{
 "body": "\n## What it measures\n\nMMLU-Pro gives a model a question and up to ten labelled options -- a few carry fewer after review\nremoved weak distractors -- across 14 categories: Biology, Business, Chemistry, Computer Science,\nEconomics, Engineering, Health, History, Law, Math, Philosophy, Physics, Psychology and Other. The\nmodel selects the single correct option.\n\nAbout 57% of the 12,032 test questions are difficulty-filtered MMLU survivors; the rest are new,\nfrom a STEM website, TheoremQA and SciBench, with options expanded toward ten via GPT-4-generated\ndistractors and reviewed by a panel of over ten experts. This cuts accuracy 16-33 points versus\nthe same models on original MMLU.\n\n## How it is scored\n\nAccuracy against a 10% random-guess floor, versus 25% on four-option MMLU -- one reason raw scores\nare not comparable between the two. No human baseline is published for MMLU-Pro.\n\nWhere original MMLU favoured log-likelihood scoring over chain-of-thought (CoT), MMLU-Pro was\nbuilt so CoT wins: accuracy drops as much as 19 points when CoT is switched off for GPT-4o.\nGrading extracts the answer letter from free-form text rather than comparing option\nlog-likelihoods. Reference protocol is 5-shot, examples drawn from a dedicated 70-question\nvalidation split, though the leaderboard notes this isn't uniform -- \"some models like Gemini use\n0-shot\" -- so shot count is not guaranteed comparable across reported scores.\n\n## Dataset and licence\n\nThe Hugging Face mirror holds 12,032 test questions (scored) plus a 70-question validation split,\n12,102 rows total: 6,810 carried over from original MMLU after a difficulty filter, 5,222 newly\nwritten. Math (1,351) and Physics (1,299) are the largest categories; History (381) and Computer\nScience (410) the smallest. Licence is MIT per the dataset card; the GitHub repository's\nevaluation code is separately licensed Apache-2.0. Answers are public in both splits; maintainers\nactively fix errors, most recently a January 2026 trim of a leading-space quirk flagged as a\npotential shortcut.\n\n## Who publishes it\n\nYubo Wang, Xueguang Ma, Ge Zhang, Yuansheng Ni, Abhranil Chandra, Shiguang Guo, Weiming Ren, Aaran\nArulraj, Xuan He, Ziyan Jiang, Tianle Li, Max Ku, Kai Wang, Alex Zhuang, Rongqi Fan, Xiang Yue and\nWenhu Chen published MMLU-Pro in June 2024 (arXiv:2406.01574), accepted at NeurIPS 2024. Most are\nat TIGER Lab, University of Waterloo (led by Wenhu Chen), with co-authors at Toronto and Carnegie\nMellon. TIGER Lab maintains the GitHub repository, dataset and leaderboard Space.\n\n## Lineage\n\nMMLU-Pro's predecessor is MMLU (`mmlu`): both test multitask academic and professional knowledge,\nbut MMLU-Pro pools MMLU's 57 subjects into 14 categories, expands four options to ten, adds harder\nreasoning questions, and drops items judged trivial or noisy. No formal successor was found.\nSeparately, several frontier cards here carry a `mmlu_physics` key close in value to that model's\n`mmlu_pro` score -- possibly a mis-keyed MMLU-Pro Physics score, not confirmed; see `mmlu_physics`\nrather than treating it as part of this lineage yet.\n\n## Saturation and contamination\n\nMMLU-Pro was built to restore headroom MMLU lost to saturation, and per-category scores still\nseparate models: one verified run showed scores from 73.6% (Engineering) to 93.0% (Biology), and\nevery tested model dropped 16-33 points versus its own MMLU score. But the ceiling is approaching\nfast: GPT-4o scored 72.55% (CoT) at launch in June 2024, and the top verified leaderboard row read\nfor this page in September 2026 credits Gemini-3.1-Pro with 91.16% -- roughly 19 points higher in\ntwo years. That pace, plus the January 2026 answer-formatting fix (see Dataset and licence), is\nworth watching even though scores have not fully collapsed together.\n\nContamination risk is assessed here as medium. About 57% of questions carry over from original\nMMLU and inherit its high contamination risk (public with its answer key since 2020); the rest are\nnew, and the full test-set answer key has been public since the June 2024 release with no held-out\nportion. No contamination study specific to MMLU-Pro was found in this research.\n\n## How to run it\n\nReference code and eval scripts live in TIGER-AI-Lab/MMLU-Pro on GitHub. In\nlm-evaluation-harness the group is `mmlu_pro`, 14\n`mmlu_pro_<category>` tasks generating free text and extracting the answer letter by regex,\n5-shot by default from the validation split. inspect_evals ships an equivalent `mmlu_pro` task.