GSM8K
8.5K grade-school math word problems. Final numeric answer scored by exact match.
Saturated.
- Made by
- OpenAI
- Released
- October 2021
- Kind
- Word problems
- Level
- School
- Scored
- Checked automatically
- Answers
- Short answer
- Tasks
- All public
- Strongest model tested
-
DeepSeek V4.1 Flash #34 on the capability index
- Last in a launch post
- September 2026, dropped after it saturated
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning. To diagnose the failures of current models and support research, we introduce GSM8K, a dataset of 8.5K high quality linguistically diverse grade school math word problems. We find that even the largest transformer models fail to achieve high test performance, despite the conceptual simplicity of this problem distribution. To increase performance, we propose training verifiers to judge the correctness of model completions. At test time, we generate many candidate solutions and select the one ranked highest by the verifier. We demonstrate that verification significantly improves performance on GSM8K, and we provide strong empirical evidence that verification scales more effectively with increased data than a finetuning baseline.
Known issues
A published audit found a problem in GSM8K. We summarize each finding in our words and link its source. The scores on this page don't include any fixes. See all audits
- Contamination Still open
Scale AI reports that many models score lower on GSM1k, a new set written to mirror GSM8K, than on GSM8K itself. It reads the gap as evidence that some models partially memorized GSM8K, while frontier models show minimal signs of overfitting.
Accuracy drops by up to 8% on GSM1k. A model's chance of generating GSM8K examples tracks its GSM8K-to-GSM1k gap (Spearman's r² = 0.36).
Our analysis confirms the widespread suspicion in the field that many models are contaminated by benchmark data, with the worst models performing 8% worse on GSM1k compared to GSM8k.
A Careful Examination of Large Language Model Performance on Grade School Arithmetic by Scale AI, May 1st, 2024
- GSM1k in the almanac
Llama 3.1-405B leads with 96.8%
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.
davinci-002 leads with 56.8% 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.
Llama 3.1-405B leads with 96.8%, 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.
- 96.8
Llama 3.1-405B
- 96.6
ERNIE-4.5-300B-A47B
- 96.4
Cogito v1 Preview 70B
- 96.4 Claude 3.5 Sonnet
- 96.3
DiffusionGemma 26B A4B
- 96.2
Cogito v2 Preview 671B MoE
- 95.9
Qwen2.5-32B
- 95.9
Gemma 3 27B
- 95.8
Qwen2.5-72B
- 95.5
Tulu 3 405B
- 95.3
Qwen2.5-VL-72B
- 95.1
Llama 3.1-70B
- 95.0 Claude 3 Opus
- 94.8
Qwen2.5-14B
- 94.8
MiniMax-Text-01
- 94.8
Qwen2.5-14B-Instruct-1M
- 94.4
Gemini 1.0 Ultra
- 94.4
Gemma 3 12B
- 94.2 Granite 4.1 30B
- 94.0 Sarvam-M
- 93.8
Qwen-Turbo
- 93.3
Code World Model (CWM)
- 93.2
Qwen2-72B
- 93.0
Llama 3-70B
- 93.0
Mistral Large 2
- 93.0
DeepSeek V4.1 Flash
- 93.0
DeepSeek-V4.1-Flash Base
- 92.6
DeepSeek-V4-Pro
- 92.3
Nemotron 3 Nano 30B-A3B Base
- 92.3 Claude 3 Sonnet
- 92.2
DeepSeek-V2 (MoE-236B, May 2024)
- 92.0 GPT-4
- 91.7
Nemotron 3.5 Lightning 30B-A3B Base
- 91.7
Nemotron 3.5 Super 120B-A12B Base
- 91.7
Gemini 1.5 Pro
- 91.6
Qwen2.5-7B
- 91.6
InternVL3.5-241B-A28B
- 91.2
Mistral Large
- 91.0 PaLM 2-L
- 91.0
phi-3-medium 14B
- 90.8
Gemini 1.5 Pro
- 90.0 Grok-1.5
- 89.6
phi-3-small 7.4B
- 89.3
DeepSeek-V3
- 89.2
Gemma 3 4B
- 88.9 Claude 3 Haiku
- 88.7
Phi-3.5-MoE
- 88.7
Qwen2.5-Omni-7B
- 88.6
Phi-4 Mini
- 88.1
Nemotron 3 Ultra 550B-A55B Base
- 88.0 Claude 2
- 87.6 GLM-4
- 87.6
OLMo 2 32B Instruct
- 87.3 Granite 4.0 H Small
- 86.2
Phi-3.5-mini
- 85.6 LFM2.5-1.2B-Thinking
- 84.5
Llama 3.1-8B
