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.
- Made by
- UC Berkeley (Hendrycks et al.)
- Released
- September 2020
- Kind
- Exam questions
- Level
- University
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
- Strongest model tested
-
DeepSeek-V4-Pro #69 on the capability index
- Last in a launch post
- August 2026, dropped after it saturated
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
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.
Known issues
A published audit found a problem in MMLU. We summarize each finding in our words and link its source. The scores on this page don't include any fixes. See all audits
- Wrong labels Unclear tasks Fixed in a copy
Gema et al. report numerous ground-truth errors in MMLU, with Virology the worst subject they analyzed. They re-annotated 5,700 questions across all 57 subjects as MMLU-Redux and estimate the error rate for all of MMLU.
57% of the analyzed Virology questions contain errors, and the authors estimate that 6.49% of all MMLU questions contain errors.
For example, we find that 57% of the analysed questions in the Virology subset contain errors.
Are We Done with MMLU? by Gema et al., June 6th, 2024
- MMLU-Redux 2.0 in the almanac
- MMLU-Redux 2.0 (5,700 re-annotated questions)
Seed1.8 leads with 92.3%
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.
Gopher (280B) leads with 60% across all independent runs
Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.
Seed1.8 leads with 92.3%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 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
- 89.1
Nemotron 3 Ultra 550B-A55B Base
- 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
- 84.1
Nemotron 3.5 Super 120B-A12B Base
- 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
Nemotron 3.5 Lightning 30B-A3B Base
- 78.6
Gemma 3 27B
- 78.6
Nemotron 3 Nano 30B-A3B Base
- 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
Nemotron 3 Ultra 550B-A55B Base 89.1%
- 22 GPT-4o May 2024 88.7%
- 23
Llama 3.1-405B 88.6%
- 24
DeepSeek-V3 88.5%
- 25
MiniMax-Text-01 88.5%
- 26 GLM-5 88.3%
- 27 Claude 3 Opus 88.2%
- 28
Solar Open 100B 88.2%
- 29
Solar Pro 2 88.2%
- 30
Qwen3-Coder-Next 87.7%
- 31 Hermes 4.3 36B 87.7%
- 32 Grok-2 Aug 2024 87.5%
- 33 GPT-4.1 mini 87.5%
- 34 Kimi k1.5 87.4%
- 35
Amazon Nova Premier 87.4%
- 36 GLM-4.5-Air 87.4%
- 37
seed-oss-36b-instruct 87.4%
- 38
Trinity Large Preview 87.2%
- 39 Hermes 4 405B 87.2%
- 40
sonar 87.1%
- 41
Tulu 3 405B 87%
- 42 Sarvam-M 87%
- 43 o3-mini 86.9%
- 44 GPT-4 Turbo Apr 2024 86.7%
- 45
ERNIE-4.5-300B-A47B 86.5%
- 46 GPT-4 Mar 2023 86.4%
- 47
Olmo 3.1 32B Think 86.4%
- 48
Qwen2.5-72B 86.1%
- 49
Llama 3.1-70B 86%
- 50
Llama 3.3 70B Instruct 86%
- 51
Cohere Command A 85.5%
- 52
Llama 4 Maverick 85.5%
- 53
Olmo 3 32B Think 85.4%
- 54 gpt-oss-20b 85.3%
- 55 Sarvam 30B 85.1%
- 56
Trinity Mini 85%
- 57
Phi-4 84.8%
- 58
Nemotron 3.5 Super 120B-A12B Base 84.1%
- 59
Falcon-H1-34B 84%
- 60
Mistral Large 2 Jul 2024 84%
- 61
EXAONE Deep 32B 83%
- 62
Qwen2-72B 82.3%
- 63
Llama 3-70B 82%
- 64 GPT-4o mini 82%
- 65 Kimi-VL-A3B-Thinking-2506 82%
- 66
Gemini 1.5 Pro Feb 2024 81.9%
- 67 GLM-4 81.5%
- 68 Claude 3 Sonnet 81.5%
- 69 Grok-1.5 81.3%
- 70
Mistral Large 81.2%
- 71 Claude 3.5 Haiku 80.9%
- 72
Mistral Small 3 80.7%
- 73
Mistral Small 3.1 80.6%
- 74
Mistral Small 3.2 80.5%
- 75 Apple Foundation Model Server (2025) 80.2%
- 76 Granite 4.1 30B 80.2%
- 77 GPT-4.1 nano 80.1%
- 78
Llama 4 Scout 79.6%
- 79
Phi-3.5-MoE 78.9%
- 80
Nemotron 3.5 Lightning 30B-A3B Base 78.6%
- 81
Gemma 3 27B 78.6%
- 82
Nemotron 3 Nano 30B-A3B Base 78.6%
- 83 Claude 2 78.5%
- 84 Granite 4.0 H Small 78.4%
- 85
phi-3-medium 14B 78.2%
- 86
DeepSeek-V2 (MoE-236B, May 2024) 77.8%
- 87
Code World Model (CWM) 77.7%
- 88
Qwen1.5-72B 77.5%
- 89
OLMo 2 32B Instruct 77.3%
- 90 Claude 3 Haiku 76.7%
- 91
InternLM3-8B-Instruct 76.6%
- 92
Gemma 2 27B 76.2%
- 93
phi-3-small 7.4B 75.7%
- 94 Grok-1 73%
- 95
Llama 3.1-8B 73%
- 96
Mixtral 8x7B 72.5%
- 97
Gemma 2 9B 72.3%
- 98
Gemma 3 12B 71.9%
- 99
DeepSeek LLM 67B 71.1%
- 100 Moonlight-16B-A3B 70%
- 101
Apertus 70B 69.6%
- 102
Phi-3.5-mini 69%
- 103
Llama 2-70B 68.9%
- 104
phi-3-mini 3.8B 68.8%
- 105
Llama 3-8B 68.4%
- 106
Phi-4 Mini 67.3%
- 107 Granite 3.2 8B Instruct 66.8%
- 108
Qwen-14B 66.5%
- 109
AFM-4.5B 65.9%
- 110 Granite 3.3 8B Instruct 65.5%
- 111
Gemma 3n E4B 64.9%
- 112 LFM2-8B-A1B 64.8%
- 113
Gemma 7B 64.3%
- 114
Falcon-Arabic-7B 63.3%
- 115
Llama 2-34B 62.6%
- 116 ChatGLM3-6B 61.4%
- 117
Mistral 7B v0.1 60.1%
- 118 granite-4.0-1b 59.4%
- 119
Gemma 3 4B 58.1%
- 120
Qwen-7B 57%
- 121
Phi-2 56.7%
- 122 LFM2-1.2B 55.2%
- 123
Llama 2-13B 54.8%
- 124
BitNet b1.58 2B4T 53.2%
- 125 LFM2-VL-1.6B 51%
- 126
North Micro Vision Instruct 50.4%
- 127
Llama 2-7B 45.3%
- 128 Qwen-1_8B 43.9%
- 129
Gemma 2B 42.3%
- 130
OLMo 2 1B Instruct 40%
- 131
Gemma 3 1B 38.8%
Source: Labs’ model cards
Source: Official MMLU leaderboard
- 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
- 89.1
Nemotron 3 Ultra 550B-A55B Base
- 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
- 88.1 GPT-4o
- 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
- 86.3
Llama 3.3 70B
- 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
- 84.1
Nemotron 3.5 Super 120B-A12B Base
- 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
- 79.9
Qwen2.5-14B
- 79.6
Llama 4 Scout
- 79.1
Qwen2.5-Coder-32B
- 78.9
Phi-3.5-MoE
- 78.6
Nemotron 3.5 Lightning 30B-A3B Base
- 78.6
Gemma 3 27B
- 78.6
Nemotron 3 Nano 30B-A3B Base
- 78.5 Claude 2
- 78.4 Granite 4.0 H Small
- 78.4
DeepSeek-V2 (MoE-236B, May 2024)
- 78.2
phi-3-medium 14B
- 77.7