\nOpenCompass carries `mmlu_pro` with several config variants (0-shot CoT, few-shot, generic-LLM-judge).\nShot count and CoT-versus-direct prompting are not applied uniformly across the public leaderboard,\nso treat cross-source comparisons with that in mind.\n\n## Reading the numbers\n\nA strong MMLU-Pro score is harder to reach by guessing or recall than a strong MMLU score,\nreflecting more genuine reasoning under harder distractors -- the benchmark's whole reason for\nexisting. It says nothing about tasks outside multiple-choice knowledge testing: open-ended\ngeneration, tool use, multi-turn dialogue. A single overall number also hides real per-category\nspread, so check the 14 category scores before assuming uniform strength, especially between the\nMath/Physics/Engineering cluster and the Law/History/Philosophy cluster. Before comparing two\nmodels, check both used chain-of-thought and the same shot count, and treat MMLU-Pro and `mmlu`\nscores as non-comparable given their different option counts and baselines.\n",
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  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
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   "as_of": "2025-03",
   "attribution": "unverified-legacy",
   "display_name": "RedPajama INCITE Chat 3B v1",
   "model_id": "together/redpajama-incite-chat-3b-v1",
   "provider": "together",
   "provider_display": "Together AI",
   "score": 11.3,
   "source": "open-llm-leaderboard-v2"
  },
  {
   "as_of": "2025-03",
   "attribution": "unverified-legacy",
   "display_name": "DialoGPT medium",
   "model_id": "microsoft/dialogpt-medium",
   "provider": "microsoft",
   "provider_display": "Microsoft",
   "score": 11.2,
   "source": "open-llm-leaderboard-v2"
  },
  {
   "as_of": "2025-03",
   "attribution": "unverified-legacy",
   "display_name": "mt5 small",
   "model_id": "google/mt5-small",
   "provider": "google",
   "provider_display": "Google DeepMind",
   "score": 11.2,
   "source": "open-llm-leaderboard-v2"
  },
  {
   "as_of": "2025-03",
   "attribution": "unverified-legacy",
   "display_name": "RedPajama INCITE 7B Chat",
   "model_id": "together/redpajama-incite-7b-chat",
   "provider": "together",
   "provider_display": "Together AI",
   "score": 11.2,
   "source": "open-llm-leaderboard-v2"
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   "attribution": "unverified-legacy",
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   "display_name": "mt5 base",
   "model_id": "google/mt5-base",
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 ],
 "page": {
  "aliases": [],
  "category": "knowledge",
  "contamination": {
   "note": "About 57% of MMLU-Pro's questions are carried over from the original MMLU test set, which has been public with its answer key since September 2020 and is already assessed as high contamination risk on the `mmlu` family page. The remaining ~43% are newly written, and the full test-set answer key (old and new questions alike) has been public on Hugging Face since the June 2024 release with no held-out or refreshed portion. No contamination study specific to MMLU-Pro, and no publisher statement about the fraction of frontier pretraining corpora that include it, was found in this research.\n",
   "risk": "medium"
  },
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 12032,
   "size_note": "The Hugging Face mirror (TIGER-Lab/MMLU-Pro, \"default\" config) holds 12,032 test questions (used for scoring) plus a separate 70-question validation split reserved for few-shot prompting -- 12,102 rows total. Of the 12,032 test questions, 6,810 are difficulty-filtered survivors of the original MMLU test set and 5,222 are newly collected, per the dataset card's own count. Math (1,351) and Physics (1,299) are the largest of the 14 categories; History (381) and Computer Science (410) are the smallest.\n",
   "splits": "validation (70, few-shot CoT source), test (12,032, scored)",
   "url": "https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice N"
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "mmlu_pro",
   "lm_eval": "mmlu_pro (group of 14 mmlu_pro_<category> tasks; generate_until scoring with regex answer-letter extraction, 5-shot from the validation split by default)",
   "opencompass": "mmlu_pro (0-shot CoT, few-shot and generic-LLM-judge config variants)",
   "other": "Reference evaluation scripts and cached model predictions are maintained in TIGER-AI-Lab/MMLU-Pro on GitHub (eval_results/)."
