MMLU
57-subject multiple-choice exam questions from elementary to professional level. Scored by accuracy.
Saturated and contaminated. Reported only as a base-model metric.
In the authors’ words
We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
- 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
Seed1.8 leads with 92.3% among models released by Aug 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 Aug 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
- 92.3
Seed1.8
- 91.8 o1
- 91.7
Gemini 1.5 Pro
- 91.3
Cogito v2 Preview 671B MoE
- 91.0
Cogito v1 Preview 70B
- 90.8 o1-preview
- 90.8
DeepSeek-R1
- 90.6
Qwen3-VL-235B-A22B
- 90.6 Sarvam 105B
- 90.5 Claude 3.5 Sonnet (October 2024)
- 90.4 Claude 3.5 Sonnet
- 90.3
LongCat-Flash-Omni
- 90.1
DeepSeek-V4-Pro
- 90.0
Gemini 1.0 Ultra
- 90.0 gpt-oss-120b
- 90.0 GLM-4.5
- 89.7
LongCat-Flash-Chat
- 89.5
Hunyuan-TurboS
- 89.5 Kimi K2
- 89.1
InternVL3.5-241B-A28B
- 88.7 GPT-4o
- 88.6
Llama 3.1-405B
- 88.5
DeepSeek-V3
- 88.5
MiniMax-Text-01
- 88.3 GLM-5
- 88.2 Claude 3 Opus
- 88.2
Solar Open 100B
- 88.2
Solar Pro 2
- 87.7
qwen3-coder-next
- 87.7 Hermes 4.3 36B
- 87.5 Grok-2
- 87.5 GPT-4.1 mini
- 87.4 Kimi k1.5
- 87.4
Amazon Nova Premier
- 87.4 GLM-4.5-Air
- 87.4
seed-oss-36b-instruct
- 87.2
Trinity Large Preview
- 87.2 Hermes 4 405B
- 87.1
sonar
- 87.0
Tulu 3 405B
- 87.0 Sarvam-M
- 86.9 o3-mini
- 86.7 GPT-4 Turbo
- 86.5
ERNIE-4.5-300B-A47B
- 86.4 GPT-4
- 86.4
Olmo 3.1 32B Think
- 86.1
Qwen2.5-72B
- 86.0
Llama 3.1-70B
- 86.0
Llama 3.3 70B Instruct
- 85.5
Cohere Command A
- 85.5
Llama 4 Maverick
- 85.4
Olmo 3 32B Think
- 85.3 gpt-oss-20b
- 85.1 Sarvam 30B
- 85.0
Trinity Mini
- 84.8
Phi-4
- 84.0
Falcon-H1-34B
- 84.0
Mistral Large 2
- 83.0
EXAONE Deep 32B
- 82.3
Qwen2-72B
- 82.0
Llama 3-70B
- 82.0 GPT-4o mini
- 82.0 Kimi-VL-A3B-Thinking-2506
- 81.9
Gemini 1.5 Pro
- 81.5 GLM-4
- 81.5 Claude 3 Sonnet
- 81.3 Grok-1.5
- 81.2
Mistral Large
- 80.9 Claude 3.5 Haiku
- 80.7
Mistral Small 3
- 80.6
Mistral Small 3.1
- 80.5
Mistral Small 3.2
- 80.2 Apple Foundation Model Server (2025)
- 80.2 Granite 4.1 30B
- 80.1 GPT-4.1 nano
- 79.6
Llama 4 Scout
- 78.9
Phi-3.5-MoE
- 78.6
Gemma 3 27B
- 78.5 Claude 2
- 78.4 Granite 4.0 H Small
- 78.2
phi-3-medium 14B
- 77.8
DeepSeek-V2 (MoE-236B, May 2024)
- 77.7
Code World Model (CWM)
- 77.5
Qwen1.5-72B
- 77.3
OLMo 2 32B Instruct
- 76.7 Claude 3 Haiku
- 76.6
InternLM3-8B-Instruct
- 76.2
Gemma 2 27B
- 75.7
phi-3-small 7.4B
- 73.0 Grok-1
- 73.0
Llama 3.1-8B
- 72.5
Mixtral 8x7B
- 72.3
Gemma 2 9B
- 71.9
Gemma 3 12B
- 71.1
DeepSeek LLM 67B
- 70.0 Moonlight-16B-A3B
- 69.6
Apertus 70B
- 69.0
Phi-3.5-mini
- 68.9
Llama 2-70B
- 68.8
phi-3-mini 3.8B
- 68.4
Llama 3-8B
- 67.3
Phi-4 Mini
- 66.8 Granite 3.2 8B Instruct
- 66.5
Qwen-14B
- 65.9
AFM-4.5B
- 65.5 Granite 3.3 8B Instruct
- 64.9
Gemma 3n E4B
- 64.8 LFM2-8B-A1B
- 64.3
Gemma 7B
- 63.3
Falcon-Arabic-7B
- 62.6
Llama 2-34B
- 61.4 ChatGLM3-6B
- 60.1
Mistral 7B v0.1
- 59.4 granite-4.0-1b
- 58.1
Gemma 3 4B
- 57.0
Qwen-7B
- 56.7
Phi-2
- 55.2 LFM2-1.2B
- 54.8
Llama 2-13B
- 53.2
BitNet b1.58 2B4T
- 51.0 LFM2-VL-1.6B
- 50.4
North Micro Vision Instruct
- 45.3
Llama 2-7B
- 43.9 Qwen-1_8B
- 42.3
Gemma 2B
- 40.0