Code World Model (CWM)
- 77.5
Qwen1.5-72B
- 77.3
OLMo 2 32B Instruct
- 77.0 Claude 1.3
- 76.7 Claude 3 Haiku
- 76.6
InternLM3-8B-Instruct
- 76.3
Yi-34B
- 76.2
Gemma 2 27B
- 75.7
phi-3-small 7.4B
- 75.2 Qwen2.5-Coder-14B
- 73.4 Claude Instant
- 73.0 Grok-1
- 73.0
Llama 3.1-8B
- 72.7 Inflection-1
- 72.5
Mixtral 8x7B
- 72.3
Gemma 2 9B
- 71.9
Gemma 3 12B
- 71.4 GPT-3.5 Turbo
- 71.1
DeepSeek LLM 67B
- 70.6
Falcon-180B
- 70.0 davinci-002
- 70.0 Moonlight-16B-A3B
- 69.9
Llama 2-70B
- 69.6
Apertus 70B
- 69.3
PaLM (540B)
- 69.0
Phi-3.5-mini
- 68.8
phi-3-mini 3.8B
- 68.6
Stable Beluga 2
- 68.4 Yi-9B
- 68.4
Llama 3-8B
- 68.0
Qwen2.5-Coder (7B)
- 67.5
Chinchilla
- 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
- 64.1
StarCoder 2 15B
- 63.4
LLaMA-65B
- 63.3
Falcon-Arabic-7B
- 63.2
Yi 6B
- 62.6
Llama 2-34B
- 62.5
Mistral 7B v0.1
- 62.5
Mistral 7B v0.2
- 61.4 ChatGLM3-6B
- 60.5 DeepSeek-Coder-V2-Lite-Base
- 60.0
Gopher (280B)
- 59.4 granite-4.0-1b
- 59.2
Baichuan2-13B
- 58.7
LLaMA-33B
- 58.7
Nemotron-4 15B
- 58.4
Falcon 2 11B
- 58.1
Gemma 3 4B
- 57.4 internlm-chat-20b
- 57.0
Qwen-7B
- 56.9
Falcon-40B
- 56.7
Phi-2
- 55.6
Llama 2-13B
- 55.2 LFM2-1.2B
- 55.1 Baichuan2-13B-Chat
- 54.2
Baichuan 2-7B
- 53.7 PaLM 62B
- 53.6
Qwen2.5-Coder (1.5B)
- 53.2
BitNet b1.58 2B4T
- 51.6
Baichuan 1-13B
- 51.0 internlm-7b
- 51.0 LFM2-VL-1.6B
- 50.4
North Micro Vision Instruct
- 49.9 INTELLECT-1
- 47.9
MPT-30B
- 47.9 chatglm2-6b
- 47.7
LLaMA-13B
- 45.7
Llama 2-7B
- 43.9 InstructGPT 175B
- 43.9 Qwen-1_8B
- 43.2 GPT-3 6.7B
- 42.3
Baichuan1-7B
- 42.3
Gemma 2B
- 42.0 Qwen2.5-Coder-0.5B
- 40.5 CodeQwen1.5-7B
- 40.0
OLMo 2 1B Instruct
- 39.4
DeepSeek Coder 33B
- 38.8
StarCoder 2 7B
- 38.8
Gemma 3 1B
- 37.6
Phi-1.5
- 36.6
StarCoder 2 3B
- 36.4
DeepSeek Coder 6.7B
- 36.3
XGen-7B
- 35.6
LLaMA-7B
- 35.0
Falcon-7B
- 30.8
MPT-7B
- 29.9 open_llama_7b
- 26.3 RedPajama-INCITE-7B-Base
- 26.2
Cerebras-GPT-13B
- 26.2
Dolly 2.0-12b
- 25.8
DeepSeek Coder 1.3B
- 25.7 GPT-J-6B
- 25.1
OPT-1.3B
- 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
Nemotron 3 Ultra 550B-A55B Base 89.1%
- 22 GPT-4o May 2024 88.7%
- 23
Llama 3.1-405B 88.6%
- 24
DeepSeek-V3 88.5%
- 25
MiniMax-Text-01 88.5%
- 26 GLM-5 88.3%
- 27 Claude 3 Opus 88.2%
- 28
Solar Open 100B 88.2%
- 29
Solar Pro 2 88.2%
- 30 GPT-4o Nov 2024 88.1%
- 31
Qwen3-Coder-Next 87.7%
- 32 Hermes 4.3 36B 87.7%
- 33 Grok-2 Aug 2024 87.5%
- 34 GPT-4.1 mini 87.5%
- 35 Kimi k1.5 87.4%
- 36
Amazon Nova Premier 87.4%
- 37 GLM-4.5-Air 87.4%
- 38
seed-oss-36b-instruct 87.4%
- 39
Trinity Large Preview 87.2%
- 40 Hermes 4 405B 87.2%
- 41
sonar 87.1%
- 42
Tulu 3 405B 87%
- 43 Sarvam-M 87%
- 44 o3-mini 86.9%
- 45 GPT-4 Turbo Apr 2024 86.7%
- 46
ERNIE-4.5-300B-A47B 86.5%
- 47 GPT-4 Mar 2023 86.4%
- 48
Olmo 3.1 32B Think 86.4%
- 49
Llama 3.3 70B 86.3%
- 50
Qwen2.5-72B 86.1%
- 51
Llama 3.1-70B 86%
- 52
Llama 3.3 70B Instruct 86%
- 53
Cohere Command A 85.5%
- 54
Llama 4 Maverick 85.5%
- 55
Olmo 3 32B Think 85.4%
- 56 gpt-oss-20b 85.3%
- 57 Sarvam 30B 85.1%
- 58
Trinity Mini 85%
- 59
Phi-4 84.8%
- 60
Nemotron 3.5 Super 120B-A12B Base 84.1%
- 61
Falcon-H1-34B 84%
- 62
Mistral Large 2 Jul 2024 84%
- 63
EXAONE Deep 32B 83%
- 64
Qwen2-72B 82.3%
- 65
Llama 3-70B 82%
- 66 GPT-4o mini 82%
- 67 Kimi-VL-A3B-Thinking-2506 82%
- 68
Gemini 1.5 Pro Feb 2024 81.9%
- 69 GLM-4 81.5%
- 70 Claude 3 Sonnet 81.5%
- 71 Grok-1.5 81.3%
- 72
Mistral Large 81.2%
- 73 Claude 3.5 Haiku 80.9%
- 74
Mistral Small 3 80.7%
- 75
Mistral Small 3.1 80.6%
- 76
Mistral Small 3.2 80.5%
- 77 Apple Foundation Model Server (2025) 80.2%
- 78 Granite 4.1 30B 80.2%
- 79 GPT-4.1 nano 80.1%
- 80
Qwen2.5-14B 79.9%
- 81
Llama 4 Scout 79.6%
- 82
Qwen2.5-Coder-32B 79.1%
- 83
Phi-3.5-MoE 78.9%
- 84
Nemotron 3.5 Lightning 30B-A3B Base 78.6%
- 85
Gemma 3 27B 78.6%
- 86
Nemotron 3 Nano 30B-A3B Base 78.6%
- 87 Claude 2 78.5%
- 88 Granite 4.0 H Small 78.4%
- 89
DeepSeek-V2 (MoE-236B, May 2024) 78.4%
- 90
phi-3-medium 14B 78.2%
- 91
Code World Model (CWM) 77.7%
- 92
Qwen1.5-72B 77.5%
- 93
OLMo 2 32B Instruct 77.3%
- 94 Claude 1.3 77%
- 95 Claude 3 Haiku 76.7%
- 96
InternLM3-8B-Instruct 76.6%
- 97
Yi-34B 76.3%
- 98
Gemma 2 27B 76.2%
- 99
phi-3-small 7.4B 75.7%
- 100 Qwen2.5-Coder-14B 75.2%
- 101 Claude Instant 73.4%
- 102 Grok-1 73%
- 103
Llama 3.1-8B 73%
- 104 Inflection-1 72.7%
- 105
Mixtral 8x7B 72.5%
- 106
Gemma 2 9B 72.3%
- 107
Gemma 3 12B 71.9%
- 108 GPT-3.5 Turbo Nov 2023 71.4%
- 109
DeepSeek LLM 67B 71.1%
- 110
Falcon-180B 70.6%
- 111 davinci-002 70%
- 112 Moonlight-16B-A3B 70%
- 113
Llama 2-70B 69.9%
- 114
Apertus 70B 69.6%
- 115
PaLM (540B) 69.3%
- 116
Phi-3.5-mini 69%
- 117
phi-3-mini 3.8B 68.8%
- 118
Stable Beluga 2 68.6%
- 119 Yi-9B 68.4%
- 120
Llama 3-8B 68.4%
- 121
Qwen2.5-Coder (7B) 68%
- 122
Chinchilla 67.5%
- 123
Phi-4 Mini 67.3%
- 124 Granite 3.2 8B Instruct 66.8%
- 125
Qwen-14B 66.5%
- 126
AFM-4.5B 65.9%
- 127 Granite 3.3 8B Instruct 65.5%
- 128
Gemma 3n E4B 64.9%
- 129 LFM2-8B-A1B 64.8%
- 130
Gemma 7B 64.3%
- 131
StarCoder 2 15B 64.1%
- 132
LLaMA-65B 63.4%
- 133
Falcon-Arabic-7B 63.3%
- 134
Yi 6B 63.2%
- 135
Llama 2-34B 62.6%
- 136
Mistral 7B v0.1 62.5%
- 137
Mistral 7B v0.2 62.5%
- 138 ChatGLM3-6B 61.4%
- 139 DeepSeek-Coder-V2-Lite-Base 60.5%
- 140
Gopher (280B) few-shot 60%
- 141 granite-4.0-1b 59.4%
- 142
Baichuan2-13B 59.2%
- 143
LLaMA-33B 58.7%
- 144
Nemotron-4 15B 58.7%
- 145
Falcon 2 11B 58.4%
- 146
Gemma 3 4B 58.1%
- 147 internlm-chat-20b 57.4%
- 148
Qwen-7B 57%
- 149
Falcon-40B 56.9%
- 150
Phi-2 56.7%
- 151
Llama 2-13B 55.6%
- 152 LFM2-1.2B 55.2%
- 153 Baichuan2-13B-Chat 55.1%
- 154
Baichuan 2-7B 54.2%
- 155 PaLM 62B 53.7%
- 156
Qwen2.5-Coder (1.5B) 53.6%
- 157
