060}`

## T0280 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label": "A", "confidence_0_100": 72, "stated_reasoning": "A leads on multi-turn, memory, web-search and latency, which are primary for multi-step agent workflows; B only edges relevance and is far cheaper but cost is secondary and its memory gap is large.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "A: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, cost 86.55, p95 7.56s; B: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost 4.64, p95 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 529, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 347}, "cost": 0.0041004, "cost_details": {"upstream_inference_completions_cost": 0.003174, "upstream_inference_cost": 0.0041004, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1101}`

### final

```text
{"final_choice_label":"A","confidence_0_100":85,"stated_reasoning":"A remains stronger on multi-turn, memory, web-search and latency for agent workflows; BFCL rank 1 vs 4 reinforces the provisional choice while cost stays secondary.","cited_overall_rank":"A:1, B:4","cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, cost 86.55, p95 7.56s; B: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost 4.64, p95 13.50s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 309, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 123}, "cost": 0.0026936, "cost_details": {"upstream_inference_completions_cost": 0.001854, "upstream_inference_cost": 0.0026936, "upstream_inference_prompt_cost": 0.0008396}, "is_byok": false, "prompt_tokens": 855, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1164}`

## T0281 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"A","confidence_0_100":72,"stated_reasoning":"Primary agent needs favor memory, multi-turn, and web-search; A leads substantially on memory and web-search with near-tie multi-turn and better latency. Relevance is modestly lower and cost far higher, but cost is secondary and latency remains acceptable.","cited_overall_rank":null,"cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, P95 7.56s, cost $86.55; B: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, P95 13.50s, cost $4.64"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 752, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 564}, "cost": 0.0054384, "cost_details": {"upstream_inference_completions_cost": 0.004512, "upstream_inference_cost": 0.0054384, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1324}`

### final

```text
{"final_choice_label":"A","confidence_0_100":80,"stated_reasoning","A leads on memory, web-search, multi-turn, and latency for agent workflows; overall rank 1 supports keeping A despite higher cost and slightly weaker relevance, with cost secondary.","cited_overall_rank":"A:1; B:4","cited_scenario_specific_evidence":"A: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, P95 7.56s, cost $86.55; B: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, P95 13.50s, cost $4.64","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 411, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 224}, "cost": 0.0039704, "cost_details": {"upstream_inference_completions_cost": 0.002466, "upstream_inference_cost": 0.0039704, "upstream_inference_prompt_cost": 0.0015044}, "is_byok": false, "prompt_tokens": 861, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1272}`

## T0282 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label": "B", "confidence_0_100": 72, "stated_reasoning": "B leads on memory, web-search, multi-turn, and latency, which are primary for multi-step agent workflows; relevance is acceptable though lower; cost is much higher but secondary.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s vs A: 68, 55.7, 77.5, 79.98, 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 612, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 460}, "cost": 0.0045984, "cost_details": {"upstream_inference_completions_cost": 0.003672, "upstream_inference_cost": 0.0045984, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1184}`

### final

```text
{"final_choice_label": "B", "confidence_0_100": 85, "stated_reasoning": "B remains preferred: stronger memory, web-search, multi-turn, and lower latency for multi-step agents; BFCL rank 1 reinforces this over A rank 4 despite higher cost and slightly lower relevance.", "cited_overall_rank": "B:1, A:4", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s vs A: 68, 55.7, 77.5, 79.98, 13.50s; cost secondary", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 286, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 112}, "cost": 0.0031484, "cost_details": {"upstream_inference_completions_cost": 0.001716, "upstream_inference_cost": 0.0031484, "upstream_inference_prompt_cost": 0.0014324}, "is_byok": false, "prompt_tokens": 825, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1111}`

## T0283 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model A", "Model B"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":72,"stated_reasoning":"For multi-step agent workflows, memory and web-search are critical alongside multi-turn; B leads substantially on memory (73.76 vs 55.7) and web-search (84.5 vs 77.5), edges multi-turn, and has better latency, while cost is secondary and relevance gap is moderate.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, cost 86.55, P95 7.56s vs A 68/55.7/77.5/79.98/4.64/13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 729, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 551}, "cost": 0.0053004, "cost_details": {"upstream_inference_completions_cost": 0.004374, "upstream_inference_cost": 0.0053004, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1301}`

### final

