Benchmark Almanac

MedXpertQA

Muse Spark 52.6% leads since Apr 2026 Fading

Models answer 4,460 expert-level medical exam questions across 17 specialties, in a text subset and an image subset. Scoring is multiple-choice accuracy.

In the authors’ words

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

Abstract of MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding (arXiv:2501.18362, CC0)
Made by Tsinghua University (TsinghuaC3I) Released Jan 2025 Medicine All tasks public Last in a launch post Jun 2026 , dropped before it saturated Last model added Jun 2026
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Multiple choice
2
Models scored in lab cards
2
Launch posts list it, from 2 labs
1
System card reports it, from 1 lab

Muse Spark leads with 52.6%

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.

  1. 1 Muse Spark 52.6%
  2. 2 MAI-Thinking-1 43%
#ModelScoreSettingDateSource
1 Muse Spark 52.6% Apr 2026 Model card
2 MAI-Thinking-1 43% Jun 2026 Model card

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release.

Meta
Lab claims Not tested since Jun 2026
Muse Spark 52.6%

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Muse Spark 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Muse Spark

Labs headline it less often now, last in Jun 2026

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.

Meta
Microsoft
DateLaunch postScoreAs the post writes it
2 Jun 2026 MAI-Thinking-1 43 MedXpert QA
8 Apr 2026 Muse Spark 52.6 MedXpertQA (Text)

One system card reports it

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.

DateDocumentScoreAs the document writes itSection
2 Jun 2026 MAI-Thinking-1 technical report 43 MedXpert QA, text multiple choice (Table 12, rounded) Health

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