Clinical Benchmarks
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GPT-5

Released 7 Aug 2025$1.25 input, $10 output per million tokens400K contextproprietaryAlso written as gpt-5-thinking, GPT-5 Thinking

11 results on 5 of 13 boards, 6 of them on secondary measures, latest measured Sep 2026. Compare with other models

Clinical Benchmarks Index54.1Rank 6 of 1495 of 10 boardsAverage × multiplier 59.3 × 0.913 = 54.1This site’s own calculation. Working How it is computed

Results

Each line sits on its own board's scale. Tall tick: the board leader.

Clinical reasoning and knowledge

  1. HealthBench Professional
    length-adjusted, max reasoning effort, GPT-5.6 system card Table 6 column GPT-5 (51.0 unadjusted, 3616 chars)
    0.462
    Rank 28 of 35Leader GPT-6 Astra (Anthropic run) 0.703
    Vendor-reported
    Measured Jun 2026

Documentation and coding

  1. MedScribe (Vals AI)
    model ID openai/gpt-5-2025-08-07; max_output_tokens=30000; reasoning_effort=high
    83.65
    Rank 41 of 105Leader Claude Opus 5.5 91.43
    Official leaderboard
    Measured Sep 2026
  2. MedCode (Vals AI)
    model ID openai/gpt-5-2025-08-07; reasoning_effort=high; max_output_tokens=30000
    49.63
    Rank 18 of 103Leader Claude Opus 5 63.57
    Official leaderboard
    Measured Sep 2026

Safety

  1. MedPIC
    zero-shot; independent questions; exact option-set match
    75.6
    Rank 2 of 28Leader Gemini-3.1-Pro 80.7
    Independent run
    Measured Aug 2026
  2. First, Do NOHARM (v2)
    from the Model Leaderboard SAFETY column (NOHARM v2 F1 weighted, shown with CI); not in the Latest Flagships ranking
    68.6
    Rank 10 of 17 here, 19 models on the boardLeader LiSA 2.5 86.2
    Official leaderboard
    Measured Aug 2026

Index working

The Clinical Benchmarks Index is this site's own calculation: a weighted average of the board scores times a multiplier for the boards behind it. How it is computed

  • HealthBench Professional0.46246.2×0.743length-adjusted, max reasoning effort, GPT-5.6 system card Table 6 column GPT-5 (51.0 unadjusted, 3616 chars)
  • MedScribe (Vals AI)83.6583.7×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID openai/gpt-5-2025-08-07; max_output_tokens=30000; reasoning_effort=high
  • MedCode (Vals AI)49.6349.6×0.911model ID openai/gpt-5-2025-08-07; reasoning_effort=high; max_output_tokens=30000
  • MedPIC75.675.6×0.482, near its ceilingNear its ceiling: leader 80.7, weight 0.482zero-shot; independent questions; exact option-set match
  • First, Do NOHARM (v2)68.668.6×0.507from the Model Leaderboard SAFETY column (NOHARM v2 F1 weighted, shown with CI); not in the Latest Flagships ranking
Weighted average of 5 boards
59.3
Multiplier for 5 of 10 boards
× 0.913
Index
59.3 × 0.913 = 54.1

Score: the result on the board’s own 0 to 100 scale. Dot: this model; tick: the board’s leader. Weight: how much the board counts in the weighted average; bold where the board is near its ceiling and counts for less. Each result links to its quote and page; the line under it is the setting it was measured with.

Secondary measures

Safety

  1. MedPIC, Guideline-following accuracy (GF)
    zero-shot; independent questions; exact option-set match
    83.5
    Rank 2 of 28Leader Gemini-3.1-Pro 87.7
    Independent run
    Measured Aug 2026
  2. MedPIC, Counterfactual accuracy (CF)
    zero-shot; independent questions; exact option-set match
    63.4
    Rank 2 of 28Leader Gemini-3.1-Pro 69.9
    Independent run
    Measured Aug 2026
  3. MedPIC, Linked counterfactual pair accuracy
    zero-shot; independent questions; exact option-set match
    38.2
    Rank 2 of 28Leader Gemini-3.1-Pro 48.3
    Independent run
    Measured Aug 2026
  4. MedPIC, Risk deactivation accuracy
    zero-shot; independent questions; exact option-set match
    61.8
    Rank 2 of 28Leader Gemini-3.1-Pro 77.9
    Independent run
    Measured Aug 2026
  5. MedPIC, Risk activation accuracy
    zero-shot; independent questions; exact option-set match
    70.4
    Rank 3 of 28Leader MedGemma-27B-Text 76.1
    Independent run
    Measured Aug 2026
  6. MedPIC, Published GF-minus-CF accuracy gap
    zero-shot; independent questions; exact option-set match
    20.1
    Descriptive measure, not ranked
    Independent run
    Measured Aug 2026