OLMo 2 1B Instruct
- 38.8
Gemma 3 1B
- 1
Seed1.8 92.3%
- 2 o1 91.8%
- 3
Gemini 1.5 Pro May 2024 91.7%
- 4
Cogito v2 Preview 671B MoE 91.3%
- 5
Cogito v1 Preview 70B 91%
- 6 o1-preview 90.8%
- 7
DeepSeek-R1 90.8%
- 8
Qwen3-VL-235B-A22B 90.6%
- 9 Sarvam 105B 90.6%
- 10 Claude 3.5 Sonnet (October 2024) 90.5%
- 11 Claude 3.5 Sonnet 90.4%
- 12
LongCat-Flash-Omni 90.3%
- 13
DeepSeek-V4-Pro 90.1%
- 14
Gemini 1.0 Ultra 90%
- 15 gpt-oss-120b 90%
- 16 GLM-4.5 90%
- 17
LongCat-Flash-Chat 89.7%
- 18
Hunyuan-TurboS 89.5%
- 19 Kimi K2 Jul 2025 89.5%
- 20
InternVL3.5-241B-A28B 89.1%
- 21 GPT-4o May 2024 88.7%
- 22
Llama 3.1-405B 88.6%
- 23
DeepSeek-V3 88.5%
- 24
MiniMax-Text-01 88.5%
- 25 GLM-5 88.3%
- 26 Claude 3 Opus 88.2%
- 27
Solar Open 100B 88.2%
- 28
Solar Pro 2 88.2%
- 29
qwen3-coder-next 87.7%
- 30 Hermes 4.3 36B 87.7%
- 31 Grok-2 Aug 2024 87.5%
- 32 GPT-4.1 mini 87.5%
- 33 Kimi k1.5 87.4%
- 34
Amazon Nova Premier 87.4%
- 35 GLM-4.5-Air 87.4%
- 36
seed-oss-36b-instruct 87.4%
- 37
Trinity Large Preview 87.2%
- 38 Hermes 4 405B 87.2%
- 39
sonar 87.1%
- 40
Tulu 3 405B 87%
- 41 Sarvam-M 87%
- 42 o3-mini 86.9%
- 43 GPT-4 Turbo Apr 2024 86.7%
- 44
ERNIE-4.5-300B-A47B 86.5%
- 45 GPT-4 Mar 2023 86.4%
- 46
Olmo 3.1 32B Think 86.4%
- 47
Qwen2.5-72B 86.1%
- 48
Llama 3.1-70B 86%
- 49
Llama 3.3 70B Instruct 86%
- 50
Cohere Command A 85.5%
- 51
Llama 4 Maverick 85.5%
- 52
Olmo 3 32B Think 85.4%
- 53 gpt-oss-20b 85.3%
- 54 Sarvam 30B 85.1%
- 55
Trinity Mini 85%
- 56
Phi-4 84.8%
- 57
Falcon-H1-34B 84%
- 58
Mistral Large 2 Jul 2024 84%
- 59
EXAONE Deep 32B 83%
- 60
Qwen2-72B 82.3%
- 61
Llama 3-70B 82%
- 62 GPT-4o mini 82%
- 63 Kimi-VL-A3B-Thinking-2506 82%
- 64
Gemini 1.5 Pro Feb 2024 81.9%
- 65 GLM-4 81.5%
- 66 Claude 3 Sonnet 81.5%
- 67 Grok-1.5 81.3%
- 68
Mistral Large 81.2%
- 69 Claude 3.5 Haiku 80.9%
- 70
Mistral Small 3 80.7%
- 71
Mistral Small 3.1 80.6%
- 72
Mistral Small 3.2 80.5%
- 73 Apple Foundation Model Server (2025) 80.2%
- 74 Granite 4.1 30B 80.2%
- 75 GPT-4.1 nano 80.1%
- 76
Llama 4 Scout 79.6%
- 77
Phi-3.5-MoE 78.9%
- 78
Gemma 3 27B 78.6%
- 79 Claude 2 78.5%
- 80 Granite 4.0 H Small 78.4%
- 81
phi-3-medium 14B 78.2%
- 82
DeepSeek-V2 (MoE-236B, May 2024) 77.8%
- 83
Code World Model (CWM) 77.7%
- 84
Qwen1.5-72B 77.5%
- 85
OLMo 2 32B Instruct 77.3%
- 86 Claude 3 Haiku 76.7%
- 87
InternLM3-8B-Instruct 76.6%
- 88
Gemma 2 27B 76.2%
- 89
phi-3-small 7.4B 75.7%
- 90 Grok-1 73%
- 91
Llama 3.1-8B 73%
- 92
Mixtral 8x7B 72.5%
- 93
Gemma 2 9B 72.3%
- 94
Gemma 3 12B 71.9%
- 95
DeepSeek LLM 67B 71.1%
- 96 Moonlight-16B-A3B 70%
- 97
Apertus 70B 69.6%
- 98
Phi-3.5-mini 69%
- 99
Llama 2-70B 68.9%
- 100
phi-3-mini 3.8B 68.8%
- 101
Llama 3-8B 68.4%
- 102
Phi-4 Mini 67.3%
- 103 Granite 3.2 8B Instruct 66.8%
- 104
Qwen-14B 66.5%
- 105
AFM-4.5B 65.9%
- 106 Granite 3.3 8B Instruct 65.5%
- 107
Gemma 3n E4B 64.9%
- 108 LFM2-8B-A1B 64.8%
- 109
Gemma 7B 64.3%
- 110
Falcon-Arabic-7B 63.3%
- 111
Llama 2-34B 62.6%
- 112 ChatGLM3-6B 61.4%
- 113
Mistral 7B v0.1 60.1%
- 114 granite-4.0-1b 59.4%
- 115
Gemma 3 4B 58.1%
- 116
Qwen-7B 57%
- 117
Phi-2 56.7%
- 118 LFM2-1.2B 55.2%
- 119
Llama 2-13B 54.8%
- 120
BitNet b1.58 2B4T 53.2%
- 121 LFM2-VL-1.6B 51%
- 122