- 84.4 LFM2-8B-A1B
- 84.1
DeepSeek LLM 67B
- 83.6
Falcon-H1-34B
- 82.5
phi-3-mini 3.8B
- 81.7 Granite 3.2 8B Instruct
- 80.9 Granite 3.3 8B Instruct
- 80.7
Mistral Small 3
- 79.6
Llama 3-8B
- 79.5
Qwen1.5-72B
- 79.4 GLM-4.5
- 77.6
Apertus 70B
- 77.4 Moonlight-16B-A3B
- 76.3 granite-4.0-1b
- 74.0
Gemma 2 27B
- 72.3 ChatGLM3-6B
- 68.8 GLM-5
- 68.6
Gemma 2 9B
- 68.3
OLMo 2 1B Instruct
- 67.7
Falcon-Arabic-7B
- 62.9 Grok-1
- 62.8
Gemma 3 1B
- 61.1
Phi-2
- 60.1
Qwen-14B
- 58.4
Mixtral 8x7B
- 58.4
BitNet b1.58 2B4T
- 58.3 LFM2-1.2B
- 56.8
Llama 2-70B
- 54.1
Qwen-7B
- 52.2
Mistral 7B v0.1
- 46.4
Gemma 7B
- 42.2
Llama 2-34B
- 28.7
Llama 2-13B
- 27.8 Qwen-1_8B
- 17.7
Gemma 2B
- 14.6
Llama 2-7B
- 1
Llama 3.1-405B 96.8%
- 2
ERNIE-4.5-300B-A47B 96.6%
- 3
Cogito v1 Preview 70B 96.4%
- 4 Claude 3.5 Sonnet 96.4%
- 5
DiffusionGemma 26B A4B 96.3%
- 6
Cogito v2 Preview 671B MoE 96.2%
- 7
Qwen2.5-32B 95.9%
- 8
Gemma 3 27B 95.9%
- 9
Qwen2.5-72B 95.8%
- 10
Tulu 3 405B 95.5%
- 11
Qwen2.5-VL-72B 95.3%
- 12
Llama 3.1-70B 95.1%
- 13 Claude 3 Opus 95%
- 14
Qwen2.5-14B 94.8%
- 15
MiniMax-Text-01 94.8%
- 16
Qwen2.5-14B-Instruct-1M 94.8%
- 17
Gemini 1.0 Ultra 94.4%
- 18
Gemma 3 12B 94.4%
- 19 Granite 4.1 30B 94.2%
- 20 Sarvam-M 94%
- 21
Qwen-Turbo 93.8%
- 22
Code World Model (CWM) 93.3%
- 23
Qwen2-72B 93.2%
- 24
Llama 3-70B 93%
- 25
Mistral Large 2 Jul 2024 93%
- 26
DeepSeek V4.1 Flash 93%
- 27
DeepSeek-V4.1-Flash Base 93%
- 28
DeepSeek-V4-Pro 92.6%
- 29
Nemotron 3 Nano 30B-A3B Base 92.3%
- 30 Claude 3 Sonnet 92.3%
- 31
DeepSeek-V2 (MoE-236B, May 2024) 92.2%
- 32 GPT-4 Mar 2023 92%
- 33
Nemotron 3.5 Lightning 30B-A3B Base 91.7%
- 34
Nemotron 3.5 Super 120B-A12B Base 91.7%
- 35
Gemini 1.5 Pro Feb 2024 91.7%
- 36
Qwen2.5-7B 91.6%
- 37
InternVL3.5-241B-A28B 91.6%
- 38
Mistral Large 91.2%
- 39 PaLM 2-L 91%
- 40
phi-3-medium 14B 91%
- 41
Gemini 1.5 Pro May 2024 90.8%
- 42 Grok-1.5 90%
- 43
phi-3-small 7.4B 89.6%
- 44
DeepSeek-V3 89.3%
- 45
Gemma 3 4B 89.2%
- 46 Claude 3 Haiku 88.9%
- 47
Phi-3.5-MoE 88.7%
- 48
Qwen2.5-Omni-7B 88.7%
- 49
Phi-4 Mini 88.6%
- 50
Nemotron 3 Ultra 550B-A55B Base 88.1%
- 51 Claude 2 88%
- 52 GLM-4 87.6%
- 53
OLMo 2 32B Instruct 87.6%
- 54 Granite 4.0 H Small 87.3%
- 55
Phi-3.5-mini 86.2%
- 56 LFM2.5-1.2B-Thinking 85.6%
- 57
Llama 3.1-8B 84.5%
- 58 LFM2-8B-A1B 84.4%
- 59
DeepSeek LLM 67B 84.1%
- 60
Falcon-H1-34B 83.6%
- 61
phi-3-mini 3.8B 82.5%
- 62 Granite 3.2 8B Instruct 81.7%
- 63 Granite 3.3 8B Instruct 80.9%
- 64
Mistral Small 3 80.7%
- 65
Llama 3-8B 79.6%
- 66
Qwen1.5-72B 79.5%
- 67 GLM-4.5 79.4%
- 68
Apertus 70B 77.6%
- 69 Moonlight-16B-A3B 77.4%
- 70 granite-4.0-1b 76.3%
- 71
Gemma 2 27B 74%
- 72 ChatGLM3-6B 72.3%
- 73 GLM-5 68.8%
- 74
Gemma 2 9B 68.6%
- 75
OLMo 2 1B Instruct 68.3%
- 76
Falcon-Arabic-7B 67.7%
- 77 Grok-1 62.9%
- 78
Gemma 3 1B 62.8%
- 79
Phi-2 61.1%
- 80
Qwen-14B 60.1%
- 81
Mixtral 8x7B 58.4%
- 82
BitNet b1.58 2B4T 58.4%
- 83 LFM2-1.2B 58.3%
- 84
Llama 2-70B 56.8%
- 85
Qwen-7B 54.1%
- 86
Mistral 7B v0.1 52.2%
- 87
Gemma 7B 46.4%
- 88
Llama 2-34B 42.2%
- 89
Llama 2-13B 28.7%
- 90 Qwen-1_8B 27.8%
- 91
Gemma 2B 17.7%
- 92
Llama 2-7B 14.6%
Source: Labs’ model cards
- 56.8 davinci-002
- 50.6 davinci-003
- 48.4
Llama 2-70B
- 46.9 GPT-3.5 Turbo
- 46.6
LLaMA-65B
- 38.1
Mistral 7B v0.1
- 34.4
MPT-30B
- 33.8
Falcon-40B
- 32.0
LLaMA-33B
- 24.5
Llama 2-13B
- 22.6
Vicuna-13B-v1.3
- 15.4
LLaMA-13B
- 9.50
BLOOM-176B
- 9.00 GPT-3 175B (davinci)
- 8.00
LLaMA-7B
- 4.00
OPT-175B
- 4.00
Falcon-7B
- 1.80
OPT-66B
- 1.60 GPT-3 6.7B
- 0.70 GPT-3 XL (babbage)
- 0.60 GPT-3 Medium
- 0.60 InstructGPT 6B
- 0.40 InstructGPT 350M
- 0.0 InstructGPT 1.3B
- 1 davinci-002 56.8%
- 2 davinci-003 50.6%
- 3
Llama 2-70B 48.4%
- 4 GPT-3.5 Turbo Jun 2023 46.9%
- 5
