Medical AI Superintelligence Test (MAST) Leaderboard
We independently benchmark AI models on clinical safety, medical imaging, and reasoning, so you can make informed decisions about the health AI you use.
Which AI can you trust for medical questions?
Ranked by MAST composite score across five clinical benchmarks. Updated April 2026.
Model | ScoreOverall Score | |
|---|---|---|
| 1 | AMBOSS LiSA 1.0  AMBOSS | 66.7% |
| 2 | Gemini 2.5 Flash  Google | 66.1% |
| 3 | Gemini 2.5 Pro Google | 65.4% |
| 4 | Grok 4  xAI | 64.0% |
| 5 | Grok 4 Fast xAI | 63.4% |
| 6 | Glass Health 4.0  Glass Health | 62.7% |
| 7 | GPT-5  OpenAI | 62.6% |
| 8 | Gemini 2.0 Flash Google | 61.8% |
Compare models
See how two models perform across all five benchmarks.
| Benchmark | Gap |
|---|---|
| First Do NOHARM v2 | — |
| Script Concordance Test | — |
| MedAgentBench v2 | — |
| ReXrank Mini | — |
| DermBench | — |
Looking for more technical information?
The technical leaderboard hosts the full evaluation dataset that powers MAST — granular benchmark results, standard deviations, and the model-vs-model comparisons used by researchers, developers, and organizations evaluating medical AI.
Developers & Contributors
Analyze, audit, and contribute to MAST. Explore the methodology, run evaluations, and help improve medical AI safety benchmarks.
Submission guidelines
We welcome benchmark submissions via GitHub. All submissions must include a peer-reviewed or pre-print manuscript, a publicly accessible dataset, and reproducible evaluation code. Results should be generated using the official MAST evaluation harness.
Review process
Submissions are reviewed by the MAST governance committee for clinical relevance, methodological rigor, and reproducibility. Accepted benchmarks are integrated into the composite score on a quarterly release cycle. See our policies and instructions on GitHub before opening a pull request.