North Micro Vision Instruct 50.4%
- 123
Llama 2-7B 45.3%
- 124 Qwen-1_8B 43.9%
- 125
Gemma 2B 42.3%
- 126
OLMo 2 1B Instruct 40%
- 127
Gemma 3 1B 38.8%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 92.3% | Dec 2025 | Model card | |
| 2 | o1 | 91.8% | Dec 2024 | Model card | |
| 3 | | 91.7% | May 2024 | Model card | |
| 4 | | 91.3% | Jul 2025 | Model card | |
| 5 | | 91% | Apr 2025 | Model card | |
| 6 | o1-preview | 90.8% | Sep 2024 | Model card | |
| 7 | | 90.8% | Jan 2025 | Model card | |
| 8 | | 90.6% | Sep 2025 | Model card | |
| 9 | Sarvam 105B | 90.6% | Mar 2026 | Model card | |
| 10 | Claude 3.5 Sonnet (October 2024) | 90.5% | Oct 2024 | Model card | |
| 11 | Claude 3.5 Sonnet | 90.4% | Jun 2024 | Model card | |
| 12 | | 90.3% | Oct 2025 | Model card | |
| 13 | | 90.1% | Apr 2026 | Model card | |
| 14 | | 90% | Dec 2023 | Model card | |
| 15 | gpt-oss-120b | 90% | Aug 2025 | Model card | |
| 16 | GLM-4.5 | 90% | Aug 2025 | Model card | |
| 17 | | 89.7% | Aug 2025 | Model card | |
| 18 | | 89.5% | Feb 2025 | Model card | |
| 19 | Kimi K2 Jul 2025 | 89.5% | Jul 2025 | Model card | |
| 20 | | 89.1% | Aug 2025 | Model card | |
| 21 | GPT-4o May 2024 | 88.7% | May 2024 | Model card | |
| 22 | | 88.6% | Jul 2024 | Model card | |
| 23 | | 88.5% | Dec 2024 | Model card | |
| 24 | | 88.5% | Jan 2025 | Model card | |
| 25 | GLM-5 | 88.3% | Feb 2026 | Model card | |
| 26 | Claude 3 Opus | 88.2% | Mar 2024 | Model card | |
| 27 | | 88.2% | Dec 2025 | Model card | |
| 28 | | 88.2% | Jul 2025 | Model card | |
| 29 | | 87.7% | Feb 2026 | Model card | |
| 30 | Hermes 4.3 36B | 87.7% | Dec 2025 | Model card | |
| 31 | Grok-2 Aug 2024 | 87.5% | Aug 2024 | Model card | |
| 32 | GPT-4.1 mini | 87.5% | Apr 2025 | Model card | |
| 33 | Kimi k1.5 | 87.4% | Jan 2025 | Model card | |
| 34 | | 87.4% | Apr 2025 | Model card | |
| 35 | GLM-4.5-Air | 87.4% | Aug 2025 | Model card | |
| 36 | | 87.4% | Aug 2025 | Model card | |
| 37 | | 87.2% | Feb 2026 | Model card | |
| 38 | Hermes 4 405B | 87.2% | Aug 2025 | Model card | |
| 39 | | 87.1% | Feb 2025 | Model card | |
| 40 | | 87% | Jan 2025 | Model card | |
| 41 | Sarvam-M | 87% | May 2025 | Model card | |
| 42 | o3-mini | 86.9% | Jan 2025 | Model card | |
| 43 | GPT-4 Turbo Apr 2024 | 86.7% | Apr 2024 | Model card | |
| 44 | | 86.5% | Jun 2025 | Model card | |
| 45 | GPT-4 Mar 2023 | 86.4% | Mar 2023 | Model card | |
| 46 | | 86.4% | Dec 2025 | Model card | |
| 47 | | 86.1% | Sep 2024 | Model card | |
| 48 | | 86% | Jul 2024 | Model card | |
| 49 | | 86% | Dec 2024 | Model card | |
| 50 | | 85.5% | Apr 2025 | Model card | |
| 51 | | 85.5% | Apr 2025 | Model card | |
| 52 | | 85.4% | Nov 2025 | Model card | |
| 53 | gpt-oss-20b | 85.3% | Aug 2025 | Model card | |
| 54 | Sarvam 30B | 85.1% | Mar 2026 | Model card | |
| 55 | | 85% | Dec 2025 | Model card | |
| 56 | | 84.8% | Dec 2024 | Model card | |
| 57 | | 84% | May 2025 | Model card | |
| 58 | | 84% | Jul 2024 | Model card | |
| 59 | | 83% | Mar 2025 | Model card | |
| 60 | | 82.3% | Jun 2024 | Model card | |
| 61 | | 82% | Apr 2024 | Model card | |
| 62 | GPT-4o mini | 82% | Jul 2024 | Model card | |
| 63 | Kimi-VL-A3B-Thinking-2506 | 82% | Jun 2025 | Model card | |