BitNet b1.58 2B4T 53.2%
- 158
Baichuan 1-13B 51.6%
- 159 internlm-7b 51%
- 160 LFM2-VL-1.6B 51%
- 161
North Micro Vision Instruct 50.4%
- 162 INTELLECT-1 49.9%
- 163
MPT-30B 47.9%
- 164 chatglm2-6b 47.9%
- 165
LLaMA-13B 47.7%
- 166
Llama 2-7B 45.7%
- 167 InstructGPT 175B 43.9%
- 168 Qwen-1_8B 43.9%
- 169 GPT-3 6.7B fine-tuned 43.2%
- 170
Baichuan1-7B 42.3%
- 171
Gemma 2B 42.3%
- 172 Qwen2.5-Coder-0.5B 42%
- 173 CodeQwen1.5-7B 40.5%
- 174
OLMo 2 1B Instruct 40%
- 175
DeepSeek Coder 33B 39.4%
- 176
StarCoder 2 7B 38.8%
- 177
Gemma 3 1B 38.8%
- 178
Phi-1.5 37.6%
- 179
StarCoder 2 3B 36.6%
- 180
DeepSeek Coder 6.7B 36.4%
- 181
XGen-7B 36.3%
- 182
LLaMA-7B 35.6%
- 183
Falcon-7B 35%
- 184
MPT-7B 30.8%
- 185 open_llama_7b 29.9%
- 186 RedPajama-INCITE-7B-Base 26.3%
- 187
Cerebras-GPT-13B 26.2%
- 188
Dolly 2.0-12b 26.2%
- 189
DeepSeek Coder 1.3B 25.8%
- 190 GPT-J-6B 25.7%
- 191
OPT-1.3B 25.1%
Sources: Labs’ model cards, Another board, via Epoch AI (CC BY 4.0) and official MMLU leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 92.3% | Dec 2025 | Lab model card | |
| 2 | o1 | 91.8% | Dec 2024 | Lab model card | |
| 3 | | 91.7% | May 2024 | Lab model card | |
| 4 | | 91.3% | Jul 2025 | Lab model card | |
| 5 | | 91% | Apr 2025 | Lab model card | |
| 6 | o1-preview | 90.8% | Sep 2024 | Lab model card | |
| 7 | | 90.8% | Jan 2025 | Lab model card | |
| 8 | | 90.6% | Sep 2025 | Lab model card | |
| 9 | Sarvam 105B | 90.6% | Mar 2026 | Lab model card | |
| 10 | Claude 3.5 Sonnet (October 2024) | 90.5% | Oct 2024 | Lab model card | |
| 11 | Claude 3.5 Sonnet | 90.4% | Jun 2024 | Lab model card | |
| 12 | | 90.3% | Oct 2025 | Lab model card | |
| 13 | | 90.1% | Apr 2026 | Lab model card | |
| 14 | | 90% | Dec 2023 | Lab model card | |
| 15 | gpt-oss-120b | 90% | Aug 2025 | Lab model card | |
| 16 | GLM-4.5 | 90% | Aug 2025 | Lab model card | |
| 17 | | 89.7% | Aug 2025 | Lab model card | |
| 18 | | 89.5% | Feb 2025 | Lab model card | |
| 19 | Kimi K2 Jul 2025 | 89.5% | Jul 2025 | Lab model card | |
| 20 | | 89.1% | Aug 2025 | Lab model card | |
| 21 | | 89.1% | Jun 2026 | Lab model card | |
| 22 | GPT-4o May 2024 | 88.7% | May 2024 | Lab model card | |
| 23 | | 88.6% | Jul 2024 | Lab model card | |
| 24 | | 88.5% | Dec 2024 | Lab model card | |
| 25 | | 88.5% | Jan 2025 | Lab model card | |
| 26 | GLM-5 | 88.3% | Feb 2026 | Lab model card | |
| 27 | Claude 3 Opus | 88.2% | Mar 2024 | Lab model card | |
| 28 | | 88.2% | Dec 2025 | Lab model card | |
| 29 | | 88.2% | Jul 2025 | Lab model card | |
| 30 | | 87.7% | Feb 2026 | Lab model card | |
| 31 | Hermes 4.3 36B | 87.7% | Dec 2025 | Lab model card | |
| 32 | Grok-2 Aug 2024 | 87.5% | Aug 2024 | Lab model card | |
| 33 | GPT-4.1 mini | 87.5% | Apr 2025 | Lab model card | |
| 34 | Kimi k1.5 | 87.4% | Jan 2025 | Lab model card | |
| 35 | | 87.4% | Apr 2025 | Lab model card | |
| 36 | GLM-4.5-Air | 87.4% | Aug 2025 | Lab model card | |
| 37 | | 87.4% | Aug 2025 | Lab model card | |
| 38 | | 87.2% | Feb 2026 | Lab model card | |
| 39 | Hermes 4 405B | 87.2% | Aug 2025 | Lab model card | |
| 40 | | 87.1% | Feb 2025 | Lab model card | |
| 41 | | 87% | Jan 2025 | Lab model card | |
| 42 | Sarvam-M | 87% | May 2025 | Lab model card | |
| 43 | o3-mini | 86.9% | Jan 2025 | Lab model card | |
| 44 | GPT-4 Turbo Apr 2024 | 86.7% | Apr 2024 | Lab model card | |
| 45 | | 86.5% | Jun 2025 | Lab model card | |
| 46 | GPT-4 Mar 2023 | 86.4% | Mar 2023 | Lab model card | |
| 47 | | 86.4% | Dec 2025 | Lab model card | |
| 48 | | 86.1% | Sep 2024 | Lab model card | |
| 49 | | 86% | Jul 2024 | Lab model card | |
| 50 | | 86% | Dec 2024 | Lab model card | |
| 51 | | 85.5% | Apr 2025 | Lab model card | |
| 52 | | 85.5% | Apr 2025 | Lab model card | |
| 53 | | 85.4% | Nov 2025 | Lab model card | |
| 54 | gpt-oss-20b | 85.3% | Aug 2025 | Lab model card | |
| 55 | Sarvam 30B | 85.1% | Mar 2026 | Lab model card | |
| 56 | | 85% | Dec 2025 | Lab model card | |
| 57 | | 84.8% | Dec 2024 | Lab model card | |
| 58 | | 84.1% | Oct 2026 | Lab model card | |
| 59 | | 84% | May 2025 | Lab model card | |
| 60 | | 84% | Jul 2024 | Lab model card | |
| 61 | | 83% | Mar 2025 | Lab model card | |
| 62 | | 82.3% | Jun 2024 | Lab model card | |
| 63 | | 82% | Apr 2024 | Lab model card | |
| 64 | GPT-4o mini | 82% | Jul 2024 | Lab model card | |
| 65 | Kimi-VL-A3B-Thinking-2506 | 82% | Jun 2025 | Lab model card | |
| 66 | | 81.9% | Feb 2024 | Lab model card | |
| 67 | GLM-4 | 81.5% | Jan 2024 | Lab model card | |
| 68 | Claude 3 Sonnet | 81.5% | Mar 2024 | Lab model card | |
| 69 | Grok-1.5 | 81.3% | Mar 2024 | Lab model card | |
| 70 | | 81.2% | Feb 2024 | Lab model card | |
| 71 | Claude 3.5 Haiku | 80.9% | Oct 2024 | Lab model card | |
| 72 | | 80.7% | Jan 2025 | Lab model card | |
| 73 | | 80.6% | Mar 2025 | Lab model card | |
| 74 | | 80.5% | Jun 2025 | Lab model card | |
| 75 | Apple Foundation Model Server (2025) | 80.2% | Jul 2025 | Lab model card | |
| 76 | Granite 4.1 30B | 80.2% | Apr 2026 | Lab model card | |
| 77 | GPT-4.1 nano | 80.1% | Apr 2025 | Lab model card | |
| 78 | | 79.6% | Apr 2025 | Lab model card | |
| 79 | | 78.9% | Aug 2024 | Lab model card | |
| 80 | | 78.6% | Oct 2026 | Lab model card | |
| 81 | | 78.6% | Mar 2025 | Lab model card | |
| 82 | | 78.6% | Dec 2025 | Lab model card | |
| 83 | Claude 2 | 78.5% | Jul 2023 | Lab model card | |
| 84 | Granite 4.0 H Small | 78.4% | Oct 2025 | Lab model card | |
| 85 | | 78.2% | Apr 2024 | Lab model card | |
| 86 | | 77.8% | May 2024 | Lab model card | |
| 87 | | 77.7% | Sep 2025 | Lab model card | |
| 88 | | 77.5% | Feb 2024 | Lab model card | |