Sources

The quote, page and document for each of the 11 results
  1. 28HealthBench Professional length-adjusted 0.462
    Printed as 46.2Vendor-reported, measured Jun 2026Configuration: length-adjusted, max reasoning effort, GPT-5.6 system card Table 6 column GPT-5 (51.0 unadjusted, 3616 chars)
    GPT-5.6 System Card system card, OpenAI, 9 Jul 2026. Section 5.1 HealthBench, Table 6 (reported as length-adjusted score (unadjusted, mean response length in characters)), column GPT-5
    HealthBench Professional length-adjusted 46.2 (51.0, 3616) 39.6 (48.0, 4863) 45.9 (50.0, 3400) 48.1 (51.9, 3308) 51.8 (57.2, 3818) 60.5 (64.1, 3228) 57.7 (62.4, 3618) 55.7 (59.8, 3389)
    Every result from this document
    • Also printed in GPT-5.6 Preview System Card: 46.2, Section 5.1 HealthBench, Table 6 (reported as length-adjusted score (unadjusted, mean response length in characters)) (preview card, published 2026-06-26), column GPT-5
  2. 41MedScribe (Vals AI) gpt-5-2025-08-07 83.65
    Printed as 83.65%Official leaderboard, measured Sep 2026Configuration: model ID openai/gpt-5-2025-08-07; max_output_tokens=30000; reasoning_effort=high
    Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 41 of 105 (GPT 5), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["openai/gpt-5-2025-08-07"].
    41 | GPT 5 | 83.65%±1.94 | $1.25/$10 | 3m05s
    Every result from this document
  3. 18MedCode (Vals AI) gpt-5-2025-08-07 49.63
    Printed as 49.63%Official leaderboard, measured Sep 2026Configuration: model ID openai/gpt-5-2025-08-07; reasoning_effort=high; max_output_tokens=30000
    Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 18 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["openai/gpt-5-2025-08-07"].
    18 | GPT 5 | 49.63%±2.10 | $1.25/$10 | 58.27s
    Every result from this document
  4. 2MedPIC zero-shot 75.6
    Printed as 75.6Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column Overall; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  5. 10First, Do NOHARM (v2) from the Model Leaderboard… 68.6
    Printed as 68.6%Official leaderboard, measured Aug 2026Configuration: from the Model Leaderboard SAFETY column (NOHARM v2 F1 weighted, shown with CI); not in the Latest Flagships ranking
    MAST technical leaderboard (First Do NOHARM v2 and per-benchmark results) official leaderboard, ARISE AI Research Network, 15 Aug 2026. arise-ai.org/mast/technical, Model Leaderboard (Top 10 shown), row 10, SAFETY column
    10 GPT-5 72.6%±2.3 68.6%±4.5 80.3%±4.4 30.1%±9.0 44.2%±2.5 45.4%±1.3 73.1%±3.9 73.7%±2.7 46.6%±0.0
    Every result from this document
  6. 2MedPIC, Guideline-following accuracy (GF) zero-shot 83.5
    Printed as 83.5Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column GF; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  7. 2MedPIC, Counterfactual accuracy (CF) zero-shot 63.4
    Printed as 63.4Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column CF; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  8. 2MedPIC, Linked counterfactual pair accuracy zero-shot 38.2
    Printed as 38.2Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column Pair; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  9. 3MedPIC, Risk activation accuracy zero-shot 70.4
    Printed as 70.4Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column Activation; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  10. 2MedPIC, Risk deactivation accuracy zero-shot 61.8
    Printed as 61.8Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column Deactivation; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document
  11. –MedPIC, Published GF-minus-CF accuracy gap zero-shot 20.1
    Printed as 20.1Independent run, measured Aug 2026Configuration: zero-shot; independent questions; exact option-set match
    Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning paper, Zhitian Hou, Yuhang Liu, Pengkai Wang, Zeyu Liu, Guanghao Zhu, Zheng Liu, Shuo Cai, Congkai Xie, Zhijie Sang, Kun Zeng, Hongxia Yang (The Hong Kong Polytechnic University; InfiX.ai; Sun Yat-sen University), 4 Aug 2026. arXiv:2608.03028v1, Section 4, Table 2 (#S4.T2), row GPT-5, column Δ_GF−CF; row order: Model | Overall | GF | CF | Δ_GF−CF | Activation | Deactivation | Pair
    GPT-5 | 75.6 | 83.5 | 63.4 | 20.1 | 70.4 | 61.8 | 38.2
    Every result from this document

Other OpenAI models: GPT-4.1, GPT-4.1 mini, GPT-4o, GPT-5.1, GPT-5.2, GPT-5.3-Codex, GPT-5.4, GPT-5.4 mini, GPT-5.4 nano, GPT-5.5, GPT-5.5 Instant, GPT-5.6 Luna, GPT-5.6 Sol, GPT-5.6 Terra, GPT-5 mini, GPT-5 nano, GPT-6.1 Sol, GPT-6 Astra, GPT-6 Luna, GPT-6 Sol, GPT OSS 120B, GPT OSS 20B, o3, o4-mini