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MedXpertQA

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Interactive timeline showing model performance evolution on MedXpertQA

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MedXpertQA Leaderboard

12 models
ContextCostLicense
1
2
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
122B
3
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
27B262K$0.30 / $2.40
4
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
35B
531B262K$0.13 / $0.38
625B262K$0.13 / $0.40
725B
812B
91.0T
108B
115B
124B
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About this benchmark

What is 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.

MedXpertQA is a multimodal benchmark evaluating models on multimodal, reasoning, healthcare, and vision tasks. LLM Stats tracks 12 models on this benchmark, scored on a 0–1 scale. The current average is 0.5, with the leader at 0.8.

Compare leaders on the best AI for multimodal, best AI for reasoning, best AI for healthcare and best AI for vision leaderboards.

Current leaders

Muse Spark from Meta currently leads the MedXpertQA leaderboard with a score of 0.784 across 12 evaluated AI models.

1Muse SparkMeta78.4%
2Qwen3.5-122B-A10BAlibaba Cloud / Qwen Team67.3%
3Qwen3.5-27BAlibaba Cloud / Qwen Team62.4%

Source paper

Title
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding
Authors
Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, and 5 others
Published
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

FAQ

Common questions about the MedXpertQA benchmark and leaderboard.

What is the MedXpertQA benchmark?

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.

What is the MedXpertQA leaderboard?

The MedXpertQA leaderboard ranks 12 AI models based on their performance on this benchmark. Currently, Muse Spark by Meta leads with a score of 0.784. The average score across all models is 0.500.

What is the highest MedXpertQA score?

The highest MedXpertQA score is 0.784, achieved by Muse Spark from Meta.

How many models are evaluated on MedXpertQA?

12 models have been evaluated on the MedXpertQA benchmark, with 0 verified results and 12 self-reported results.

Where can I find the MedXpertQA paper?

The MedXpertQA paper is available at https://arxiv.org/abs/2501.18362. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does MedXpertQA cover?

MedXpertQA is categorized under multimodal, reasoning, healthcare, and vision. The benchmark evaluates multimodal models.

What is the best open-source model on MedXpertQA?

Qwen3.5-122B-A10B by Alibaba Cloud / Qwen Team is the top-ranked open-source model on MedXpertQA, with a score of 0.673 (rank #2).

How recent are the MedXpertQA leaderboard results?

The MedXpertQA leaderboard was last updated in August 2026 and currently includes 12 evaluated models.