| 89 | | 77.3% | Mar 2025 | Lab model card | |
| 90 | Claude 3 Haiku | 76.7% | Mar 2024 | Lab model card | |
| 91 | | 76.6% | Jan 2025 | Lab model card | |
| 92 | | 76.2% | Jul 2024 | Lab model card | |
| 93 | | 75.7% | Aug 2024 | Lab model card | |
| 94 | Grok-1 | 73% | Nov 2023 | Lab model card | |
| 95 | | 73% | Jul 2024 | Lab model card | |
| 96 | | 72.5% | Dec 2023 | Lab model card | |
| 97 | | 72.3% | Jul 2024 | Lab model card | |
| 98 | | 71.9% | Mar 2025 | Lab model card | |
| 99 | | 71.1% | Nov 2023 | Lab model card | |
| 100 | Moonlight-16B-A3B | 70% | Feb 2025 | Lab model card | |
| 101 | | 69.6% | Sep 2025 | Lab model card | |
| 102 | | 69% | Aug 2024 | Lab model card | |
| 103 | | 68.9% | Jul 2023 | Lab model card | |
| 104 | | 68.8% | Aug 2024 | Lab model card | |
| 105 | | 68.4% | Apr 2024 | Lab model card | |
| 106 | | 67.3% | Feb 2025 | Lab model card | |
| 107 | Granite 3.2 8B Instruct | 66.8% | Feb 2025 | Lab model card | |
| 108 | | 66.5% | Sep 2023 | Lab model card | |
| 109 | | 65.9% | Jul 2025 | Lab model card | |
| 110 | Granite 3.3 8B Instruct | 65.5% | Apr 2025 | Lab model card | |
| 111 | | 64.9% | Jun 2025 | Lab model card | |
| 112 | LFM2-8B-A1B | 64.8% | Oct 2025 | Lab model card | |
| 113 | | 64.3% | Feb 2024 | Lab model card | |
| 114 | | 63.3% | Nov 2025 | Lab model card | |
| 115 | | 62.6% | Jul 2023 | Lab model card | |
| 116 | ChatGLM3-6B | 61.4% | Oct 2023 | Lab model card | |
| 117 | | 60.1% | Sep 2023 | Lab model card | |
| 118 | granite-4.0-1b | 59.4% | Oct 2025 | Lab model card | |
| 119 | | 58.1% | Mar 2025 | Lab model card | |
| 120 | | 57% | Sep 2023 | Lab model card | |
| 121 | | 56.7% | Dec 2023 | Lab model card | |
| 122 | LFM2-1.2B | 55.2% | Jul 2025 | Lab model card | |
| 123 | | 54.8% | Jul 2023 | Lab model card | |
| 124 | | 53.2% | Apr 2025 | Lab model card | |
| 125 | LFM2-VL-1.6B | 51% | Aug 2025 | Lab model card | |
| 126 | | 50.4% | Aug 2026 | Lab model card | |
| 127 | | 45.3% | Jul 2023 | Lab model card | |
| 128 | Qwen-1_8B | 43.9% | Sep 2023 | Lab model card | |
| 129 | | 42.3% | Mar 2024 | Lab model card | |
| 130 | | 40% | Apr 2025 | Lab model card | |
| 131 | | 38.8% | Mar 2025 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 60% | few-shot | Dec 2021 | Official leaderboard |
| 2 | GPT-3 6.7B | 43.2% | fine-tuned | Jun 2020 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 92.3% | Dec 2025 | Lab model card | |
| 2 | o1 | 91.8% | Dec 2024 | Lab model card | |
| 3 | | 91.7% | May 2024 | Lab model card | |
| 4 | | 91.3% | Jul 2025 | Lab model card | |
| 5 | | 91% | Apr 2025 | Lab model card | |
| 6 | o1-preview | 90.8% | Sep 2024 | Lab model card | |
| 7 | | 90.8% | Jan 2025 | Lab model card | |
| 8 | | 90.6% | Sep 2025 | Lab model card | |
| 9 | Sarvam 105B | 90.6% | Mar 2026 | Lab model card | |
| 10 | Claude 3.5 Sonnet (October 2024) | 90.5% | Oct 2024 | Lab model card | |
| 11 | Claude 3.5 Sonnet | 90.4% | Jun 2024 | Lab model card | |
| 12 | | 90.3% | Oct 2025 | Lab model card | |
| 13 | | 90.1% | Apr 2026 | Lab model card | |
| 14 | | 90% | Dec 2023 | Lab model card | |
| 15 | gpt-oss-120b | 90% | Aug 2025 | Lab model card | |
| 16 | GLM-4.5 | 90% | Aug 2025 | Lab model card | |
| 17 | | 89.7% | Aug 2025 | Lab model card | |
| 18 | | 89.5% | Feb 2025 | Lab model card | |
| 19 | Kimi K2 Jul 2025 | 89.5% | Jul 2025 | Lab model card | |
| 20 | | 89.1% | Aug 2025 | Lab model card | |
| 21 | | 89.1% | Jun 2026 | Lab model card | |
| 22 | GPT-4o May 2024 | 88.7% | May 2024 | Lab model card | |
| 23 | | 88.6% | Jul 2024 | Lab model card | |
| 24 | | 88.5% | Dec 2024 | Lab model card | |
| 25 | | 88.5% | Jan 2025 | Lab model card | |
| 26 | GLM-5 | 88.3% | Feb 2026 | Lab model card | |
| 27 | Claude 3 Opus | 88.2% | Mar 2024 | Lab model card | |
| 28 | | 88.2% | Dec 2025 | Lab model card | |
| 29 | | 88.2% | Jul 2025 | Lab model card | |
| 30 | GPT-4o Nov 2024 | 88.1% | Nov 2024 | Claim in Phi-4 Technical Report, via Epoch AI | |
| 31 | | 87.7% | Feb 2026 | Lab model card | |
| 32 | Hermes 4.3 36B | 87.7% | Dec 2025 | Lab model card | |
| 33 | Grok-2 Aug 2024 | 87.5% | Aug 2024 | Lab model card | |
| 34 | GPT-4.1 mini | 87.5% | Apr 2025 | Lab model card | |
| 35 | Kimi k1.5 | 87.4% | Jan 2025 | Lab model card | |
| 36 | | 87.4% | Apr 2025 | Lab model card | |
| 37 | GLM-4.5-Air | 87.4% | Aug 2025 | Lab model card | |
| 38 | | 87.4% | Aug 2025 | Lab model card | |
| 39 | | 87.2% | Feb 2026 | Lab model card | |
| 40 | Hermes 4 405B | 87.2% | Aug 2025 | Lab model card | |
| 41 | | 87.1% | Feb 2025 | Lab model card | |
| 42 | | 87% | Jan 2025 | Lab model card | |
| 43 | Sarvam-M | 87% | May 2025 | Lab model card | |
| 44 | o3-mini | 86.9% | Jan 2025 | Lab model card | |
| 45 | GPT-4 Turbo Apr 2024 | 86.7% | Apr 2024 | Lab model card | |
| 46 | | 86.5% | Jun 2025 | Lab model card | |
| 47 | GPT-4 Mar 2023 | 86.4% | Mar 2023 | Lab model card | |
| 48 | | 86.4% | Dec 2025 | Lab model card | |
| 49 | | 86.3% | Dec 2024 | Claim in Phi-4 Technical Report, via Epoch AI | |
| 50 | | 86.1% | Sep 2024 | Lab model card | |
| 51 | | 86% | Jul 2024 | Lab model card | |
| 52 | | 86% | Dec 2024 | Lab model card | |
| 53 | | 85.5% | Apr 2025 | Lab model card | |
| 54 | | 85.5% | Apr 2025 | Lab model card | |
| 55 | | 85.4% | Nov 2025 | Lab model card | |
| 56 | gpt-oss-20b | 85.3% | Aug 2025 | Lab model card | |
| 57 | Sarvam 30B | 85.1% | Mar 2026 | Lab model card | |
| 58 | | 85% | Dec 2025 | Lab model card | |
| 59 | | 84.8% | Dec 2024 | Lab model card | |
| 60 | | 84.1% | Oct 2026 | Lab model card | |
| 61 | | 84% | May 2025 | Lab model card | |