LLaMA-65B 46.6%
- 6
Mistral 7B v0.1 38.1%
- 7
MPT-30B 34.4%
- 8
Falcon-40B 33.8%
- 9
LLaMA-33B 32%
- 10
Llama 2-13B 24.5%
- 11
Vicuna-13B-v1.3 22.6%
- 12
LLaMA-13B 15.4%
- 13
BLOOM-176B 9.50%
- 14 GPT-3 175B (davinci) 9.00%
- 15
LLaMA-7B 8.00%
- 16
OPT-175B 4.00%
- 17
Falcon-7B 4.00%
- 18
OPT-66B 1.80%
- 19 GPT-3 6.7B 1.60%
- 20 GPT-3 XL (babbage) 0.70%
- 21 GPT-3 Medium 0.60%
- 22 InstructGPT 6B 0.60%
- 23 InstructGPT 350M 0.40%
- 24 InstructGPT 1.3B 0%
Source: Stanford HELM, via Epoch AI (CC BY 4.0)
- 96.8
Llama 3.1-405B
- 96.6
ERNIE-4.5-300B-A47B
- 96.4
Cogito v1 Preview 70B
- 96.4 Claude 3.5 Sonnet
- 96.3
DiffusionGemma 26B A4B
- 96.2
Cogito v2 Preview 671B MoE
- 95.9
Qwen2.5-32B
- 95.9
Gemma 3 27B
- 95.8
Qwen2.5-72B
- 95.5
Tulu 3 405B
- 95.3
Qwen2.5-VL-72B
- 95.1
Llama 3.1-70B
- 95.0 Claude 3 Opus
- 94.8
Qwen2.5-14B
- 94.8
MiniMax-Text-01
- 94.8
Qwen2.5-14B-Instruct-1M
- 94.5
DeepSeek-Coder-V2 236B
- 94.4
Gemini 1.0 Ultra
- 94.4
Gemma 3 12B
- 94.2 Qwen2.5-Coder-14B-Instruct
- 94.2 Granite 4.1 30B
- 94.0 Sarvam-M
- 93.8
Qwen-Turbo
- 93.3
Code World Model (CWM)
- 93.2
Qwen2-72B
- 93.0
Llama 3-70B
- 93.0
Mistral Large 2
- 93.0
Qwen2.5-Coder-32B-Instruct
- 93.0
DeepSeek V4.1 Flash
- 93.0
DeepSeek-V4.1-Flash Base
- 92.6
DeepSeek-V4-Pro
- 92.3
Nemotron 3 Nano 30B-A3B Base
- 92.3 Claude 3 Sonnet
- 92.2
DeepSeek-V2 (MoE-236B, May 2024)
- 92.0 GPT-4
- 91.7
Nemotron 3.5 Lightning 30B-A3B Base
- 91.7
Nemotron 3.5 Super 120B-A12B Base
- 91.7
Gemini 1.5 Pro
- 91.6
Qwen2.5-7B
- 91.6
InternVL3.5-241B-A28B
- 91.3 GPT-4o mini
- 91.2
Mistral Large
- 91.1
Qwen2.5-Coder-32B
- 91.0 PaLM 2-L
- 91.0
phi-3-medium 14B
- 90.8
Gemini 1.5 Pro
- 90.0 Grok-1.5
- 90.0 GPT-4
- 89.6
phi-3-small 7.4B
- 89.3
DeepSeek-V3
- 89.2
Gemma 3 4B
- 88.9 Claude 3 Haiku
- 88.7
Phi-3.5-MoE
- 88.7 Qwen2.5-Coder-14B
- 88.7
Qwen2.5-Omni-7B
- 88.6
Phi-4 Mini
- 88.1
Nemotron 3 Ultra 550B-A55B Base
- 88.0 Claude 2
- 87.6 GLM-4
- 87.6 DeepSeek-Coder-V2-Lite-Instruct
- 87.6
OLMo 2 32B Instruct
- 87.3 Granite 4.0 H Small
- 86.7 Claude Instant
- 86.7
Qwen2.5-Coder (7B)
- 86.2
Phi-3.5-mini
- 85.6 LFM2.5-1.2B-Thinking
- 84.9
Gemma 2 9B
- 84.5
Llama 3.1-8B
- 84.4 LFM2-8B-A1B
- 84.2
Mistral NeMo
- 84.1
DeepSeek LLM 67B
- 83.6
Falcon-H1-34B
- 82.5
phi-3-mini 3.8B
- 82.4
Gemini 1.5 Flash
- 81.7 Granite 3.2 8B Instruct
- 80.9 Granite 3.3 8B Instruct
- 80.7
Mistral Small 3
- 80.7 Qwen2.5-Coder-3B-Instruct
- 79.6
Llama 3-8B
- 79.5
Qwen1.5-72B
- 79.4 GLM-4.5
- 78.2 davinci-003
- 77.6
Apertus 70B
- 77.4 Moonlight-16B-A3B
- 76.3 granite-4.0-1b
- 76.0
Yi-34B
- 75.7 Qwen2.5-Coder-3B
- 74.4
Mixtral 8x7B
- 74.0
Gemma 2 27B
- 72.3 ChatGLM3-6B
- 69.6
Llama 2-70B
- 69.6
Stable Beluga 2
- 68.8 GLM-5
- 68.3
OLMo 2 1B Instruct
- 67.7
Falcon-Arabic-7B
- 67.1 DeepSeek-Coder-V2-Lite-Base
- 65.8
Qwen2.5-Coder (1.5B)
- 62.9 internlm-20b
- 62.9 Grok-1
- 62.8
Gemma 3 1B
- 61.3
Qwen-14B
- 61.1
Phi-2
- 58.4
BitNet b1.58 2B4T
- 58.3 LFM2-1.2B
- 57.8 GPT-3.5 Turbo
- 57.7
StarCoder 2 15B
- 56.8 davinci-002
- 56.5
PaLM (540B)
- 54.4
LLaMA-65B
- 54.4
Falcon-180B
- 54.4
Mistral 7B v0.1
- 54.1
Qwen-7B
- 53.8
Falcon 2 11B
- 52.8
Baichuan2-13B
- 46.4
Gemma 7B
- 46.0
Nemotron-4 15B
- 45.7 Baichuan2-13B-Chat
- 44.9
Yi 6B
- 44.1
LLaMA-33B
- 42.2
Llama 2-34B
- 38.6 INTELLECT-1
- 37.7 CodeQwen1.5-7B
- 36.9
Llama 2-13B
- 35.4
DeepSeek Coder 33B
- 35.4
Mistral 7B v0.2
- 34.5 Qwen2.5-Coder-0.5B
- 34.4
MPT-30B
- 33.8
Falcon-40B
- 33.0 PaLM 62B
- 32.7
StarCoder 2 7B
- 32.4 chatglm2-6b
- 31.2 internlm-7b
- 28.1 vicuna-13b-v1.1
- 27.8 Qwen-1_8B
- 26.8
Baichuan 1-13B
- 24.6
Baichuan 2-7B
- 22.6
Vicuna-13B-v1.3
- 21.6
StarCoder 2 3B
- 21.3
DeepSeek Coder 6.7B
- 20.6
LLaMA-13B
- 17.7
Gemma 2B
- 16.7
Llama 2-7B
- 15.7 internlm-chat-20b
- 11.0
LLaMA-7B