| 64 | | 81.9% | Feb 2024 | Model card | |
| 65 | GLM-4 | 81.5% | Jan 2024 | Model card | |
| 66 | Claude 3 Sonnet | 81.5% | Mar 2024 | Model card | |
| 67 | Grok-1.5 | 81.3% | Mar 2024 | Model card | |
| 68 | | 81.2% | Feb 2024 | Model card | |
| 69 | Claude 3.5 Haiku | 80.9% | Oct 2024 | Model card | |
| 70 | | 80.7% | Jan 2025 | Model card | |
| 71 | | 80.6% | Mar 2025 | Model card | |
| 72 | | 80.5% | Jun 2025 | Model card | |
| 73 | Apple Foundation Model Server (2025) | 80.2% | Jul 2025 | Model card | |
| 74 | Granite 4.1 30B | 80.2% | Apr 2026 | Model card | |
| 75 | GPT-4.1 nano | 80.1% | Apr 2025 | Model card | |
| 76 | | 79.6% | Apr 2025 | Model card | |
| 77 | | 78.9% | Aug 2024 | Model card | |
| 78 | | 78.6% | Mar 2025 | Model card | |
| 79 | Claude 2 | 78.5% | Jul 2023 | Model card | |
| 80 | Granite 4.0 H Small | 78.4% | Oct 2025 | Model card | |
| 81 | | 78.2% | Apr 2024 | Model card | |
| 82 | | 77.8% | May 2024 | Model card | |
| 83 | | 77.7% | Sep 2025 | Model card | |
| 84 | | 77.5% | Feb 2024 | Model card | |
| 85 | | 77.3% | Mar 2025 | Model card | |
| 86 | Claude 3 Haiku | 76.7% | Mar 2024 | Model card | |
| 87 | | 76.6% | Jan 2025 | Model card | |
| 88 | | 76.2% | Jul 2024 | Model card | |
| 89 | | 75.7% | Aug 2024 | Model card | |
| 90 | Grok-1 | 73% | Nov 2023 | Model card | |
| 91 | | 73% | Jul 2024 | Model card | |
| 92 | | 72.5% | Dec 2023 | Model card | |
| 93 | | 72.3% | Jul 2024 | Model card | |
| 94 | | 71.9% | Mar 2025 | Model card | |
| 95 | | 71.1% | Nov 2023 | Model card | |
| 96 | Moonlight-16B-A3B | 70% | Feb 2025 | Model card | |
| 97 | | 69.6% | Sep 2025 | Model card | |
| 98 | | 69% | Aug 2024 | Model card | |
| 99 | | 68.9% | Jul 2023 | Model card | |
| 100 | | 68.8% | Aug 2024 | Model card | |
| 101 | | 68.4% | Apr 2024 | Model card | |
| 102 | | 67.3% | Feb 2025 | Model card | |
| 103 | Granite 3.2 8B Instruct | 66.8% | Feb 2025 | Model card | |
| 104 | | 66.5% | Sep 2023 | Model card | |
| 105 | | 65.9% | Jul 2025 | Model card | |
| 106 | Granite 3.3 8B Instruct | 65.5% | Apr 2025 | Model card | |
| 107 | | 64.9% | Jun 2025 | Model card | |
| 108 | LFM2-8B-A1B | 64.8% | Oct 2025 | Model card | |
| 109 | | 64.3% | Feb 2024 | Model card | |
| 110 | | 63.3% | Nov 2025 | Model card | |
| 111 | | 62.6% | Jul 2023 | Model card | |
| 112 | ChatGLM3-6B | 61.4% | Oct 2023 | Model card | |
| 113 | | 60.1% | Sep 2023 | Model card | |
| 114 | granite-4.0-1b | 59.4% | Oct 2025 | Model card | |
| 115 | | 58.1% | Mar 2025 | Model card | |
| 116 | | 57% | Sep 2023 | Model card | |
| 117 | | 56.7% | Dec 2023 | Model card | |
| 118 | LFM2-1.2B | 55.2% | Jul 2025 | Model card | |
| 119 | | 54.8% | Jul 2023 | Model card | |
| 120 | | 53.2% | Apr 2025 | Model card | |
| 121 | LFM2-VL-1.6B | 51% | Aug 2025 | Model card | |
| 122 | | 50.4% | Aug 2026 | Model card | |
| 123 | | 45.3% | Jul 2023 | Model card | |
| 124 | Qwen-1_8B | 43.9% | Sep 2023 | Model card | |
| 125 | | 42.3% | Mar 2024 | Model card | |
| 126 | | 40% | Apr 2025 | Model card | |
| 127 | | 38.8% | Mar 2025 | Model card |
Epoch AI has scores for 127 models. We plot every source in the charts below.
