MMLU-Pro overview and key facts (October 2026)

MMLU-Pro is an evaluation dataset with about 12,000 hard multiple-choice questions across disciplines. Teams use it to compare how well language models reason and recall knowledge.

MMLU-Pro extends the older MMLU benchmark from four to ten answer options, which lowers the chance of guessing correctly. Questions cover subjects such as math, law, health, engineering and economics. More than ten experts reviewed the questions, and the maintainers keep correcting errors, most recently in January 2026. TIGER-Lab publishes the data under the MIT license, together with code on GitHub and a leaderboard.

• Compares candidate modelsBefore choosing a model for an internal assistant, teams can run MMLU-Pro on the candidates and compare scores per subject. This gives a first view of knowledge and reasoning beyond vendor claims.
• Checks fine-tuned models for regressionsAfter fine-tuning a model on company data, running the benchmark again shows whether general knowledge dropped. Teams catch side effects before deployment. Comparing scores per subject shows exactly which areas were affected by the fine-tuning.
• Compares prompting methodsThe published results distinguish chain-of-thought and direct answers. Teams can test whether step-by-step prompting is worth the extra tokens for their chosen model. Longer reasoning raises cost per request, so the answer affects the budget.

• Ten answer options instead of four reduce the effect of guessing.
• Maintained with a public changelog of corrected questions.

MMLU-Pro is maintained by TIGER-Lab at the University of Waterloo in Canada, according to the contacts on the dataset card.

Public benchmark questions may have entered model training data, so high scores can overstate real ability; add your own task-specific tests. The questions are in English and say little about German-language performance. Compare results only within the same prompt setup.

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