| 62 | | 84% | Jul 2024 | Lab model card | |
| 63 | | 83% | Mar 2025 | Lab model card | |
| 64 | | 82.3% | Jun 2024 | Lab model card | |
| 65 | | 82% | Apr 2024 | Lab model card | |
| 66 | GPT-4o mini | 82% | Jul 2024 | Lab model card | |
| 67 | Kimi-VL-A3B-Thinking-2506 | 82% | Jun 2025 | Lab model card | |
| 68 | | 81.9% | Feb 2024 | Lab model card | |
| 69 | GLM-4 | 81.5% | Jan 2024 | Lab model card | |
| 70 | Claude 3 Sonnet | 81.5% | Mar 2024 | Lab model card | |
| 71 | Grok-1.5 | 81.3% | Mar 2024 | Lab model card | |
| 72 | | 81.2% | Feb 2024 | Lab model card | |
| 73 | Claude 3.5 Haiku | 80.9% | Oct 2024 | Lab model card | |
| 74 | | 80.7% | Jan 2025 | Lab model card | |
| 75 | | 80.6% | Mar 2025 | Lab model card | |
| 76 | | 80.5% | Jun 2025 | Lab model card | |
| 77 | Apple Foundation Model Server (2025) | 80.2% | Jul 2025 | Lab model card | |
| 78 | Granite 4.1 30B | 80.2% | Apr 2026 | Lab model card | |
| 79 | GPT-4.1 nano | 80.1% | Apr 2025 | Lab model card | |
| 80 | | 79.9% | Sep 2024 | Claim in Phi-4 Technical Report, via Epoch AI | |
| 81 | | 79.6% | Apr 2025 | Lab model card | |
| 82 | | 79.1% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 83 | | 78.9% | Aug 2024 | Lab model card | |
| 84 | | 78.6% | Oct 2026 | Lab model card | |
| 85 | | 78.6% | Mar 2025 | Lab model card | |
| 86 | | 78.6% | Dec 2025 | Lab model card | |
| 87 | Claude 2 | 78.5% | Jul 2023 | Lab model card | |
| 88 | Granite 4.0 H Small | 78.4% | Oct 2025 | Lab model card | |
| 89 | | 78.4% | May 2024 | Claim in DeepSeek-V3 Technical Report, via Epoch AI | |
| 90 | | 78.2% | Apr 2024 | Lab model card | |
| 91 | | 77.7% | Sep 2025 | Lab model card | |
| 92 | | 77.5% | Feb 2024 | Lab model card | |
| 93 | | 77.3% | Mar 2025 | Lab model card | |
| 94 | Claude 1.3 | 77% | Apr 2023 | Claim in Model Card and Evaluations for Claude Models, via Epoch AI | |
| 95 | Claude 3 Haiku | 76.7% | Mar 2024 | Lab model card | |
| 96 | | 76.6% | Jan 2025 | Lab model card | |
| 97 | | 76.3% | Nov 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 98 | | 76.2% | Jul 2024 | Lab model card | |
| 99 | | 75.7% | Aug 2024 | Lab model card | |
| 100 | Qwen2.5-Coder-14B | 75.2% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 101 | Claude Instant | 73.4% | Aug 2023 | Claim in Model Card and Evaluations for Claude Models, via Epoch AI | |
| 102 | Grok-1 | 73% | Nov 2023 | Lab model card | |
| 103 | | 73% | Jul 2024 | Lab model card | |
| 104 | Inflection-1 | 72.7% | Jun 2023 | Claim in Inflection-1, via Epoch AI | |
| 105 | | 72.5% | Dec 2023 | Lab model card | |
| 106 | | 72.3% | Jul 2024 | Lab model card | |
| 107 | | 71.9% | Mar 2025 | Lab model card | |
| 108 | GPT-3.5 Turbo Nov 2023 | 71.4% | Nov 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 109 | | 71.1% | Nov 2023 | Lab model card | |
| 110 | | 70.6% | Sep 2023 | Claim in The Falcon Series of Open Language Models, via Epoch AI | |
| 111 | davinci-002 | 70% | Mar 2022 | Claim in GPT-4 Technical Report, via Epoch AI | |
| 112 | Moonlight-16B-A3B | 70% | Feb 2025 | Lab model card | |
| 113 | | 69.9% | Jul 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 114 | | 69.6% | Sep 2025 | Lab model card | |
| 115 | | 69.3% | Apr 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 116 | | 69% | Aug 2024 | Lab model card | |
| 117 | | 68.8% | Aug 2024 | Lab model card | |
| 118 | | 68.6% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 119 | Yi-9B | 68.4% | Mar 2024 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 120 | | 68.4% | Apr 2024 | Lab model card | |
| 121 | | 68% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 122 | | 67.5% | Mar 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 123 | | 67.3% | Feb 2025 | Lab model card | |
| 124 | Granite 3.2 8B Instruct | 66.8% | Feb 2025 | Lab model card | |
| 125 | | 66.5% | Sep 2023 | Lab model card | |
| 126 | | 65.9% | Jul 2025 | Lab model card | |
| 127 | Granite 3.3 8B Instruct | 65.5% | Apr 2025 | Lab model card | |
| 128 | | 64.9% | Jun 2025 | Lab model card | |
| 129 | LFM2-8B-A1B | 64.8% | Oct 2025 | Lab model card | |
| 130 | | 64.3% | Feb 2024 | Lab model card | |
| 131 | | 64.1% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 132 | | 63.4% | Feb 2023 | Claim in Llama 2: Open Foundation and Fine-Tuned Chat Models, via Epoch AI | |
| 133 | | 63.3% | Nov 2025 | Lab model card | |
| 134 | | 63.2% | Nov 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 135 | | 62.6% | Jul 2023 | Lab model card | |
| 136 | | 62.5% | Sep 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 137 | | 62.5% | Dec 2023 | Claim in Gemma: Open Models Based on Gemini Research and Technology, via Epoch AI | |
| 138 | ChatGLM3-6B | 61.4% | Oct 2023 | Lab model card | |
| 139 | DeepSeek-Coder-V2-Lite-Base | 60.5% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 140 | | 60% | few-shot | Dec 2021 | Official leaderboard |
| 141 | granite-4.0-1b | 59.4% | Oct 2025 | Lab model card | |
| 142 | | 59.2% | Sep 2023 | Claim in Nemotron-4 15B Technical Report, via Epoch AI | |
| 143 | | 58.7% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 144 | | 58.7% | Feb 2024 | Claim in Nemotron-4 15B Technical Report, via Epoch AI | |
| 145 | | 58.4% | May 2024 | Claim in Falcon2-11B Technical Report, via Epoch AI | |
| 146 | | 58.1% | Mar 2025 | Lab model card | |
| 147 | internlm-chat-20b | 57.4% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 148 | | 57% | Sep 2023 | Lab model card | |
| 149 | | 56.9% | Mar 2023 | Claim in Falcon2-11B Technical ReportFalcon2-11B Technical Report, via Epoch AI | |
| 150 | | 56.7% | Dec 2023 | Lab model card | |