- 9.50
BLOOM-176B
- 9.20
Baichuan1-7B
- 9.10
MPT-7B
- 9.00 GPT-3 175B (davinci)
- 6.80
Falcon-7B
- 4.40
DeepSeek Coder 1.3B
- 4.00
OPT-175B
- 1.80
OPT-66B
- 1.60 GPT-3 6.7B
- 0.70 GPT-3 XL (babbage)
- 0.60 GPT-3 Medium
- 0.60 InstructGPT 6B
- 0.40 InstructGPT 350M
- 0.0 InstructGPT 1.3B
- 1
Llama 3.1-405B 96.8%
- 2
ERNIE-4.5-300B-A47B 96.6%
- 3
Cogito v1 Preview 70B 96.4%
- 4 Claude 3.5 Sonnet 96.4%
- 5
DiffusionGemma 26B A4B 96.3%
- 6
Cogito v2 Preview 671B MoE 96.2%
- 7
Qwen2.5-32B 95.9%
- 8
Gemma 3 27B 95.9%
- 9
Qwen2.5-72B 95.8%
- 10
Tulu 3 405B 95.5%
- 11
Qwen2.5-VL-72B 95.3%
- 12
Llama 3.1-70B 95.1%
- 13 Claude 3 Opus 95%
- 14
Qwen2.5-14B 94.8%
- 15
MiniMax-Text-01 94.8%
- 16
Qwen2.5-14B-Instruct-1M 94.8%
- 17
DeepSeek-Coder-V2 236B 94.5%
- 18
Gemini 1.0 Ultra 94.4%
- 19
Gemma 3 12B 94.4%
- 20 Qwen2.5-Coder-14B-Instruct 94.2%
- 21 Granite 4.1 30B 94.2%
- 22 Sarvam-M 94%
- 23
Qwen-Turbo 93.8%
- 24
Code World Model (CWM) 93.3%
- 25
Qwen2-72B 93.2%
- 26
Llama 3-70B 93%
- 27
Mistral Large 2 Jul 2024 93%
- 28
Qwen2.5-Coder-32B-Instruct 93%
- 29
DeepSeek V4.1 Flash 93%
- 30
DeepSeek-V4.1-Flash Base 93%
- 31
DeepSeek-V4-Pro 92.6%
- 32
Nemotron 3 Nano 30B-A3B Base 92.3%
- 33 Claude 3 Sonnet 92.3%
- 34
DeepSeek-V2 (MoE-236B, May 2024) 92.2%
- 35 GPT-4 Mar 2023 92%
- 36
Nemotron 3.5 Lightning 30B-A3B Base 91.7%
- 37
Nemotron 3.5 Super 120B-A12B Base 91.7%
- 38
Gemini 1.5 Pro Feb 2024 91.7%
- 39
Qwen2.5-7B 91.6%
- 40
InternVL3.5-241B-A28B 91.6%
- 41 GPT-4o mini 91.3%
- 42
Mistral Large 91.2%
- 43
Qwen2.5-Coder-32B 91.1%
- 44 PaLM 2-L 91%
- 45
phi-3-medium 14B 91%
- 46
Gemini 1.5 Pro May 2024 90.8%
- 47 Grok-1.5 90%
- 48 GPT-4 Jun 2023 90%
- 49
phi-3-small 7.4B 89.6%
- 50
DeepSeek-V3 89.3%
- 51
Gemma 3 4B 89.2%
- 52 Claude 3 Haiku 88.9%
- 53
Phi-3.5-MoE 88.7%
- 54 Qwen2.5-Coder-14B 88.7%
- 55
Qwen2.5-Omni-7B 88.7%
- 56
Phi-4 Mini 88.6%
- 57
Nemotron 3 Ultra 550B-A55B Base 88.1%
- 58 Claude 2 88%
- 59 GLM-4 87.6%
- 60 DeepSeek-Coder-V2-Lite-Instruct 87.6%
- 61
OLMo 2 32B Instruct 87.6%
- 62 Granite 4.0 H Small 87.3%
- 63 Claude Instant 86.7%
- 64
Qwen2.5-Coder (7B) 86.7%
- 65
Phi-3.5-mini 86.2%
- 66 LFM2.5-1.2B-Thinking 85.6%
- 67
Gemma 2 9B 84.9%
- 68
Llama 3.1-8B 84.5%
- 69 LFM2-8B-A1B 84.4%
- 70
Mistral NeMo 84.2%
- 71
DeepSeek LLM 67B 84.1%
- 72
Falcon-H1-34B 83.6%
- 73
phi-3-mini 3.8B 82.5%
- 74
Gemini 1.5 Flash May 2024 82.4%
- 75 Granite 3.2 8B Instruct 81.7%
- 76 Granite 3.3 8B Instruct 80.9%
- 77
Mistral Small 3 80.7%
- 78 Qwen2.5-Coder-3B-Instruct 80.7%
- 79
Llama 3-8B 79.6%
- 80
Qwen1.5-72B 79.5%
- 81 GLM-4.5 79.4%
- 82 davinci-003 78.2%
- 83
Apertus 70B 77.6%
- 84 Moonlight-16B-A3B 77.4%
- 85 granite-4.0-1b 76.3%
- 86
Yi-34B 76%
- 87 Qwen2.5-Coder-3B 75.7%
- 88
Mixtral 8x7B 74.4%
- 89
Gemma 2 27B 74%
- 90 ChatGLM3-6B 72.3%
- 91
Llama 2-70B 69.6%
- 92
Stable Beluga 2 69.6%
- 93 GLM-5 68.8%
- 94
OLMo 2 1B Instruct 68.3%
- 95
Falcon-Arabic-7B 67.7%
- 96 DeepSeek-Coder-V2-Lite-Base 67.1%
- 97
Qwen2.5-Coder (1.5B) 65.8%
- 98 internlm-20b 62.9%
- 99 Grok-1 62.9%
- 100
Gemma 3 1B 62.8%
- 101
Qwen-14B 61.3%
- 102
Phi-2 61.1%
- 103
BitNet b1.58 2B4T 58.4%
- 104 LFM2-1.2B 58.3%
- 105 GPT-3.5 Turbo Jun 2023 57.8%
- 106
StarCoder 2 15B 57.7%
- 107 davinci-002 56.8%
- 108
PaLM (540B) 56.5%
- 109
LLaMA-65B 54.4%
- 110
Falcon-180B 54.4%
- 111
Mistral 7B v0.1 54.4%
- 112
Qwen-7B 54.1%
- 113
Falcon 2 11B 53.8%
- 114
Baichuan2-13B 52.8%
- 115
Gemma 7B 46.4%
- 116
Nemotron-4 15B 46%
- 117 Baichuan2-13B-Chat 45.7%
- 118
Yi 6B 44.9%
- 119
LLaMA-33B 44.1%
- 120
Llama 2-34B 42.2%
- 121 INTELLECT-1 38.6%
- 122 CodeQwen1.5-7B 37.7%
- 123
Llama 2-13B 36.9%
- 124
DeepSeek Coder 33B 35.4%
- 125
Mistral 7B v0.2 35.4%
- 126 Qwen2.5-Coder-0.5B 34.5%
- 127
MPT-30B 34.4%
- 128