6 models have held number one since 2023
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for Epoch AI’s runs and what labs claim in their cards.
The best score passed 85% in Mar 2023
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 Aug 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 |
|---|---|---|---|
| 12 Aug 2026 | 50.4 | MMLU test | |
| 29 Apr 2026 | Granite 4.1 30B | 80.2 | MMLU (5-shot) |
| 24 Apr 2026 | 90.1 | MMLU (EM) | |
| 6 Mar 2026 | Sarvam 30B | 85.1 | MMLU |
| 6 Mar 2026 | Sarvam 105B | 90.6 | MMLU |
| 27 Jan 2026 | 87.2 | MMLU | |
| 31 Dec 2025 | 88.2 | MMLU | |
| 18 Dec 2025 | 92.3 | MMLU | |
| 12 Dec 2025 | 86.4 | MMLU | |
| 3 Dec 2025 | Hermes 4.3 36B | 87.7 | MMLU |
| 1 Dec 2025 | 85 | MMLU (Zero Shot) | |
| 20 Nov 2025 | 85.4 | MMLU | |
| 31 Oct 2025 | 90.3 | MMLU(acc) | |
| 28 Oct 2025 | granite-4.0-1b | 59.4 | MMLU (5-shot) |
| 7 Oct 2025 | LFM2-8B-A1B | 64.8 | MMLU |
| 2 Oct 2025 | Granite 4.0 H Small | 78.4 | MMLU (5-shot) |
| 23 Sep 2025 | 90.6 | MMLU | |
| 29 Aug 2025 | 89.7 | MMLU(acc) | |
| 26 Aug 2025 | Hermes 4 405B | 87.2 | MMLU (reasoning) |
| 20 Aug 2025 | 87.4 | MMLU | |
| 12 Aug 2025 | LFM2-VL-1.6B | 51 | Text MMLU |
| 5 Aug 2025 | gpt-oss-20b | 85.3 | MMLU |
| 5 Aug 2025 | gpt-oss-120b | 90 | MMLU Questions across academic disciplines |
| 31 Jul 2025 | 91.3 | MMLU (thinking) | |
| 29 Jul 2025 | 65.9 | MMLU | |
| 11 Jul 2025 | Kimi K2 | 89.5 | MMLU, EM |
| 10 Jul 2025 | LFM2-1.2B | 55.2 | MMLU |
| 10 Jul 2025 | 88.2 | MMLU (0-shot, CoT) | |
| 30 Jun 2025 | 86.5 | MMLU | |
| 26 Jun 2025 | 64.9 | MMLU 0-shot (IT) | |
| 21 Jun 2025 | Kimi-VL-A3B-Thinking-2506 | 82 | MMLU |
| 20 Jun 2025 | 80.5 | MMLU | |
| 23 May 2025 | Sarvam-M | 87 | MMLU (0.87) |
| 20 May 2025 | 84 | MMLU | |
| 30 Apr 2025 | 87.4 | MMLU | |
| 29 Apr 2025 | 40 | MMLU | |
| 16 Apr 2025 | Granite 3.3 8B Instruct | 65.5 | MMLU |
| 15 Apr 2025 | 53.2 | MMLU | |
| 14 Apr 2025 | GPT-4.1 nano | 80.1 | MMLU |
| 14 Apr 2025 | GPT-4.1 mini | 87.5 | MMLU |
| 8 Apr 2025 | 91 | MMLU (thinking) | |
| 5 Apr 2025 | 79.6 | MMLU (5-shot) [pretrained] | |
| 5 Apr 2025 | 85.5 | MMLU (5-shot) [pretrained] | |
| 17 Mar 2025 | 80.6 | MMLU | |
| 13 Mar 2025 | MMLU (chart only) | ||
| 13 Mar 2025 | 77.3 | MMLU | |
| 12 Mar 2025 | 78.6 | MMLU 5-shot (PT) | |
| 27 Feb 2025 | 89.5 | MMLU | |
| 26 Feb 2025 | 67.3 | MMLU (5-shot) | |
| 26 Feb 2025 | Granite 3.2 8B Instruct | 66.8 | MMLU |
| 22 Feb 2025 | Moonlight-16B-A3B | 70 | MMLU |
| 11 Feb 2025 | 87.1 | MMLU (0-shot) | |
| 31 Jan 2025 | o3-mini | 86.9 | MMLU (pass@1) |
| 30 Jan 2025 | 87 | MMLU (5 shot, CoT) | |
| 30 Jan 2025 | 80.7 | Best of MMLU (5-shot) [base] / French MMLU [base] / German MMLU [base] / Spanish MMLU [base] | |
| 28 Jan 2025 | MMLU | ||
| 20 Jan 2025 | Kimi k1.5 | 87.4 | MMLU (EM), short-CoT |
| 20 Jan 2025 | 90.8 | MMLU (Pass@1) | |
| 15 Jan 2025 | 88.5 | MMLU* (0-shot CoT) | |
| 13 Jan 2025 | 76.6 | MMLU(0-shot) | |
| 26 Dec 2024 | 88.5 | MMLU (EM) | |
| 17 Dec 2024 | o1 | 91.8 | MMLU (pass @1) |
| 12 Dec 2024 | 84.8 | MMLU (simple-evals) | |
| 6 Dec 2024 | 86 | MMLU (CoT), 0-shot, macro_avg/acc | |
| 22 Oct 2024 | Claude 3.5 Sonnet (October 2024) | 90.5 | MMLU (5-shot CoT) |
| 19 Sep 2024 | 86.1 | MMLU | |
