HealthBench Professional
525 physician-authored tasks from real clinician chats (care consult, documentation, research) with physician-adjudicated rubrics.
Enriched about 3.5x for examples that are hard for OpenAI models. About one third adversarial.
In the authors’ words
Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited. We introduce HealthBench Professional, an open benchmark for evaluating large language models on real tasks that clinicians bring to ChatGPT in the course of their work. The benchmark is organized around three common use cases central to clinical practice: care consult, writing and documentation, and medical research. Each example includes a physician-authored conversation with ChatGPT for Clinicians and is scored via rubrics written and iteratively adjudicated by three or more physicians across three phases. HealthBench Professional examples were carefully selected for quality, representativeness, and difficulty for OpenAI's current frontier models, to enable continued measurement of progress. Difficult examples for recent OpenAI models were enriched by roughly 3.5 times relative to the candidate pool of 15,079 examples. Additionally, about one-third of examples involve physicians conducting deliberate adversarial testing of models. As a strong baseline, we also collected human physician responses for all tasks (unbounded time, specialist-matched, web access). The best scoring system, GPT-5.4 in ChatGPT for Clinicians, outperforms base GPT-5.4, all other models, and human physicians. We hope HealthBench Professional provides the healthcare AI community a measure to track frontier model progress in real-world clinical tasks and build systems that clinicians can trust to improve care.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Claude Sonnet 5.5 leads with 69.2%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 69.2 Claude Sonnet 5.5
- 68.8
Gemini 4 Argon
- 66.0 Claude Fable 5
- 65.6 Claude Opus 5.5
- 65.3
Ling-3.1-flash
- 64.8 Claude Haiku 5.5
- 64.7 GPT-6 Astra
- 64.2 GPT-6.1 Sol
- 62.7 GPT-6.1 Terra
- 62.3
Muse Wonder Full
- 62.1 Claude Fable 5.1
- 60.8 GPT-6 Luna
- 60.8 GPT-6 Sol
- 60.5 GPT-5.6 Sol
- 60.5
Muse Wonder
- 59.8 Claude Opus 5
- 59.3
Muse Spark 1.1
- 57.8 Claude Sonnet 5
- 57.7 GPT-5.6 Terra
- 56.7 Grok 4.7
- 55.8 Claude Opus 4.8
- 55.7 GPT-5.6 Luna
- 51.8 GPT-5.5
- 38.4 GPT-5.5 Instant
- 35.0
MAI-Thinking-1
- 1 Claude Sonnet 5.5 69.2%
- 2
Gemini 4 Argon 68.8%
- 3 Claude Fable 5 66%
- 4 Claude Opus 5.5 65.6%
- 5
Ling-3.1-flash 65.3%
- 6 Claude Haiku 5.5 64.8%
- 7 GPT-6 Astra 64.7%
- 8 GPT-6.1 Sol 64.2%
- 9 GPT-6.1 Terra 62.7%
- 10
Muse Wonder Full 62.3%
- 11 Claude Fable 5.1 62.1%
- 12 GPT-6 Luna 60.8%
- 13 GPT-6 Sol 60.8%
- 14 GPT-5.6 Sol 60.5%
- 15
Muse Wonder 60.5%
- 16 Claude Opus 5 59.8%
- 17
Muse Spark 1.1 59.3%
- 18 Claude Sonnet 5 57.8%
- 19 GPT-5.6 Terra 57.7%
- 20 Grok 4.7 56.7%
- 21 Claude Opus 4.8 55.8%
- 22 GPT-5.6 Luna 55.7%
- 23 GPT-5.5 51.8%
- 24 GPT-5.5 Instant 38.4%
- 25
MAI-Thinking-1 35%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 5.5 | 69.2% | Sep 2026 | Model card | |
| 2 | | 68.8% | Oct 2026 | Model card | |
| 3 | Claude Fable 5 | 66% | Jun 2026 | Model card | |
| 4 | Claude Opus 5.5 | 65.6% | Sep 2026 | Model card | |
| 5 | | 65.3% | Oct 2026 | Model card | |
| 6 | Claude Haiku 5.5 | 64.8% | Oct 2026 | Model card | |
| 7 | GPT-6 Astra | 64.7% | Sep 2026 | Model card | |
| 8 | GPT-6.1 Sol | 64.2% | Sep 2026 | Model card | |
| 9 | GPT-6.1 Terra | 62.7% | Oct 2026 | Model card | |
| 10 | | 62.3% | Oct 2026 | Model card | |