Falcon-40B 33.8%
- 129 PaLM 62B 33%
- 130
StarCoder 2 7B 32.7%
- 131 chatglm2-6b 32.4%
- 132 internlm-7b 31.2%
- 133 vicuna-13b-v1.1 28.1%
- 134 Qwen-1_8B 27.8%
- 135
Baichuan 1-13B 26.8%
- 136
Baichuan 2-7B 24.6%
- 137
Vicuna-13B-v1.3 22.6%
- 138
StarCoder 2 3B 21.6%
- 139
DeepSeek Coder 6.7B 21.3%
- 140
LLaMA-13B 20.6%
- 141
Gemma 2B 17.7%
- 142
Llama 2-7B 16.7%
- 143 internlm-chat-20b 15.7%
- 144
LLaMA-7B 11%
- 145
BLOOM-176B 9.50%
- 146
Baichuan1-7B 9.20%
- 147
MPT-7B 9.10%
- 148 GPT-3 175B (davinci) 9.00%
- 149
Falcon-7B 6.80%
- 150
DeepSeek Coder 1.3B 4.40%
- 151
OPT-175B 4.00%
- 152
OPT-66B 1.80%
- 153 GPT-3 6.7B 1.60%
- 154 GPT-3 XL (babbage) 0.70%
- 155 GPT-3 Medium 0.60%
- 156 InstructGPT 6B 0.60%
- 157 InstructGPT 350M 0.40%
- 158 InstructGPT 1.3B 0%
Sources: Labs’ model cards and Stanford HELM, via Epoch AI (CC BY 4.0)
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 96.8% | Jul 2024 | Lab model card | |
| 2 | | 96.6% | Jun 2025 | Lab model card | |
| 3 | | 96.4% | Apr 2025 | Lab model card | |
| 4 | Claude 3.5 Sonnet | 96.4% | Jun 2024 | Lab model card | |
| 5 | | 96.3% | Jul 2026 | Lab model card | |
| 6 | | 96.2% | Jul 2025 | Lab model card | |
| 7 | | 95.9% | Dec 2024 | Lab model card | |
| 8 | | 95.9% | Mar 2025 | Lab model card | |
| 9 | | 95.8% | Sep 2024 | Lab model card | |
| 10 | | 95.5% | Jan 2025 | Lab model card | |
| 11 | | 95.3% | Feb 2025 | Lab model card | |
| 12 | | 95.1% | Jul 2024 | Lab model card | |
| 13 | Claude 3 Opus | 95% | Mar 2024 | Lab model card | |
| 14 | | 94.8% | Dec 2024 | Lab model card | |
| 15 | | 94.8% | Jan 2025 | Lab model card | |
| 16 | | 94.8% | Jan 2025 | Lab model card | |
| 17 | | 94.4% | Dec 2023 | Lab model card | |
| 18 | | 94.4% | Mar 2025 | Lab model card | |
| 19 | Granite 4.1 30B | 94.2% | Apr 2026 | Lab model card | |
| 20 | Sarvam-M | 94% | May 2025 | Lab model card | |
| 21 | | 93.8% | Dec 2024 | Lab model card | |
| 22 | | 93.3% | Sep 2025 | Lab model card | |
| 23 | | 93.2% | Jul 2024 | Lab model card | |
| 24 | | 93% | Apr 2024 | Lab model card | |
| 25 | | 93% | Jul 2024 | Lab model card | |
| 26 | | 93% | Sep 2026 | Lab model card | |
| 27 | | 93% | Sep 2026 | Lab model card | |
| 28 | | 92.6% | Apr 2026 | Lab model card | |
| 29 | | 92.3% | Dec 2025 | Lab model card | |
| 30 | Claude 3 Sonnet | 92.3% | Mar 2024 | Lab model card | |
| 31 | | 92.2% | May 2024 | Lab model card | |
| 32 | GPT-4 Mar 2023 | 92% | Mar 2023 | Lab model card | |
| 33 | | 91.7% | Oct 2026 | Lab model card | |
| 34 | | 91.7% | Oct 2026 | Lab model card | |
| 35 | | 91.7% | Feb 2024 | Lab model card | |
| 36 | | 91.6% | Dec 2024 | Lab model card | |
| 37 | | 91.6% | Aug 2025 | Lab model card | |
| 38 | | 91.2% | Feb 2024 | Lab model card | |
| 39 | PaLM 2-L | 91% | May 2023 | Lab model card | |
| 40 | | 91% | Aug 2024 | Lab model card | |
| 41 | | 90.8% | May 2024 | Lab model card | |
| 42 | Grok-1.5 | 90% | Mar 2024 | Lab model card | |
| 43 | | 89.6% | Aug 2024 | Lab model card | |
| 44 | | 89.3% | Dec 2024 | Lab model card | |
| 45 | | 89.2% | Mar 2025 | Lab model card | |
| 46 | Claude 3 Haiku | 88.9% | Mar 2024 | Lab model card | |
| 47 | | 88.7% | Aug 2024 | Lab model card | |
| 48 | | 88.7% | Mar 2025 | Lab model card | |
| 49 | | 88.6% | Feb 2025 | Lab model card | |
| 50 | | 88.1% | Jun 2026 | Lab model card | |
| 51 | Claude 2 | 88% | Jul 2023 | Lab model card | |
| 52 | GLM-4 | 87.6% | Jan 2024 | Lab model card | |
| 53 | | 87.6% | Mar 2025 | Lab model card | |
| 54 | Granite 4.0 H Small | 87.3% | Oct 2025 | Lab model card | |
| 55 | | 86.2% | Aug 2024 | Lab model card | |
| 56 | LFM2.5-1.2B-Thinking | 85.6% | Jan 2026 | Lab model card | |
| 57 | | 84.5% | Jul 2024 | Lab model card | |