| 12 Sep 2024 | o1-preview | 90.8 | MMLU pass@1 |
| 20 Aug 2024 | 78.9 | MMLU (5-shot) | |
| 13 Aug 2024 | Grok-2 | 87.5 | MMLU (0-shot CoT) |
| 24 Jul 2024 | 84 | Best of MMLU [base pretrained] / Multilingual MMLU French [base] / Multilingual MMLU German [base] / Multilingual MMLU Spanish [base] / Multilingual MMLU Italian [base] | |
| 23 Jul 2024 | 88.6 | MMLU (0-shot, CoT) | |
| 18 Jul 2024 | GPT-4o mini | 82 | MMLU |
| 27 Jun 2024 | 75.2 | MMLU (5-shot, top-1) | |
| 20 Jun 2024 | Claude 3.5 Sonnet | 90.4 | MMLU (5-shot CoT) |
| 7 Jun 2024 | 82.3 | MMLU | |
| 14 May 2024 | 91.7 | MMLU, maj@32 (pretrained) (5-shot = 85.9%) | |
| 13 May 2024 | GPT-4o | 88.7 | MMLU (0-shot CoT) |
| 7 May 2024 | 77.8 | MMLU | |
| 22 Apr 2024 | 78.2 | MMLU (5-Shot) | |
| 18 Apr 2024 | 82 | MMLU (5-shot) [instruct] | |
| 11 Apr 2024 | GPT-4 Turbo | 86.7 | MMLU% |
| 28 Mar 2024 | Grok-1.5 | 81.3 | MMLU (5-shot) |
| 4 Mar 2024 | Claude 3 Opus | 88.2 | MMLU (5-shot CoT) |
| 26 Feb 2024 | 81.2 | Best of MMLU / French MMLU / German MMLU / Spanish MMLU / Italian MMLU | |
| 21 Feb 2024 | 64.3 | MMLU (5-shot, top-1) | |
| 15 Feb 2024 | 81.9 | MMLU, 5-shot | |
| 5 Feb 2024 | 77.5 | MMLU | |
| 16 Jan 2024 | GLM-4 | 81.5 | MMLU (5-shot) |
| 12 Dec 2023 | 56.7 | MMLU, Table 2 | |
| 11 Dec 2023 | 72.5 | Best of MMLU (MCQ in 57 subjects) / French MMLU / German MMLU / Spanish MMLU / Italian MMLU | |
| 6 Dec 2023 | 90 | MMLU, CoT@32 (5-shot = 83.7%) | |
| 29 Nov 2023 | 71.1 | MMLU | |
| 3 Nov 2023 | Grok-1 | 73 | MMLU (5-shot) |
| 27 Oct 2023 | ChatGLM3-6B | 61.4 | MMLU |
| 27 Sep 2023 | 60.1 | MMLU | |
| 25 Sep 2023 | 66.5 | MMLU (5-shot) | |
| 18 Jul 2023 | 68.9 | MMLU | |
| 11 Jul 2023 | Claude 2 | 78.5 | MMLU (5-shot CoT) |
| 14 Mar 2023 | GPT-4 | 86.4 | MMLU (5-shot) |
87 system cards report it, from 26 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 |
|---|---|---|---|---|
| 21 Mar 2026 | | 92.3 | MMLU (Table 1) | Capabilities |
| 28 Feb 2026 | | 87.7 | MMLU (Table 8) | Capabilities |
| 19 Feb 2026 | | 87.2 | MMLU, Trinity Large Preview (Table 4) | Capabilities |
| 17 Feb 2026 | GLM-5 technical report | 88.3 | GLM-5-Base, MMLU (EM) (Table 11) | Capabilities |
| 11 Jan 2026 | | 88.2 | MMLU (Table 6) | Capabilities |
| 15 Dec 2025 | | 85.4 | MMLU, Final Think 3.0 column (Table 14) | Capabilities |
| 15 Dec 2025 | | 86.4 | MMLU (Tables 1 and 14) | Capabilities |
| 28 Nov 2025 | LFM2-8B-A1B technical report | 64.8 | MMLU, LFM2-8B-A1B (Table 7) | Capabilities |
| 28 Nov 2025 | LFM2-1.2B technical report | 55.2 | MMLU, LFM2-1.2B (Table 6) | Capabilities |
| 8 Nov 2025 | | 63.3 | MMLU 5-shot (Table 8) | Capabilities |
| 31 Oct 2025 | | 90.3 | MMLU (acc) (Table 13) | Capabilities |
| 8 Oct 2025 | | 40 | MMLU (Table 7, Table 23) | Capabilities |
| 30 Sep 2025 | | 77.7 | MMLU, without reasoning (Table 13) | Capabilities |
| 17 Sep 2025 | | 69.6 | MMLU, 0-shot CoT exact match (Table 17) | Capabilities |
| 1 Sep 2025 | | 89.7 | MMLU (acc), model-judged answer matching (Table 3) | Capabilities |
| 25 Aug 2025 | | 89.1 | MMLU, OpenCompass (Table 14) | Capabilities |
| 25 Aug 2025 | Hermes 4 405B technical report | 87.2 | MMLU, reasoning mode (Table 3). Non-reasoning 73.6 | Capabilities |
| 8 Aug 2025 | GLM-4.5 technical report | 90 | MMLU (EM) (Table 6) | Capabilities |
