MedXpertQA
A comprehensive benchmark to evaluate expert-level medical knowledge and advanced reasoning, featuring 4,460 questions spanning 17 specialties and 11 body systems. Includes both text-only and multimodal subsets with expert-level exam questions incorporating diverse medical images and rich clinical information.
Muse Spark from Meta currently leads the MedXpertQA leaderboard with a score of 0.784 across 12 evaluated AI models.
What MedXpertQA measures
MedXpertQA is a multimodal benchmark that evaluates large language models on multimodal, reasoning, healthcare, and vision tasks. LLM Stats tracks 12 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.5, with the leader reaching 0.8.
Compare leaders on the best AI for multimodal, best AI for reasoning, best AI for healthcare and best AI for vision leaderboards.
Publication
- Paper
- MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding
- Authors
- Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, and 5 others
- Published
- arXiv
- 2501.18362
Abstract
We introduce MedXpertQA, a highly challenging and comprehensive benchmark to evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA includes 4,460 questions spanning 17 specialties and 11 body systems. It includes two subsets, Text for text evaluation and MM for multimodal evaluation. Notably, MM introduces expert-level exam questions with diverse images and rich clinical information, including patient records and examination results, setting it apart from traditional medical multimodal benchmarks with simple QA pairs generated from image captions. MedXpertQA applies rigorous filtering and augmentation to address the insufficient difficulty of existing benchmarks like MedQA, and incorporates specialty board questions to improve clinical relevance and comprehensiveness. We perform data synthesis to mitigate data leakage risk and conduct multiple rounds of expert reviews to ensure accuracy and reliability. We evaluate 18 leading models on \benchmark. Moreover, medicine is deeply connected to real-world decision-making, providing a rich and representative setting for assessing reasoning abilities beyond mathematics and code. To this end, we develop a reasoning-oriented subset to facilitate the assessment of o1-like models. Code and data are available at: https://github.com/TsinghuaC3I/MedXpertQA
Muse Spark leads with 78.4%, followed by 
Qwen3.5-122B-A10B at 67.3% and Qwen3.5-27B at 62.4%.
Progress Over Time
Interactive timeline showing model performance evolution on MedXpertQA
MedXpertQA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Meta | — | — | — | ||
| 2 |  Alibaba Cloud / Qwen Team | 122B | 262K | $0.40 / $3.20 | ||
| 3 | Alibaba Cloud / Qwen Team | 27B | 262K | $0.30 / $2.40 | ||
| 4 | Alibaba Cloud / Qwen Team | 35B | 262K | $0.25 / $2.00 | ||
| 5 |  Google | 31B | 262K | $0.14 / $0.40 | ||
| 6 | Google | 25B | 262K | $0.13 / $0.40 | ||
| 7 | Google | 25B | — | — | ||
| 8 | Google | 12B | — | — | ||
| 9 |  Microsoft | 1.0T | — | — | ||
| 10 | Google | 8B | — | — | ||
| 11 | Google | 5B | — | — | ||
| 12 | Google | 4B | — | — |
FAQ
Common questions about MedXpertQA.
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