| 58 | LFM2-8B-A1B | 84.4% | Oct 2025 | Lab model card | |
| 59 | | 84.1% | Nov 2023 | Lab model card | |
| 60 | | 83.6% | May 2025 | Lab model card | |
| 61 | | 82.5% | Aug 2024 | Lab model card | |
| 62 | Granite 3.2 8B Instruct | 81.7% | Feb 2025 | Lab model card | |
| 63 | Granite 3.3 8B Instruct | 80.9% | Apr 2025 | Lab model card | |
| 64 | | 80.7% | Jan 2025 | Lab model card | |
| 65 | | 79.6% | Apr 2024 | Lab model card | |
| 66 | | 79.5% | Feb 2024 | Lab model card | |
| 67 | GLM-4.5 | 79.4% | Aug 2025 | Lab model card | |
| 68 | | 77.6% | Sep 2025 | Lab model card | |
| 69 | Moonlight-16B-A3B | 77.4% | Feb 2025 | Lab model card | |
| 70 | granite-4.0-1b | 76.3% | Oct 2025 | Lab model card | |
| 71 | | 74% | Jun 2024 | Lab model card | |
| 72 | ChatGLM3-6B | 72.3% | Oct 2023 | Lab model card | |
| 73 | GLM-5 | 68.8% | Feb 2026 | Lab model card | |
| 74 | | 68.6% | Jul 2024 | Lab model card | |
| 75 | | 68.3% | Apr 2025 | Lab model card | |
| 76 | | 67.7% | Nov 2025 | Lab model card | |
| 77 | Grok-1 | 62.9% | Nov 2023 | Lab model card | |
| 78 | | 62.8% | Mar 2025 | Lab model card | |
| 79 | | 61.1% | Dec 2023 | Lab model card | |
| 80 | | 60.1% | Sep 2023 | Lab model card | |
| 81 | | 58.4% | Dec 2023 | Lab model card | |
| 82 | | 58.4% | Apr 2025 | Lab model card | |
| 83 | LFM2-1.2B | 58.3% | Jul 2025 | Lab model card | |
| 84 | | 56.8% | Jul 2023 | Lab model card | |
| 85 | | 54.1% | Sep 2023 | Lab model card | |
| 86 | | 52.2% | Oct 2023 | Lab model card | |
| 87 | | 46.4% | Feb 2024 | Lab model card | |
| 88 | | 42.2% | Jul 2023 | Lab model card | |
| 89 | | 28.7% | Jul 2023 | Lab model card | |
| 90 | Qwen-1_8B | 27.8% | Sep 2023 | Lab model card | |
| 91 | | 17.7% | Mar 2024 | Lab model card | |
| 92 | | 14.6% | Jul 2023 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | davinci-002 | 56.8% | Mar 2022 | Stanford HELM, via Epoch AI | |
| 2 | davinci-003 | 50.6% | Nov 2022 | Stanford HELM, via Epoch AI | |
| 3 | | 48.4% | Jul 2023 | Stanford HELM, via Epoch AI | |
| 4 | GPT-3.5 Turbo Jun 2023 | 46.9% | Jun 2023 | Stanford HELM, via Epoch AI | |
| 5 | | 46.6% | Feb 2023 | Stanford HELM, via Epoch AI | |
| 6 | | 38.1% | Sep 2023 | Stanford HELM, via Epoch AI | |
| 7 | | 34.4% | Jun 2023 | Stanford HELM, via Epoch AI | |
| 8 | | 33.8% | May 2023 | Stanford HELM, via Epoch AI | |
| 9 | | 32% | Feb 2023 | Stanford HELM, via Epoch AI | |
| 10 | | 24.5% | Jul 2023 | Stanford HELM, via Epoch AI | |
| 11 | | 22.6% | Jun 2023 | Stanford HELM, via Epoch AI | |
| 12 | | 15.4% | Feb 2023 | Stanford HELM, via Epoch AI | |
| 13 | | 9.50% | Jul 2022 | Stanford HELM, via Epoch AI | |
| 14 | GPT-3 175B (davinci) | 9.00% | May 2020 | Stanford HELM, via Epoch AI | |
| 15 | | 8.00% | Feb 2023 | Stanford HELM, via Epoch AI | |
| 16 | | 4.00% | May 2022 | Stanford HELM, via Epoch AI | |
| 17 | | 4.00% | Apr 2023 | Stanford HELM, via Epoch AI | |
| 18 | | 1.80% | May 2022 | Stanford HELM, via Epoch AI | |
| 19 | GPT-3 6.7B | 1.60% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 20 | GPT-3 XL (babbage) | 0.70% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 21 | GPT-3 Medium | 0.60% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 22 | InstructGPT 6B | 0.60% | Jan 2022 | Stanford HELM, via Epoch AI | |
| 23 | InstructGPT 350M | 0.40% | Jan 2022 | Stanford HELM, via Epoch AI | |
| 24 | InstructGPT 1.3B | 0% | Jan 2022 | Stanford HELM, via Epoch AI |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 96.8% | Jul 2024 | Lab model card | |
| 2 | | 96.6% | Jun 2025 | Lab model card | |
| 3 | | 96.4% | Apr 2025 | Lab model card | |
| 4 | Claude 3.5 Sonnet | 96.4% | Jun 2024 | Lab model card | |
| 5 | | 96.3% | Jul 2026 | Lab model card | |