```text
{"final_choice_label":"B","confidence_0_100":88,"stated_reasoning":"B remains preferred for multi-step agents due to clear leads in memory and web-search, slight multi-turn edge, and lower latency; BFCL overall rank 1 vs 4 further supports B despite higher cost and slightly lower relevance.","cited_overall_rank":"B:1, A:4","cited_scenario_specific_evidence":"B multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, cost 86.55, P95 7.56s vs A 68/55.7/77.5/79.98/4.64/13.50s","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 279, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 104}, "cost": 0.0031584, "cost_details": {"upstream_inference_completions_cost": 0.001674, "upstream_inference_cost": 0.0031584, "upstream_inference_prompt_cost": 0.0014844}, "is_byok": false, "prompt_tokens": 851, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1130}`

## T0284 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "Model B", "confidence_0_100": 72, "stated_reasoning": "For multi-step agent workflows, memory and web-search are critical along with multi-turn; Model B leads substantially on memory (73.76 vs 55.7), web-search (84.5 vs 77.5), slightly on multi-turn, and has better P95 latency (7.56s vs 13.5s). Relevance favors A and cost strongly favors A but cost is secondary; overall B better matches primary needs.", "cited_overall_rank": "not provided", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, rel 73.61, cost 86.55, p95 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, rel 79.98, cost 4.64, p95 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 701, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 473}, "cost": 0.0051324, "cost_details": {"upstream_inference_completions_cost": 0.004206, "upstream_inference_cost": 0.0051324, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1273}`

### final

```text
{"final_choice_label": "Model B", "confidence_0_100": 85, "stated_reasoning": "Primary needs are multi-turn, memory, web-search, and latency for agent workflows; B leads on memory, web-search, multi-turn, and P95 latency. BFCL rank 1 vs 4 reinforces B. Cost favors A but is secondary; relevance edge for A is insufficient to override.", "cited_overall_rank": "B:1 A:4", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, rel 73.61, cost 86.55, p95 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, rel 79.98, cost 4.64, p95 13.50s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 316, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 103}, "cost": 0.0034804, "cost_details": {"upstream_inference_completions_cost": 0.001896, "upstream_inference_cost": 0.0034804, "upstream_inference_prompt_cost": 0.0015844}, "is_byok": false, "prompt_tokens": 901, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1217}`

## T0285 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "Model A", "confidence_0_100": 72, "stated_reasoning": "Model A leads on memory, web-search, multi-turn, and latency which are primary for multi-step agent workflows; relevance is modestly lower and cost is much higher but cost is secondary.", "cited_overall_rank": null, "cited_scenario_specific_evidence": "A: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s, cost $86.55; B: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, P95 13.50s, cost $4.64"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 666, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 492}, "cost": 0.0049224, "cost_details": {"upstream_inference_completions_cost": 0.003996, "upstream_inference_cost": 0.0049224, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1238}`

### final

```text
{"final_choice_label": "Model A", "confidence_0_100": 80, "stated_reasoning": "Model A remains preferred for multi-step agents due to stronger memory, web-search, multi-turn, and lower latency; BFCL overall rank 1 vs 4 further supports keeping A despite higher cost and slightly weaker relevance.", "cited_overall_rank": "A:1, B:4", "cited_scenario_specific_evidence": "A: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s, cost $86.55; B: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, P95 13.50s, cost $4.64", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 363, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 166}, "cost": 0.0036544, "cost_details": {"upstream_inference_completions_cost": 0.002178, "upstream_inference_cost": 0.0036544, "upstream_inference_prompt_cost": 0.0014764}, "is_byok": false, "prompt_tokens": 847, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1210}`

## T0286 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"B","confidence_0_100":72,"stated_reasoning":"For multi-step agent workflows, memory, multi-turn, and web-search are primary; B leads substantially on memory and web-search, edges multi-turn, and has markedly better P95 latency. Relevance favors A and cost strongly favors A, but cost is secondary and relevance gap is smaller than B memory/web advantages.","cited_overall_rank":null,"cited_scenario_specific_evidence":"B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, latency 7.56s, cost $86.55; A: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, latency 13.50s, cost $4.64"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 660, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 466}, "cost": 0.0042336, "cost_details": {"upstream_inference_completions_cost": 0.00396, "upstream_inference_cost": 0.0042336, "upstream_inference_prompt_cost": 0.0002736}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1232}`

