MMLU-Redux
Manually re-annotated subset of MMLU that fixes wrong labels. Multiple-choice accuracy.
Saturated. This first release has 3,000 questions in 30 subjects. The authors now recommend MMLU-Redux 2.0, and cards that write only MMLU-Redux do not say which version.
In the authors’ words
Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed questions in the Virology subset contain errors. To address this issue, we introduce a comprehensive framework for identifying dataset errors using a novel error annotation protocol. Then, we create MMLU-Redux, which is a subset of 5,700 manually re-annotated questions across all 57 MMLU subjects. We estimate that 6.49% of MMLU questions contain errors. Using MMLU-Redux, we demonstrate significant discrepancies with the model performance metrics that were originally reported. Our results strongly advocate for revising MMLU's error-ridden questions to enhance its future utility and reliability as a benchmark. https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux-2.0.
- Level
- University Undergraduates and entry-level professionals
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
Qwen3.7-Max leads with 95% among models released by Jun 2026
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.
The newest model with a score here came out in Jun 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 95.0
Qwen3.7-Max
- 94.9
Qwen3.5 397B-A17B
- 94.5
Qwen 3.6 Plus
- 94.5
Qwen3.7-Plus
- 94.4 Kimi K2 Thinking
- 94.2
Qwen3.5-Omni-Plus
- 94.0
Qwen3.5-122B-A10B
- 93.8
Qwen3-235B-A22B-Thinking
- 93.7
DeepSeek-V3.1
- 93.7
Qwen3-VL-235B-A22B
- 93.5
Qwen3.6 27B
- 93.4
DeepSeek-R1
- 93.3
Qwen 3.6 35B-A3B
- 92.8
Qwen3-Max
- 92.7
Qwen3-235B-A22B
- 92.7 Kimi K2
- 92.5
Qwen3-Next-80B-A3B
- 92.3
EXAONE 4.0 32B
- 91.2
qwen3-coder-next
- 90.9
Qwen3-32B
- 90.8
DeepSeek-V4-Pro
- 89.5
Qwen3-30B-A3B
- 89.3
LongCat-Flash-Thinking
- 89.1
DeepSeek-V3
- 88.6
Qwen3-14B
- 87.5
Qwen3-8B
- 86.8
Qwen2.5-72B
- 86.6
Qwen3-Omni-30B-A3B
- 86.3
Nemotron-Cascade 2 30B-A3B
- 85.9
Qwen2.5-VL-72B
- 83.9
Qwen2.5-32B
- 83.7
Qwen3-4B
- 81.7
Qwen-Turbo
- 80.7
Qwen2.5-14B-Instruct-1M
- 80.0
Qwen2.5-14B
- 75.4
Qwen2.5-7B
- 73.9
Qwen3-1.7B
- 71.0
Qwen2.5-Omni-7B
- 1
Qwen3.7-Max 95%
- 2
Qwen3.5 397B-A17B 94.9%
- 3
Qwen 3.6 Plus 94.5%
- 4
Qwen3.7-Plus 94.5%
- 5 Kimi K2 Thinking 94.4%
- 6
Qwen3.5-Omni-Plus 94.2%
- 7
Qwen3.5-122B-A10B 94%
- 8
Qwen3-235B-A22B-Thinking Jul 2025 93.8%
- 9
DeepSeek-V3.1 93.7%
- 10
Qwen3-VL-235B-A22B 93.7%
- 11
Qwen3.6 27B 93.5%
- 12
DeepSeek-R1 93.4%
- 13
Qwen 3.6 35B-A3B 93.3%
- 14
Qwen3-Max 92.8%
- 15
Qwen3-235B-A22B 92.7%
- 16 Kimi K2 Jul 2025 92.7%
- 17
Qwen3-Next-80B-A3B 92.5%
- 18
EXAONE 4.0 32B 92.3%
- 19
qwen3-coder-next 91.2%
- 20
Qwen3-32B 90.9%
- 21
DeepSeek-V4-Pro 90.8%
- 22
Qwen3-30B-A3B 89.5%
- 23
LongCat-Flash-Thinking 89.3%
- 24
DeepSeek-V3 89.1%
- 25
Qwen3-14B 88.6%
- 26
Qwen3-8B 87.5%
- 27
Qwen2.5-72B 86.8%
- 28
Qwen3-Omni-30B-A3B 86.6%
- 29
Nemotron-Cascade 2 30B-A3B 86.3%
- 30
Qwen2.5-VL-72B 85.9%
- 31
Qwen2.5-32B 83.9%
- 32
Qwen3-4B 83.7%
- 33
Qwen-Turbo 81.7%
- 34
Qwen2.5-14B-Instruct-1M 80.7%
- 35
Qwen2.5-14B 80%
- 36
Qwen2.5-7B 75.4%
- 37
Qwen3-1.7B 73.9%
- 38