| 6 | | 96.2% | Jul 2025 | Lab model card | |
| 7 | | 95.9% | Dec 2024 | Lab model card | |
| 8 | | 95.9% | Mar 2025 | Lab model card | |
| 9 | | 95.8% | Sep 2024 | Lab model card | |
| 10 | | 95.5% | Jan 2025 | Lab model card | |
| 11 | | 95.3% | Feb 2025 | Lab model card | |
| 12 | | 95.1% | Jul 2024 | Lab model card | |
| 13 | Claude 3 Opus | 95% | Mar 2024 | Lab model card | |
| 14 | | 94.8% | Dec 2024 | Lab model card | |
| 15 | | 94.8% | Jan 2025 | Lab model card | |
| 16 | | 94.8% | Jan 2025 | Lab model card | |
| 17 | | 94.5% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 18 | | 94.4% | Dec 2023 | Lab model card | |
| 19 | | 94.4% | Mar 2025 | Lab model card | |
| 20 | Qwen2.5-Coder-14B-Instruct | 94.2% | Nov 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 21 | Granite 4.1 30B | 94.2% | Apr 2026 | Lab model card | |
| 22 | Sarvam-M | 94% | May 2025 | Lab model card | |
| 23 | | 93.8% | Dec 2024 | Lab model card | |
| 24 | | 93.3% | Sep 2025 | Lab model card | |
| 25 | | 93.2% | Jul 2024 | Lab model card | |
| 26 | | 93% | Apr 2024 | Lab model card | |
| 27 | | 93% | Jul 2024 | Lab model card | |
| 28 | | 93% | Nov 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 29 | | 93% | Sep 2026 | Lab model card | |
| 30 | | 93% | Sep 2026 | Lab model card | |
| 31 | | 92.6% | Apr 2026 | Lab model card | |
| 32 | | 92.3% | Dec 2025 | Lab model card | |
| 33 | Claude 3 Sonnet | 92.3% | Mar 2024 | Lab model card | |
| 34 | | 92.2% | May 2024 | Lab model card | |
| 35 | GPT-4 Mar 2023 | 92% | Mar 2023 | Lab model card | |
| 36 | | 91.7% | Oct 2026 | Lab model card | |
| 37 | | 91.7% | Oct 2026 | Lab model card | |
| 38 | | 91.7% | Feb 2024 | Lab model card | |
| 39 | | 91.6% | Dec 2024 | Lab model card | |
| 40 | | 91.6% | Aug 2025 | Lab model card | |
| 41 | GPT-4o mini | 91.3% | Jul 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 42 | | 91.2% | Feb 2024 | Lab model card | |
| 43 | | 91.1% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 44 | PaLM 2-L | 91% | May 2023 | Lab model card | |
| 45 | | 91% | Aug 2024 | Lab model card | |
| 46 | | 90.8% | May 2024 | Lab model card | |
| 47 | Grok-1.5 | 90% | Mar 2024 | Lab model card | |
| 48 | GPT-4 Jun 2023 | 90% | Jun 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 49 | | 89.6% | Aug 2024 | Lab model card | |
| 50 | | 89.3% | Dec 2024 | Lab model card | |
| 51 | | 89.2% | Mar 2025 | Lab model card | |
| 52 | Claude 3 Haiku | 88.9% | Mar 2024 | Lab model card | |
| 53 | | 88.7% | Aug 2024 | Lab model card | |
| 54 | Qwen2.5-Coder-14B | 88.7% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 55 | | 88.7% | Mar 2025 | Lab model card | |
| 56 | | 88.6% | Feb 2025 | Lab model card | |
| 57 | | 88.1% | Jun 2026 | Lab model card | |
| 58 | Claude 2 | 88% | Jul 2023 | Lab model card | |
| 59 | GLM-4 | 87.6% | Jan 2024 | Lab model card | |
| 60 | DeepSeek-Coder-V2-Lite-Instruct | 87.6% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 61 | | 87.6% | Mar 2025 | Lab model card | |
| 62 | Granite 4.0 H Small | 87.3% | Oct 2025 | Lab model card | |
| 63 | Claude Instant | 86.7% | Aug 2023 | Claim in Releasing Claude Instant 1.2, via Epoch AI | |
| 64 | | 86.7% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 65 | | 86.2% | Aug 2024 | Lab model card | |
| 66 | LFM2.5-1.2B-Thinking | 85.6% | Jan 2026 | Lab model card | |
| 67 | | 84.9% | Jun 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 68 | | 84.5% | Jul 2024 | Lab model card | |
| 69 | LFM2-8B-A1B | 84.4% | Oct 2025 | Lab model card | |
| 70 | | 84.2% | Jul 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 71 | | 84.1% | Nov 2023 | Lab model card | |
| 72 | | 83.6% | May 2025 | Lab model card | |