| 151 | | 55.6% | Jul 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 152 | LFM2-1.2B | 55.2% | Jul 2025 | Lab model card | |
| 153 | Baichuan2-13B-Chat | 55.1% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 154 | | 54.2% | Sep 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 155 | PaLM 62B | 53.7% | Apr 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 156 | | 53.6% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 157 | | 53.2% | Apr 2025 | Lab model card | |
| 158 | | 51.6% | Jul 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 159 | internlm-7b | 51% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 160 | LFM2-VL-1.6B | 51% | Aug 2025 | Lab model card | |
| 161 | | 50.4% | Aug 2026 | Lab model card | |
| 162 | INTELLECT-1 | 49.9% | Nov 2024 | Claim in INTELLECT-1 Technical Report, via Epoch AI | |
| 163 | | 47.9% | Jun 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 164 | chatglm2-6b | 47.9% | Jun 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 165 | | 47.7% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 166 | | 45.7% | Jul 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 167 | InstructGPT 175B | 43.9% | Jan 2022 | Claim in Scaling Language Models: Methods, Analysis & Insights fro…, via Epoch AI | |
| 168 | Qwen-1_8B | 43.9% | Sep 2023 | Lab model card | |
| 169 | GPT-3 6.7B | 43.2% | fine-tuned | Jun 2020 | Official leaderboard |
| 170 | | 42.3% | Jun 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 171 | | 42.3% | Mar 2024 | Lab model card | |
| 172 | Qwen2.5-Coder-0.5B | 42% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 173 | CodeQwen1.5-7B | 40.5% | Apr 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 174 | | 40% | Apr 2025 | Lab model card | |
| 175 | | 39.4% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 176 | | 38.8% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 177 | | 38.8% | Mar 2025 | Lab model card | |
| 178 | | 37.6% | Sep 2023 | Claim in Textbooks Are All You Need II: phi-1.5 technical report, via Epoch AI | |
| 179 | | 36.6% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 180 | | 36.4% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 181 | | 36.3% | Jun 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 182 | | 35.6% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 183 | | 35% | Apr 2023 | Claim in Falcon2-11B Technical Report, via Epoch AI | |
| 184 | | 30.8% | May 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 185 | open_llama_7b | 29.9% | Jun 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 186 | RedPajama-INCITE-7B-Base | 26.3% | May 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 187 | | 26.2% | Mar 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 188 | | 26.2% | Apr 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 189 | | 25.8% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 190 | GPT-J-6B | 25.7% | Aug 2021 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 191 | | 25.1% | May 2022 | Claim in XGen-7B Technical Report, via Epoch AI |
The benchmark's own leaderboard has 10 entries, 2 of them models we track. Epoch AI collected claims for 85 models from labs’ reports and papers. Switch the scores above to rank them together.
5 models have held number one in labs’ own numbers since 2023 Number one over time 6 models have held number one with claims included since 2023
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
OpenAI’s own number for GPT-4 passed 85% in March 2023
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official MMLU leaderboard, labs’ model cards and claims collected by Epoch AI (CC BY 4.0)
Labs headline it less often now, last in August 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 |
|---|---|---|---|
| Aug 12, 2026 | 50.4 | MMLU test | |
| Apr 29, 2026 | Granite 4.1 30B | 80.2 | MMLU (5-shot) |
| Apr 24, 2026 | 90.1 | MMLU (EM) | |
| Mar 6, 2026 | Sarvam 30B | 85.1 | MMLU |
| Mar 6, 2026 | Sarvam 105B | 90.6 | MMLU |
| Jan 27, 2026 | 87.2 | MMLU | |
| Dec 31, 2025 | 88.2 | MMLU | |
| Dec 18, 2025 | 92.3 | MMLU | |
| Dec 12, 2025 | 86.4 | MMLU | |
| Dec 3, 2025 | Hermes 4.3 36B | 87.7 | MMLU |
| Dec 1, 2025 | 85 | MMLU (Zero Shot) | |
| Nov 20, 2025 | 85.4 | MMLU | |
| Oct 31, 2025 | 90.3 | MMLU(acc) | |
| Oct 28, 2025 | granite-4.0-1b | 59.4 | MMLU (5-shot) |
| Oct 7, 2025 | LFM2-8B-A1B | 64.8 | MMLU |
| Oct 2, 2025 | Granite 4.0 H Small | 78.4 | MMLU (5-shot) |
| Sep 23, 2025 | 90.6 | MMLU | |
| Aug 29, 2025 | 89.7 | MMLU(acc) | |
| Aug 26, 2025 | Hermes 4 405B | 87.2 | MMLU (reasoning) |
| Aug 20, 2025 | 87.4 | MMLU | |
| Aug 12, 2025 | LFM2-VL-1.6B | 51 | Text MMLU |
| Aug 5, 2025 | gpt-oss-20b | 85.3 | MMLU |
| Aug 5, 2025 | gpt-oss-120b | 90 | MMLU Questions across academic disciplines |
| Jul 31, 2025 | 91.3 | MMLU (thinking) | |
| Jul 29, 2025 | 65.9 | MMLU | |
| Jul 11, 2025 | Kimi K2 | 89.5 | MMLU, EM |
| Jul 10, 2025 | LFM2-1.2B | 55.2 | MMLU |
| Jul 10, 2025 | 88.2 | MMLU (0-shot, CoT) | |
| Jun 30, 2025 | 86.5 | MMLU | |
| Jun 26, 2025 | 64.9 | MMLU 0-shot (IT) | |
| Jun 21, 2025 | Kimi-VL-A3B-Thinking-2506 | 82 | MMLU |
| Jun 20, 2025 | 80.5 | MMLU | |
| May 23, 2025 | Sarvam-M | 87 | MMLU (0.87) |
| May 20, 2025 | 84 | MMLU | |
| Apr 30, 2025 | 87.4 | MMLU | |
| Apr 29, 2025 | 40 | MMLU | |
| Apr 16, 2025 | Granite 3.3 8B Instruct | 65.5 | MMLU |
| Apr 15, 2025 | 53.2 | MMLU | |
| Apr 14, 2025 | GPT-4.1 nano | 80.1 | MMLU |
| Apr 14, 2025 | GPT-4.1 mini | 87.5 | MMLU |
| Apr 8, 2025 | 91 | MMLU (thinking) | |
| Apr 5, 2025 | 79.6 | MMLU (5-shot) [pretrained] | |
| Apr 5, 2025 | 85.5 | MMLU (5-shot) [pretrained] | |