| 8 Aug 2025 | GLM-4.5-Air technical report | 87.4 | MMLU (EM) (Table 6) | Capabilities |
| 5 Aug 2025 | gpt-oss-20b model card | 85.3 | MMLU, high reasoning (Table 3) | Capabilities |
| 5 Aug 2025 | gpt-oss-120b model card | 90 | MMLU, high reasoning (Table 3) | Capabilities |
| 30 Jul 2025 | | 84 | MMLU, logprobs 5-shot (Table 24) | Capabilities |
| 21 Jul 2025 | Kimi K2 technical report | 89.5 | MMLU (EM) (Table 3) | Capabilities |
| 17 Jul 2025 | | MMLU 5-shot, text-only, chart without printed numbers (Figure 6). Speech-synthesized MMLU* 74.3 (Table 8) | Capabilities | |
| 17 Jul 2025 | Apple Foundation Model Server (2025) technical report | 80.2 | MMLU, Simple-Evals, AFM Server (Table 2). Table 3 prints 80.0 at 16 bits and 79.2 for AFM Server Opt at 3.6 bits per weight | Capabilities |
| 29 Jun 2025 | | 86.5 | MMLU (Table 5) | Capabilities |
| 30 Apr 2025 | | 87.4 | MMLU, 0-shot CoT, macro average (Table 1) | Capabilities |
| 16 Apr 2025 | | 53.2 | MMLU (5-shot; Acc) (Table 1, Table 2, Table 3) | Capabilities |
| 5 Apr 2025 | | 79.6 | MMLU, 5-shot, macro_avg/acc_char, pretrained (Pre-trained models table) | Capabilities |
| 5 Apr 2025 | | 85.5 | MMLU, 5-shot, macro_avg/acc_char, pretrained (Pre-trained models table) | Capabilities |
| 1 Apr 2025 | | 85.5 | MMLU, 5-shot CoT, simple-evals (Table 3, Table 1) | Capabilities |
| 25 Mar 2025 | | 58.1 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 76.9 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 38.8 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| 25 Mar 2025 | | 71.9 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| 16 Mar 2025 | | 83 | MMLU, accuracy (Table 3) | Capabilities |
| 3 Mar 2025 | | 67.3 | MMLU (5-Shot) (Table 7) | Capabilities |
| 20 Jan 2025 | Kimi k1.5 technical report | 87.4 | MMLU (EM), short-CoT (Table 3) | Capabilities |
| 20 Jan 2025 | | 90.8 | MMLU (EM), DeepSeek-R1 (Table 8, same as final column of Table 3) | Capabilities |
| 14 Jan 2025 | | 88.5 | MMLU, 0-shot CoT (Table 8) | Capabilities |
| 31 Dec 2024 | | 77.3 | MMLU, 0-shot CoT (Tables 7 and 16) | Capabilities |
| 26 Dec 2024 | | 88.5 | MMLU (EM), DeepSeek-V3 (Table 6) | Capabilities |
| 12 Dec 2024 | | 84.8 | MMLU, simple-evals (Table 1) | Capabilities |
| 6 Dec 2024 | | 86 | MMLU (CoT), 0-shot, macro_avg/acc (Instruction tuned models table) | Capabilities |
| 23 Nov 2024 | | 73 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 69.4 | Capabilities |
| 23 Nov 2024 | | 86 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 83.6 | Capabilities |
| 23 Nov 2024 | | 88.6 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 87.3 | Capabilities |
| 22 Nov 2024 | | 87 | MMLU (5 shot, CoT), RLVR (Table 4) | Capabilities |
| 22 Oct 2024 | Claude 3.5 Sonnet (October 2024) model card | 90.5 | MMLU, 5-shot CoT. 5-shot 88.7%, 0-shot CoT 89.3% (Table 7) | Capabilities |
| 22 Oct 2024 | Claude 3.5 Haiku model card | 80.9 | MMLU, 5-shot CoT. 5-shot 77.6%, 0-shot CoT 80.3% (Table 8) | Capabilities |
| 30 Aug 2024 | | 75.7 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| 30 Aug 2024 | | 68.8 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| 30 Aug 2024 | | 78 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| 30 Aug 2024 | | 78.9 | MMLU (5-shot) (Table 3) | Capabilities |