### final

```text
{"final_choice_label":"B","confidence_0_100":85,"stated_reasoning":"Scenario priorities favor multi-turn, memory, web-search, and latency; B leads on those and is only modestly behind on relevance while cost remains secondary. BFCL overall rank 1 for B vs 4 for A reinforces the provisional choice without reversing it.","cited_overall_rank":"B:1, A:4","cited_scenario_specific_evidence":"B: multi-turn 68.38, memory 73.76, web-search 84.5, relevance 73.61, P95 7.56s, cost $86.55; A: multi-turn 68, memory 55.7, web-search 77.5, relevance 79.98, P95 13.50s, cost $4.64","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 311, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 113}, "cost": 0.0027296, "cost_details": {"upstream_inference_completions_cost": 0.001866, "upstream_inference_cost": 0.0027296, "upstream_inference_prompt_cost": 0.0008636}, "is_byok": false, "prompt_tokens": 867, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1178}`

## T0287 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "GLM-4.6 (FC thinking)", "Model B": "Claude-Opus-4-5-20251101 (FC)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label": "B", "confidence_0_100": 72, "stated_reasoning": "For multi-step agent workflows, memory, multi-turn, and web-search dominate; B leads substantially on memory (73.76 vs 55.7) and web-search (84.5 vs 77.5), edges multi-turn, and has better P95 latency (7.56s vs 13.5s). Cost is secondary despite A being far cheaper; A only leads on relevance/irrelevance.", "cited_overall_rank": "not provided", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, cost 86.55, P95 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost 4.64, P95 13.50s"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 604, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 375}, "cost": 0.0038976, "cost_details": {"upstream_inference_completions_cost": 0.003624, "upstream_inference_cost": 0.0038976, "upstream_inference_prompt_cost": 0.0002736}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1176}`

### final

```text
{"final_choice_label": "B", "confidence_0_100": 88, "stated_reasoning": "Scenario priorities favor multi-turn, memory, and web-search; B leads on those and latency while holding BFCL overall rank 1 vs A at 4. Cost remains secondary; A’s relevance edge and lower cost do not offset weaker memory and slower P95 for agent workflows.", "cited_overall_rank": "B: 1; A: 4", "cited_scenario_specific_evidence": "B: multi-turn 68.38, memory 73.76, web-search 84.5, rel/irrel 73.61, cost 86.55, P95 7.56s; A: multi-turn 68, memory 55.7, web-search 77.5, rel/irrel 79.98, cost 4.64, P95 13.50s", "changed_after_rank_disclosure": false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 324, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 109}, "cost": 0.0028776, "cost_details": {"upstream_inference_completions_cost": 0.001944, "upstream_inference_cost": 0.0028776, "upstream_inference_prompt_cost": 0.0009336}, "is_byok": false, "prompt_tokens": 902, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1226}`

## T0288 - xAI - Profile C - MRI_ORDER

- Label mapping: {"Model A": "Claude-Opus-4-5-20251101 (FC)", "Model B": "GLM-4.6 (FC thinking)"}
- Candidate order: ["Model B", "Model A"]

### provisional

```text
{"provisional_choice_label":"Model A","confidence_0_100":72,"stated_reasoning":"Model A leads on the primary agent factors of memory, web-search, and multi-turn while also showing lower latency; relevance is modestly lower and cost is much higher but cost is secondary.","cited_overall_rank":null,"cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; relevance 73.61 vs 79.98; latency 7.56s vs 13.50s; cost secondary"}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 778, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 633}, "cost": 0.0055944, "cost_details": {"upstream_inference_completions_cost": 0.004668, "upstream_inference_cost": 0.0055944, "upstream_inference_prompt_cost": 0.0009264}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 1350}`

### final

```text
{"final_choice_label":"Model A","confidence_0_100":85,"stated_reasoning","Model A remains preferred: stronger memory, web-search, and multi-turn for agent workflows, better latency, and top BFCL rank 1; relevance is slightly weaker and cost higher but cost is secondary.","cited_overall_rank":"Model A: 1; Model B: 4","cited_scenario_specific_evidence":"multi-turn 68.38 vs 68; memory 73.76 vs 55.7; web-search 84.5 vs 77.5; relevance 73.61 vs 79.98; P95 latency 7.56s vs 13.50s; cost secondary","changed_after_rank_disclosure":false}
```

- Returned model field present: True
- Usage: `{"completion_tokens": 265, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 96}, "cost": 0.0023556, "cost_details": {"upstream_inference_completions_cost": 0.00159, "upstream_inference_cost": 0.0023556, "upstream_inference_prompt_cost": 0.0007656}, "is_byok": false, "prompt_tokens": 818, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 512, "video_tokens": 0}, "total_tokens": 1083}`