| 11 | Claude Fable 5.1 | 62.1% | Sep 2026 | Model card | |
| 12 | GPT-6 Luna | 60.8% | Sep 2026 | Model card | |
| 13 | GPT-6 Sol | 60.8% | Sep 2026 | Model card | |
| 14 | GPT-5.6 Sol | 60.5% | Jun 2026 | Model card | |
| 15 | | 60.5% | Oct 2026 | Model card | |
| 16 | Claude Opus 5 | 59.8% | Jul 2026 | Model card | |
| 17 | | 59.3% | Jul 2026 | Model card | |
| 18 | Claude Sonnet 5 | 57.8% | Jun 2026 | Model card | |
| 19 | GPT-5.6 Terra | 57.7% | Jul 2026 | Model card | |
| 20 | Grok 4.7 | 56.7% | Sep 2026 | Model card | |
| 21 | Claude Opus 4.8 | 55.8% | May 2026 | Model card | |
| 22 | GPT-5.6 Luna | 55.7% | Jul 2026 | Model card | |
| 23 | GPT-5.5 | 51.8% | Apr 2026 | Model card | |
| 24 | GPT-5.5 Instant | 38.4% | May 2026 | Model card | |
| 25 | | 35% | Jun 2026 | Model card |
4 models have held number one since its release in Apr 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
6 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 13 Oct 2026 | 65.3 | Healthbench Professional (AQ environment) | |
| 12 Oct 2026 | 60.5 | HealthBench Pro | |
| 12 Oct 2026 | 62.3 | HealthBench Pro | |
| 7 Oct 2026 | Claude Haiku 5.5 | 64.8 | HealthBench Professional (Length-adjusted) |
| 28 Sep 2026 | Claude Sonnet 5.5 | 69.2 | HealthBench Professional |
| 22 Sep 2026 | Claude Opus 5.5 | 65.6 | HealthBench Professional |
| 21 Sep 2026 | Grok 4.7 | 56.7 | HealthBench Professional (Clinical reasoning) |
| 3 Sep 2026 | GPT-6 Astra | 63.4 | HealthBench Professional (length-adjusted) |
| 1 Sep 2026 | Claude Fable 5.1 | 62.1 | HealthBench Professional |
| 24 Jul 2026 | Claude Opus 5 | 59.8 | Health HealthBench Professional |
| 9 Jul 2026 | GPT-5.6 Luna | 55.7 | HealthBench Professional |
| 9 Jul 2026 | GPT-5.6 Terra | 57.7 | HealthBench Professional |
| 30 Jun 2026 | Claude Sonnet 5 | 57.8 | HealthBench Professional |
| 9 Jun 2026 | Claude Fable 5 | 66 | Health HealthBench Professional |
| 2 Jun 2026 | 35 | HealthBench Prof. |
31 system cards report it, from 6 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 29 Oct 2026 | GPT-6 Luna system card addendum | 47.4 | HealthBench Professional, length-adjusted (Table 5) | Health |
| 26 Oct 2026 | GPT-6.1 Terra system card addendum | 62.7 | HealthBench Professional, length-adjusted (unadjusted 64.2) (HealthBench table, 5.1) | Health |
| 13 Oct 2026 | | 68.8 | HealthBench Pro, without Search grounding 68.8 (length-adjusted 56.5); with Search grounding 76.7 (length-adjusted 61.4) (Table 5.1) | Health |
| 12 Oct 2026 | | 60.5 | HealthBench Pro, length-normalized weighted rubric score, no tools, Muse Wonder (max) (capabilities table image, page 3) | Health |
| 12 Oct 2026 | | 62.3 | HealthBench Pro, length-normalized weighted rubric score, no tools, Muse Wonder Full (max) (capabilities table image, page 3) | Health |
| 7 Oct 2026 | GPT-6 Sol system card addendum | 55.2 | HealthBench Professional, length-adjusted (Table 5) | Health |
| 7 Oct 2026 | GPT-6 Luna system card addendum | 48.2 | HealthBench Professional, length-adjusted (Table 5) | Health |
| 7 Oct 2026 | Claude Haiku 5.5 system card | 64.8 | HealthBench Professional, length-adjusted, max effort (raw 71.0) (Table 8.1.A) | Health |
| 29 Sep 2026 | GPT-6.1 Sol system card addendum | 64.2 | HealthBench Professional, length-adjusted (Table 7) | Health |
| 28 Sep 2026 | Claude Sonnet 5.5 system card | 69.2 | HealthBench Professional, length-adjusted at max effort (77.1% before length adjustment) (Table 8.1.A, 8.15.2) | Health |
| 22 Sep 2026 | GPT-6 Sol system card | 60.8 | HealthBench Professional, length-adjusted (Table 29, A.4.1) | Health |