  },
  "id": "mmlu_pro",
  "last_updated": "2026-01",
  "leaderboard_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
  "lineage": {
   "family": "mmlu",
   "predecessor": "mmlu",
   "successors": [],
   "variants": []
  },
  "measures": "MMLU-Pro gives a model a question and up to ten labelled answer options (most questions use all ten; a small number carry fewer after manual review removed unreasonable distractors), drawn from 14 broad categories spanning STEM, humanities, social sciences and business/health/other topics: Biology, Business, Chemistry, Computer Science, Economics, Engineering, Health, History, Law, Math, Philosophy, Physics, Psychology and Other. The model must select the single correct option. Roughly 57% of the questions are a difficulty-filtered subset of the original MMLU test set (with \"trivial and ambiguous\" items removed); the remaining 43% are newly written from a STEM question website, TheoremQA and SciBench, with GPT-4-generated distractors expanding every question from four options toward ten, reviewed afterward by a panel of over ten subject-matter experts.\n",
  "metric": {
   "baseline_note": "10% is the ten-option random-guess rate, versus 25% on the original four-option MMLU -- one reason raw scores are not comparable between the two benchmarks. No human baseline has been published for MMLU-Pro.\n",
   "direction": "higher_is_better",
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": 10,
   "unit": "%"
  },
  "name": "MMLU-Pro",
  "page_kind": "benchmark",
  "paper": {
   "arxiv": "2406.01574",
   "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark",
   "url": "https://arxiv.org/abs/2406.01574",
   "year": 2024
  },
  "publisher": {
   "authors": [
    "Yubo Wang",
    "Xueguang Ma",
    "Ge Zhang",
    "Yuansheng Ni",
    "Abhranil Chandra",
    "Shiguang Guo",
    "Weiming Ren",
    "Aaran Arulraj",
    "Xuan He",
    "Ziyan Jiang",
    "Tianle Li",
    "Max Ku",
    "Kai Wang",
    "Alex Zhuang",
    "Rongqi Fan",
    "Xiang Yue",
    "Wenhu Chen"
   ],
   "org": "TIGER Lab, University of Waterloo (with co-authors at the University of Toronto and Carnegie Mellon University)",
   "url": "https://github.com/TIGER-AI-Lab/MMLU-Pro"
  },
  "released": "2024-06",
  "repo_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
  "saturation": {
   "as_of": "2026-09",
   "note": "The paper's own GPT-4o baseline scored 72.55% (CoT) at launch in June 2024. The public leaderboard's top TIGER-Lab-verified row, as read for this page in September 2026, credits Gemini-3.1-Pro (added to the leaderboard 2026-03 per the dataset card changelog) with 91.16% overall -- a roughly 19-point rise in a little over two years. Per-category scores still spread models out (one verified run showed category scores from 73.6% to 93.0%), so the benchmark has not collapsed the way original MMLU did, but the pace of the climb plus a January 2026 fix for an exploitable formatting shortcut in the answer options are both worth watching.\n",
   "status": "open",
   "top_score": 91.16
  },
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark (Wang et al., arXiv:2406.01574)",
    "url": "https://arxiv.org/abs/2406.01574"
   },
   {
    "accessed": "2026-09-08",
    "title": "TIGER-AI-Lab/MMLU-Pro GitHub repository (reference code, eval scripts, licence)",
    "url": "https://github.com/TIGER-AI-Lab/MMLU-Pro"
   },
   {
    "accessed": "2026-09-08",
    "title": "TIGER-Lab/MMLU-Pro dataset card, Hugging Face",
    "url": "https://huggingface.co/datasets/TIGER-Lab/MMLU-Pro"
   },
   {
    "accessed": "2026-09-08",
    "title": "MMLU-Pro Leaderboard, Hugging Face Space",
    "url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro"
   },
   {
    "accessed": "2026-09-08",
    "title": "lm-evaluation-harness mmlu_pro task README (groups, tasks, changelog)",
    "url": "https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/mmlu_pro/README.md"
   },
   {
    "accessed": "2026-09-08",
    "title": "inspect_evals mmlu_pro task source",
    "url": "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/mmlu_pro"
   },
   {
    "accessed": "2026-09-08",
    "title": "OpenCompass mmlu_pro dataset configs",
    "url": "https://github.com/open-compass/opencompass/tree/main/opencompass/configs/datasets/mmlu_pro"
   },
   {
    "accessed": "2026-09-08",
    "title": "TIGER-Lab/MMLU-Pro datasets-server size API",
    "url": "https://datasets-server.huggingface.co/size?dataset=TIGER-Lab/MMLU-Pro"
   }
  ],
  "status": "active",
  "subcategory": "multitask academic and professional knowledge (reasoning-augmented)",
  "summary": "A harder, ten-option successor to MMLU with about 12,000 reasoning-heavy questions across 14 categories, built to restore headroom lost to MMLU's saturation at the frontier.",
  "tags": [
   "knowledge",
   "multiple-choice",
   "multitask",
   "chain-of-thought",
   "ten-option",
   "five-shot"
  ],
  "task_format": "Multiple-choice question answering with up to ten labelled options, graded on the single option the model selects. The reference protocol is 5-shot with chain-of-thought prompting, with fewshot examples drawn from a dedicated 70-question validation split; scoring extracts the answer letter from free-form generated text (for example via a regex matching \"the answer is (X)\") rather than comparing option log-likelihoods, because the authors found direct/log-likelihood scoring under-performs chain-of-thought by up to 19 points on this dataset -- the opposite of the original MMLU. Across 24 prompt styles the authors tested, score sensitivity to prompt wording fell from 4-5% on MMLU to about 2% on MMLU-Pro.\n"
 }
}