Qwen2.5-Omni-7B 71%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 95% | May 2026 | Model card | |
| 2 | | 94.9% | Feb 2026 | Model card | |
| 3 | | 94.5% | Apr 2026 | Model card | |
| 4 | | 94.5% | Jun 2026 | Model card | |
| 5 | Kimi K2 Thinking | 94.4% | Nov 2025 | Model card | |
| 6 | | 94.2% | Mar 2026 | Model card | |
| 7 | | 94% | Feb 2026 | Model card | |
| 8 | | 93.8% | Jul 2025 | Model card | |
| 9 | | 93.7% | Aug 2025 | Model card | |
| 10 | | 93.7% | Sep 2025 | Model card | |
| 11 | | 93.5% | Apr 2026 | Model card | |
| 12 | | 93.4% | May 2025 | Model card | |
| 13 | | 93.3% | Apr 2026 | Model card | |
| 14 | | 92.8% | Jan 2026 | Model card | |
| 15 | | 92.7% | Apr 2025 | Model card | |
| 16 | Kimi K2 Jul 2025 | 92.7% | Jul 2025 | Model card | |
| 17 | | 92.5% | Sep 2025 | Model card | |
| 18 | | 92.3% | Jul 2025 | Model card | |
| 19 | | 91.2% | Feb 2026 | Model card | |
| 20 | | 90.9% | May 2025 | Model card | |
| 21 | | 90.8% | Apr 2026 | Model card | |
| 22 | | 89.5% | May 2025 | Model card | |
| 23 | | 89.3% | Sep 2025 | Model card | |
| 24 | | 89.1% | Dec 2024 | Model card | |
| 25 | | 88.6% | May 2025 | Model card | |
| 26 | | 87.5% | May 2025 | Model card | |
| 27 | | 86.8% | Sep 2024 | Model card | |
| 28 | | 86.6% | Sep 2025 | Model card | |
| 29 | | 86.3% | Mar 2026 | Model card | |
| 30 | | 85.9% | Feb 2025 | Model card | |
| 31 | | 83.9% | Dec 2024 | Model card | |
| 32 | | 83.7% | May 2025 | Model card | |
| 33 | | 81.7% | Dec 2024 | Model card | |
| 34 | | 80.7% | Jan 2025 | Model card | |
| 35 | | 80% | Dec 2024 | Model card | |
| 36 | | 75.4% | Dec 2024 | Model card | |
| 37 | | 73.9% | May 2025 | Model card | |
| 38 | | 71% | Mar 2025 | Model card |
7 models have held number one since its release in Jun 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Sep 2024
All the scores we have, one dot each, so a model tested at several settings appears more than once.
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.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 1 Jun 2026 | 94.5 | MMLU-Redux | |
| 20 May 2026 | 95 | MMLU-Redux | |
| 24 Apr 2026 | 90.8 | MMLU-Redux (EM) | |
| 22 Apr 2026 | 93.5 | MMLU-Redux | |
| 15 Apr 2026 | 93.3 | MMLU-Redux | |
| 2 Apr 2026 | 94.5 | MMLU-Redux | |
| 30 Mar 2026 | 94.2 | MMLU-Redux | |
| 19 Mar 2026 | 86.3 | MMLU-Redux | |
| 24 Feb 2026 | 94 | MMLU-Redux | |
| 16 Feb 2026 | 94.9 | MMLU-Redux | |
| 23 Jan 2026 | 92.8 | MMLU-Redux | |
| 6 Nov 2025 | Kimi K2 Thinking | 94.4 | MMLU-Redux, no tools |
| 23 Sep 2025 | 93.7 | MMLU-Redux | |
| 22 Sep 2025 | 86.6 | MMLU-Redux | |
| 21 Sep 2025 | 89.3 | MMLU-Redux(acc) | |
| 11 Sep 2025 | 92.5 | MMLU-Redux | |
| 21 Aug 2025 | 93.7 | MMLU-Redux (EM) | |
| 25 Jul 2025 | 93.8 | MMLU-Redux | |
| 15 Jul 2025 | 92.3 | MMLU-Redux (reasoning) | |
| 11 Jul 2025 | Kimi K2 | 92.7 | MMLU-Redux, EM |
| 28 May 2025 | 93.4 | MMLU-Redux (EM) | |
| 29 Apr 2025 | 92.7 | MMLU-Redux | |
| 27 Mar 2025 | 71 | MMLU-redux | |
| 20 Jan 2025 | 92.9 | MMLU-Redux (EM) | |
| 26 Dec 2024 | 89.1 | MMLU-Redux (EM) | |
| 19 Sep 2024 | 86.8 | MMLU-redux |
25 system cards report it, from 6 labs