| 22 Sep 2026 | GPT-6 Luna system card | 60.8 | HealthBench Professional, length-adjusted (Table 29, A.4.1) | Health |
| 22 Sep 2026 | Claude Opus 5.5 system card | 65.6 | HealthBench Professional, length-adjusted at max effort (77.1% before length adjustment) (Table 8.1.A, 8.15.2) | Health |
| 21 Sep 2026 | Grok 4.7 model card | 56.7 | HealthBench Professional, xhigh, graded by Grok 4.6 (high), filtered tasks scored zero (5.1) | Health |
| 3 Sep 2026 | GPT-6 Astra system card | 64.7 | HealthBench Professional, length-adjusted, revised 2026-09-22 (Table 6) | Health |
| 1 Sep 2026 | Claude Fable 5.1 system card | 62.1 | HealthBench Professional length-adjusted score (Table 8.1.A); raw score 74.2% (8.17.2) | Health |
| 6 Aug 2026 | GPT-5.6 Sol system card addendum | 54 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 6 Aug 2026 | GPT-5.6 Luna system card addendum | 44.1 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 24 Jul 2026 | Claude Opus 5 system card | 59.8 | HealthBench Professional, length-adjusted at max effort (73.4% raw) (Table 8.1.A, 8.15.2) | Health |
| 9 Jul 2026 | GPT-5.6 Terra system card | 57.7 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 9 Jul 2026 | GPT-5.6 Sol system card | 60.5 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 9 Jul 2026 | GPT-5.6 Luna system card | 55.7 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 9 Jul 2026 | | 59.3 | HealthBench Professional (Figure 44) | Health |
| 30 Jun 2026 | Claude Sonnet 5 system card | 57.8 | HealthBench Professional, length-adjusted, max effort, five trials (Table 8.1.A, 8.12.2) | Health |
| 25 Jun 2026 | GPT-5.6 Terra system card | 57.7 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 25 Jun 2026 | GPT-5.6 Sol system card | 60.5 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 25 Jun 2026 | GPT-5.6 Luna system card | 55.7 | HealthBench Professional, length-adjusted (5.1 table) | Health |
| 2 Jun 2026 | | 35 | HealthBench Prof., length-penalized, GPT-5.4 grader (Table 12, rounded) | Health |
| 28 May 2026 | Claude Opus 4.8 system card | 55.8 | HealthBench Professional, length-adjusted score, max effort, Claude Sonnet 4.6 grader, five trials (8.14.1, Figure 8.14.A) | Health |
| 4 May 2026 | GPT-5.5 Instant system card | 38.4 | HealthBench Professional, length-adjusted, unadjusted 40.7 (Table 5) | Health |
| 23 Apr 2026 | GPT-5.5 system card | 51.8 | HealthBench Professional, length-adjusted (Table 7) | Health |
More in Law, finance and medicine
- MedXpertQA-MM In use Models answer expert-level medical exam questions that combine images with clinical records and test results. Scoring is accuracy.
Qwen 3.8 Max 80.4%
- HealthBench In use 5,000 realistic health conversations graded against physician-written rubrics. A length-adjusted variant penalizes verbosity. GPT-5 67.2%
- KBL In use Models answer Korean legal knowledge and reasoning tasks and Korean bar exam questions. It has 7 knowledge tasks, 4 reasoning tasks and 2,510 bar exam items.
Solar Pro 4 77.5%
- KorMedMCQA In use Models answer 7,469 multiple-choice questions from Korean licensing exams for doctors, nurses, pharmacists and dentists, held from 2012 to 2024.
Solar Pro 4 93.2%
- PRBench In use Model answers 1,100 expert-written law and finance questions. LLM judge grades against weighted expert rubrics of 10-30 criteria each.
Muse Spark 1.3 61.6%
- FrontierFinance In use Agents answer 220 investment research queries from finance experts across six use cases of the investor workflow. Three judge models check each answer against 11,543 source-attributed rubrics. Claude Fable 5.1 55.9%