| 30 Aug 2024 | | 69 | MMLU (5-shot) (Table 3) | Capabilities |
| 31 Jul 2024 | | 72.3 | MMLU, IT, 5-shot (Table 17). PT 5-shot (Table 13): 71.3 | Capabilities |
| 31 Jul 2024 | | 76.2 | MMLU, IT, 5-shot (Table 17). PT 5-shot (Table 13): 75.2 | Capabilities |
| 23 Jul 2024 | | 73 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 69.4 | Capabilities |
| 23 Jul 2024 | | 86 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 83.6 | Capabilities |
| 23 Jul 2024 | | 88.6 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 87.3 | Capabilities |
| 15 Jul 2024 | | 82.3 | MMLU, Qwen2-72B-Instruct (Table 6). 83.2 on the non-contaminated subset (Table 15) | Capabilities |
| 20 Jun 2024 | Claude 3.5 Sonnet model card | 90.4 | MMLU, 5-shot CoT. 5-shot 88.7, 0-shot CoT 88.3 (Table 1) | Capabilities |
| 18 Jun 2024 | GLM-4 technical report | 81.5 | GLM-4 (0116), MMLU (Table 2) | Capabilities |
| 18 Jun 2024 | ChatGLM3-6B technical report | 61.4 | ChatGLM3-6B-Base, MMLU (Table 1) | Capabilities |
| 7 May 2024 | | 77.8 | MMLU 5-shot, DeepSeek-V2 Chat (RL) (Table 3) | Capabilities |
| 18 Apr 2024 | | 68.4 | MMLU (5-shot), instruction tuned (Instruction tuned models table). Pretrained = 66.6 | Capabilities |
| 18 Apr 2024 | | 82 | MMLU (5-shot), instruction tuned (Instruction tuned models table). Pretrained = 79.5 | Capabilities |
| 13 Mar 2024 | | 64.3 | MMLU, PT, 5-shot top-1 (Table 6). HuggingFace H6 run (Table 7): 64.6 | Capabilities |
| 13 Mar 2024 | | 42.3 | MMLU, PT, 5-shot top-1 (Table 6) | Capabilities |
| 4 Mar 2024 | Claude 3 Sonnet model card | 81.5 | MMLU, 5-shot CoT. 5-shot 79.0 (Table 1) | Capabilities |
| 4 Mar 2024 | Claude 3 Opus model card | 88.2 | MMLU, 5-shot CoT. 5-shot 86.8 (Table 1) | Capabilities |
| 4 Mar 2024 | Claude 3 Haiku model card | 76.7 | MMLU, 5-shot CoT. 5-shot 75.2 (Table 1) | Capabilities |
| 8 Jan 2024 | | 70.6 | MMLU (MCQ in 57 subjects), 5-shot (Table 3; same in Table 2) | Capabilities |
| 5 Jan 2024 | | 71.1 | MMLU 0-shot, DeepSeek 67B Chat (Table 6) | Capabilities |
| 10 Oct 2023 | | 60.1 | MMLU, 5-shot (Table 2) | Capabilities |
| 28 Sep 2023 | | 57 | MMLU 5-shot, higher of the two settings, 0-shot 55.8 (Table 5) | Capabilities |
| 28 Sep 2023 | | 66.5 | MMLU 5-shot, higher of the two settings, 0-shot 64.6 (Table 5) | Capabilities |
| 28 Sep 2023 | | 43.9 | MMLU 5-shot, higher of the two settings, 0-shot 42.4 (Table 5) | Capabilities |
| 19 Jul 2023 | | 45.3 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| 19 Jul 2023 | | 68.9 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| 19 Jul 2023 | | 62.6 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| 19 Jul 2023 | | 54.8 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| 18 Jul 2023 | | 45.3 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| 18 Jul 2023 | | 68.9 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| 18 Jul 2023 | | 54.8 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| 11 Jul 2023 | Claude 2 model card | 78.5 | MMLU (5-shot CoT) (4.3) | Capabilities |
| 14 Mar 2023 | GPT-4 technical report | 86.4 | MMLU, 5-shot (Table 2, base model) | 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%