| Mar 17, 2025 | 80.6 | MMLU | |
| Mar 13, 2025 | MMLU (chart only) | ||
| Mar 13, 2025 | 77.3 | MMLU | |
| Mar 12, 2025 | 78.6 | MMLU 5-shot (PT) | |
| Feb 27, 2025 | 89.5 | MMLU | |
| Feb 26, 2025 | 67.3 | MMLU (5-shot) | |
| Feb 26, 2025 | Granite 3.2 8B Instruct | 66.8 | MMLU |
| Feb 22, 2025 | Moonlight-16B-A3B | 70 | MMLU |
| Feb 11, 2025 | 87.1 | MMLU (0-shot) | |
| Jan 31, 2025 | o3-mini | 86.9 | MMLU (pass@1) |
| Jan 30, 2025 | 87 | MMLU (5 shot, CoT) | |
| Jan 30, 2025 | 80.7 | Best of MMLU (5-shot) [base] / French MMLU [base] / German MMLU [base] / Spanish MMLU [base] | |
| Jan 28, 2025 | MMLU | ||
| Jan 20, 2025 | Kimi k1.5 | 87.4 | MMLU (EM), short-CoT |
| Jan 20, 2025 | 90.8 | MMLU (Pass@1) | |
| Jan 15, 2025 | 88.5 | MMLU* (0-shot CoT) | |
| Jan 13, 2025 | 76.6 | MMLU(0-shot) | |
| Dec 26, 2024 | 88.5 | MMLU (EM) | |
| Dec 17, 2024 | o1 | 91.8 | MMLU (pass @1) |
| Dec 12, 2024 | 84.8 | MMLU (simple-evals) | |
| Dec 6, 2024 | 86 | MMLU (CoT), 0-shot, macro_avg/acc | |
| Oct 22, 2024 | Claude 3.5 Sonnet (October 2024) | 90.5 | MMLU (5-shot CoT) |
| Sep 19, 2024 | 86.1 | MMLU | |
| Sep 12, 2024 | o1-preview | 90.8 | MMLU pass@1 |
| Aug 20, 2024 | 78.9 | MMLU (5-shot) | |
| Aug 13, 2024 | Grok-2 | 87.5 | MMLU (0-shot CoT) |
| Jul 24, 2024 | 84 | Best of MMLU [base pretrained] / Multilingual MMLU French [base] / Multilingual MMLU German [base] / Multilingual MMLU Spanish [base] / Multilingual MMLU Italian [base] | |
| Jul 23, 2024 | 88.6 | MMLU (0-shot, CoT) | |
| Jul 18, 2024 | GPT-4o mini | 82 | MMLU |
| Jun 27, 2024 | 75.2 | MMLU (5-shot, top-1) | |
| Jun 20, 2024 | Claude 3.5 Sonnet | 90.4 | MMLU (5-shot CoT) |
| Jun 7, 2024 | 82.3 | MMLU | |
| May 14, 2024 | 91.7 | MMLU, maj@32 (pretrained) (5-shot = 85.9%) | |
| May 13, 2024 | GPT-4o | 88.7 | MMLU (0-shot CoT) |
| May 7, 2024 | 77.8 | MMLU | |
| Apr 22, 2024 | 78.2 | MMLU (5-Shot) | |
| Apr 18, 2024 | 82 | MMLU (5-shot) [instruct] | |
| Apr 11, 2024 | GPT-4 Turbo | 86.7 | MMLU% |
| Mar 28, 2024 | Grok-1.5 | 81.3 | MMLU (5-shot) |
| Mar 4, 2024 | Claude 3 Opus | 88.2 | MMLU (5-shot CoT) |
| Feb 26, 2024 | 81.2 | Best of MMLU / French MMLU / German MMLU / Spanish MMLU / Italian MMLU | |
| Feb 21, 2024 | 64.3 | MMLU (5-shot, top-1) | |
| Feb 15, 2024 | 81.9 | MMLU, 5-shot | |
| Feb 5, 2024 | 77.5 | MMLU | |
| Jan 16, 2024 | GLM-4 | 81.5 | MMLU (5-shot) |
| Dec 12, 2023 | 56.7 | MMLU, Table 2 | |
| Dec 11, 2023 | 72.5 | Best of MMLU (MCQ in 57 subjects) / French MMLU / German MMLU / Spanish MMLU / Italian MMLU | |
| Dec 6, 2023 | 90 | MMLU, CoT@32 (5-shot = 83.7%) | |
| Nov 29, 2023 | 71.1 | MMLU | |
| Nov 3, 2023 | Grok-1 | 73 | MMLU (5-shot) |
| Oct 27, 2023 | ChatGLM3-6B | 61.4 | MMLU |
| Sep 27, 2023 | 60.1 | MMLU | |
| Sep 25, 2023 | 66.5 | MMLU (5-shot) | |
| Jul 18, 2023 | 68.9 | MMLU | |
| Jul 11, 2023 | Claude 2 | 78.5 | MMLU (5-shot CoT) |
| Mar 14, 2023 | GPT-4 | 86.4 | MMLU (5-shot) |
91 system cards report it, from 27 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 |
|---|---|---|---|---|
| Oct 15, 2026 | | 84.1 | MMLU, exact match, Table 6 | Capabilities |
| Oct 15, 2026 | | 78.6 | MMLU, exact match, Table 6 | Capabilities |
| Jun 4, 2026 | | 89.1 | MMLU, 5-shot acc | Capabilities |
| Mar 21, 2026 | | 92.3 | MMLU (Table 1) | Capabilities |
| Feb 28, 2026 | | 87.7 | MMLU (Table 8) | Capabilities |
| Feb 19, 2026 | | 87.2 | MMLU, Trinity Large Preview (Table 4) | Capabilities |
| Feb 17, 2026 | GLM-5 technical report | 88.3 | GLM-5-Base, MMLU (EM) (Table 11) | Capabilities |
| Jan 11, 2026 | | 88.2 | MMLU (Table 6) | Capabilities |
| Dec 23, 2025 | | 78.6 | MMLU, 5-shot accuracy, base model Table 2 | Capabilities |
| Dec 15, 2025 | | 85.4 | MMLU, Final Think 3.0 column (Table 14) | Capabilities |
| Dec 15, 2025 | | 86.4 | MMLU (Tables 1 and 14) | Capabilities |
| Nov 28, 2025 | LFM2-8B-A1B technical report | 64.8 | MMLU, LFM2-8B-A1B (Table 7) | Capabilities |
| Nov 28, 2025 | LFM2-1.2B technical report | 55.2 | MMLU, LFM2-1.2B (Table 6) | Capabilities |
| Nov 8, 2025 | | 63.3 | MMLU 5-shot (Table 8) | Capabilities |
| Oct 31, 2025 | | 90.3 | MMLU (acc) (Table 13) | Capabilities |
| Oct 8, 2025 | | 40 | MMLU (Table 7, Table 23) | Capabilities |
| Sep 30, 2025 | | 77.7 | MMLU, without reasoning (Table 13) | Capabilities |
| Sep 17, 2025 | | 69.6 | MMLU, 0-shot CoT exact match (Table 17) | Capabilities |
| Sep 1, 2025 | | 89.7 | MMLU (acc), model-judged answer matching (Table 3) | Capabilities |
| Aug 25, 2025 | | 89.1 | MMLU, OpenCompass (Table 14) | Capabilities |
| Aug 25, 2025 | Hermes 4 405B technical report | 87.2 | MMLU, reasoning mode (Table 3). Non-reasoning 73.6 | Capabilities |
| Aug 8, 2025 | GLM-4.5 technical report | 90 | MMLU (EM) (Table 6) | Capabilities |
| Aug 8, 2025 | GLM-4.5-Air technical report | 87.4 | MMLU (EM) (Table 6) | Capabilities |
| Aug 5, 2025 | gpt-oss-20b model card | 85.3 | MMLU, high reasoning (Table 3) | Capabilities |
| Aug 5, 2025 | gpt-oss-120b model card | 90 | MMLU, high reasoning (Table 3) | Capabilities |
| Jul 30, 2025 | | 84 | MMLU, logprobs 5-shot (Table 24) | Capabilities |
| Jul 21, 2025 | Kimi K2 technical report | 89.5 | MMLU (EM) (Table 3) | Capabilities |
| Jul 17, 2025 | | MMLU 5-shot, text-only, chart without printed numbers (Figure 6). Speech-synthesized MMLU* 74.3 (Table 8) | Capabilities | |
| Jul 17, 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 |
| Jun 29, 2025 | | 86.5 | MMLU (Table 5) | Capabilities |
| Apr 30, 2025 | | 87.4 | MMLU, 0-shot CoT, macro average (Table 1) | Capabilities |
| Apr 16, 2025 | | 53.2 | MMLU (5-shot; Acc) (Table 1, Table 2, Table 3) | Capabilities |