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 17 Apr 2026 | | 94.2 | MMLU-Redux (Table 4) | Capabilities |
| 19 Mar 2026 | | 86.3 | MMLU-Redux (Table 1) | Capabilities |
| 28 Feb 2026 | | 91.2 | MMLU-Redux (Table 8) | Capabilities |
| 26 Nov 2025 | | 93.7 | MMLU-Redux (Table 6) | Capabilities |
| 23 Sep 2025 | | 89.3 | MMLU-Redux(acc) (Table 2) | Capabilities |
| 22 Sep 2025 | | 86.6 | MMLU-Redux, Instruct (Table 4). Thinking 88.8 (Table 5) | Capabilities |
| 21 Jul 2025 | Kimi K2 technical report | 92.7 | MMLU-Redux (EM) (Table 3) | Capabilities |
| 15 Jul 2025 | | 92.3 | MMLU-Redux (Table 3, Reasoning). Non-reasoning 89.8 (Table 4) | Capabilities |
| 14 May 2025 | | 87.5 | MMLU-Redux, thinking mode (Table 17). Non-thinking 79.5 (Table 18) | Capabilities |
| 14 May 2025 | | 83.7 | MMLU-Redux, thinking mode (Table 17). Non-thinking 77.3 (Table 18) | Capabilities |
| 14 May 2025 | | 90.9 | MMLU-Redux, thinking mode (Table 13). Non-thinking 85.7 (Table 14) | Capabilities |
| 14 May 2025 | | 89.5 | MMLU-Redux, thinking mode (Table 15). Non-thinking 84.1 (Table 16) | Capabilities |
| 14 May 2025 | | 92.7 | MMLU-Redux, thinking mode (Table 11). Non-thinking 89.2 (Table 12) | Capabilities |
| 14 May 2025 | | 88.6 | MMLU-Redux, thinking mode (Table 15). Non-thinking 82.0 (Table 16) | Capabilities |
| 14 May 2025 | | 73.9 | MMLU-Redux, thinking mode (Table 19). Non-thinking 64.4 (Table 20) | Capabilities |
| 26 Mar 2025 | | 71 | MMLU-redux (Table 1) | Capabilities |
| 19 Feb 2025 | | 85.9 | MMLU-redux (Table 4) | Capabilities |
| 26 Jan 2025 | | 80.7 | MMLU-redux (Table 6) | Capabilities |
| 20 Jan 2025 | | 92.9 | MMLU-Redux (EM), DeepSeek-R1 (Table 8, same as final column of Table 3) | Capabilities |
| 26 Dec 2024 | | 89.1 | MMLU-Redux (EM), DeepSeek-V3 (Table 6) | Capabilities |
| 19 Dec 2024 | | 75.4 | MMLU-redux, Qwen2.5-7B-Instruct (Table 8) | Capabilities |
| 19 Dec 2024 | | 86.8 | MMLU-redux, Qwen2.5-72B-Instruct (Table 6) | Capabilities |
| 19 Dec 2024 | | 83.9 | MMLU-redux, Qwen2.5-32B-Instruct (Table 7) | Capabilities |
| 19 Dec 2024 | | 80 | MMLU-redux, Qwen2.5-14B-Instruct (Table 7) | Capabilities |
| 19 Dec 2024 | | 81.7 | MMLU-redux, Qwen2.5-Turbo (Table 7) | Capabilities |
Versions
More in Knowledge
- MMLU-Pro In use Harder 10-option version of MMLU with reasoning-focused questions across 14 subjects.
Intern-S2-397B 89.8%
- SuperGPQA In use 26,529 graduate-level multiple-choice questions across 285 disciplines. Accuracy.
Qwen 3.6 Max (Preview) 73.9%
- WideSearch In use 200 bilingual broad info-seeking tasks: agent collects many atomic facts into a table. Scored by success rate and row/item F1.
Tencent Hy4 preview 83.9%
- KMMLU-Pro In use Models answer multiple-choice questions taken from Korean National Professional Licensure exams. It tests professional knowledge used in Korean industry.
Solar Pro 4 79.2%
- BIG-Bench Extra Hard In use Models solve harder replacements for each of the 23 BIG-Bench Hard tasks, 4,520 examples in total. The score is a harmonic mean of task accuracies.
Gemma 4 31B IT 74.4%
- HLE-Verified In use Verified and revised version of Humanity's Last Exam (1,811 items) with uncertain items removed.
Seed2.0 Pro 73.6%