| Apr 5, 2025 | | 79.6 | MMLU, 5-shot, macro_avg/acc_char, pretrained (Pre-trained models table) | Capabilities |
| Apr 5, 2025 | | 85.5 | MMLU, 5-shot, macro_avg/acc_char, pretrained (Pre-trained models table) | Capabilities |
| Apr 1, 2025 | | 85.5 | MMLU, 5-shot CoT, simple-evals (Table 3, Table 1) | Capabilities |
| Mar 25, 2025 | | 58.1 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| Mar 25, 2025 | | 76.9 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| Mar 25, 2025 | | 38.8 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| Mar 25, 2025 | | 71.9 | MMLU, IT, 0-shot (Table 18) | Capabilities |
| Mar 16, 2025 | | 83 | MMLU, accuracy (Table 3) | Capabilities |
| Mar 3, 2025 | | 67.3 | MMLU (5-Shot) (Table 7) | Capabilities |
| Jan 20, 2025 | Kimi k1.5 technical report | 87.4 | MMLU (EM), short-CoT (Table 3) | Capabilities |
| Jan 20, 2025 | | 90.8 | MMLU (EM), DeepSeek-R1 (Table 8, same as final column of Table 3) | Capabilities |
| Jan 14, 2025 | | 88.5 | MMLU, 0-shot CoT (Table 8) | Capabilities |
| Dec 31, 2024 | | 77.3 | MMLU, 0-shot CoT (Tables 7 and 16) | Capabilities |
| Dec 26, 2024 | | 88.5 | MMLU (EM), DeepSeek-V3 (Table 6) | Capabilities |
| Dec 12, 2024 | | 84.8 | MMLU, simple-evals (Table 1) | Capabilities |
| Dec 6, 2024 | | 86 | MMLU (CoT), 0-shot, macro_avg/acc (Instruction tuned models table) | Capabilities |
| Nov 23, 2024 | | 73 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 69.4 | Capabilities |
| Nov 23, 2024 | | 86 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 83.6 | Capabilities |
| Nov 23, 2024 | | 88.6 | MMLU (0-shot, CoT), post-trained (Table 2). 5-shot = 87.3 | Capabilities |
| Nov 22, 2024 | | 87 | MMLU (5 shot, CoT), RLVR (Table 4) | Capabilities |
| Oct 22, 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 |
| Oct 22, 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 |
| Aug 30, 2024 | | 75.7 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 68.8 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 78 | MMLU (5-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 78.9 | MMLU (5-shot) (Table 3) | Capabilities |
| Aug 30, 2024 | | 69 | MMLU (5-shot) (Table 3) | Capabilities |
| Jul 31, 2024 | | 72.3 | MMLU, IT, 5-shot (Table 17). PT 5-shot (Table 13): 71.3 | Capabilities |
| Jul 31, 2024 | | 76.2 | MMLU, IT, 5-shot (Table 17). PT 5-shot (Table 13): 75.2 | Capabilities |
| Jul 23, 2024 | | 73 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 69.4 | Capabilities |
| Jul 23, 2024 | | 86 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 83.6 | Capabilities |
| Jul 23, 2024 | | 88.6 | MMLU (CoT), 0-shot, macro_avg/acc, instruction tuned (Instruction tuned models table). 5-shot = 87.3 | Capabilities |
| Jul 15, 2024 | | 82.3 | MMLU, Qwen2-72B-Instruct (Table 6). 83.2 on the non-contaminated subset (Table 15) | Capabilities |
| Jun 20, 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 |
| Jun 18, 2024 | GLM-4 technical report | 81.5 | GLM-4 (0116), MMLU (Table 2) | Capabilities |
| Jun 18, 2024 | ChatGLM3-6B technical report | 61.4 | ChatGLM3-6B-Base, MMLU (Table 1) | Capabilities |
| May 7, 2024 | | 77.8 | MMLU 5-shot, DeepSeek-V2 Chat (RL) (Table 3) | Capabilities |
| Apr 18, 2024 | | 68.4 | MMLU (5-shot), instruction tuned (Instruction tuned models table). Pretrained = 66.6 | Capabilities |
| Apr 18, 2024 | | 82 | MMLU (5-shot), instruction tuned (Instruction tuned models table). Pretrained = 79.5 | Capabilities |
| Mar 13, 2024 | | 64.3 | MMLU, PT, 5-shot top-1 (Table 6). HuggingFace H6 run (Table 7): 64.6 | Capabilities |
| Mar 13, 2024 | | 42.3 | MMLU, PT, 5-shot top-1 (Table 6) | Capabilities |
| Mar 4, 2024 | Claude 3 Sonnet model card | 81.5 | MMLU, 5-shot CoT. 5-shot 79.0 (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Opus model card | 88.2 | MMLU, 5-shot CoT. 5-shot 86.8 (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Haiku model card | 76.7 | MMLU, 5-shot CoT. 5-shot 75.2 (Table 1) | Capabilities |
| Jan 8, 2024 | | 70.6 | MMLU (MCQ in 57 subjects), 5-shot (Table 3; same in Table 2) | Capabilities |
| Jan 5, 2024 | | 71.1 | MMLU 0-shot, DeepSeek 67B Chat (Table 6) | Capabilities |
| Oct 10, 2023 | | 60.1 | MMLU, 5-shot (Table 2) | Capabilities |
| Sep 28, 2023 | | 57 | MMLU 5-shot, higher of the two settings, 0-shot 55.8 (Table 5) | Capabilities |
| Sep 28, 2023 | | 66.5 | MMLU 5-shot, higher of the two settings, 0-shot 64.6 (Table 5) | Capabilities |
| Sep 28, 2023 | | 43.9 | MMLU 5-shot, higher of the two settings, 0-shot 42.4 (Table 5) | Capabilities |
| Jul 19, 2023 | | 45.3 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| Jul 19, 2023 | | 68.9 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| Jul 19, 2023 | | 62.6 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| Jul 19, 2023 | | 54.8 | MMLU (5-shot), pretrained (Table 3, Table 19) | Capabilities |
| Jul 18, 2023 | | 45.3 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| Jul 18, 2023 | | 68.9 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| Jul 18, 2023 | | 54.8 | MMLU, pretrained (Overall performance on grouped academic benchmarks) | Capabilities |
| Jul 11, 2023 | Claude 2 model card | 78.5 | MMLU (5-shot CoT) (4.3) | Capabilities |
| Mar 14, 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%
- BBEH micro average In use Models solve harder replacements for each of the 23 BIG-Bench Hard tasks, 4,520 examples in total. The score is the share of all examples answered correctly.
Gemma 4 31B IT 74.4%
- 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%
- 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%