| 73 | | 82.5% | Aug 2024 | Lab model card | |
| 74 | | 82.4% | May 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 75 | Granite 3.2 8B Instruct | 81.7% | Feb 2025 | Lab model card | |
| 76 | Granite 3.3 8B Instruct | 80.9% | Apr 2025 | Lab model card | |
| 77 | | 80.7% | Jan 2025 | Lab model card | |
| 78 | Qwen2.5-Coder-3B-Instruct | 80.7% | Nov 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 79 | | 79.6% | Apr 2024 | Lab model card | |
| 80 | | 79.5% | Feb 2024 | Lab model card | |
| 81 | GLM-4.5 | 79.4% | Aug 2025 | Lab model card | |
| 82 | davinci-003 | 78.2% | Nov 2022 | Claim in Qwen Technical Report, via Epoch AI | |
| 83 | | 77.6% | Sep 2025 | Lab model card | |
| 84 | Moonlight-16B-A3B | 77.4% | Feb 2025 | Lab model card | |
| 85 | granite-4.0-1b | 76.3% | Oct 2025 | Lab model card | |
| 86 | | 76% | Nov 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 87 | Qwen2.5-Coder-3B | 75.7% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 88 | | 74.4% | Dec 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 89 | | 74% | Jun 2024 | Lab model card | |
| 90 | ChatGLM3-6B | 72.3% | Oct 2023 | Lab model card | |
| 91 | | 69.6% | Jul 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 92 | | 69.6% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 93 | GLM-5 | 68.8% | Feb 2026 | Lab model card | |
| 94 | | 68.3% | Apr 2025 | Lab model card | |
| 95 | | 67.7% | Nov 2025 | Lab model card | |
| 96 | DeepSeek-Coder-V2-Lite-Base | 67.1% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 97 | | 65.8% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 98 | internlm-20b | 62.9% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 99 | Grok-1 | 62.9% | Nov 2023 | Lab model card | |
| 100 | | 62.8% | Mar 2025 | Lab model card | |
| 101 | | 61.3% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 102 | | 61.1% | Dec 2023 | Lab model card | |
| 103 | | 58.4% | Apr 2025 | Lab model card | |
| 104 | LFM2-1.2B | 58.3% | Jul 2025 | Lab model card | |
| 105 | GPT-3.5 Turbo Jun 2023 | 57.8% | Jun 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 106 | | 57.7% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 107 | davinci-002 | 56.8% | Mar 2022 | Stanford HELM, via Epoch AI | |
| 108 | | 56.5% | Apr 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 109 | | 54.4% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 110 | | 54.4% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 111 | | 54.4% | Sep 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 112 | | 54.1% | Sep 2023 | Lab model card | |
| 113 | | 53.8% | May 2024 | Claim in Falcon2-11B Technical Report, via Epoch AI | |
| 114 | | 52.8% | Sep 2023 | Claim in Nemotron-4 15B Technical Report, via Epoch AI | |
| 115 | | 46.4% | Feb 2024 | Lab model card | |
| 116 | | 46% | Feb 2024 | Claim in Nemotron-4 15B Technical Report, via Epoch AI | |
| 117 | Baichuan2-13B-Chat | 45.7% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 118 | | 44.9% | Nov 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 119 | | 44.1% | Feb 2023 | Claim in Mixtral of Experts, via Epoch AI | |
| 120 | | 42.2% | Jul 2023 | Lab model card | |
| 121 | INTELLECT-1 | 38.6% | Nov 2024 | Claim in INTELLECT-1 Technical Report, via Epoch AI | |
| 122 | CodeQwen1.5-7B | 37.7% | Apr 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 123 | | 36.9% | Jul 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 124 | | 35.4% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 125 | | 35.4% | Dec 2023 | Claim in Gemma: Open Models Based on Gemini Research and Technology, via Epoch AI | |
| 126 | Qwen2.5-Coder-0.5B | 34.5% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 127 | | 34.4% | Jun 2023 | Stanford HELM, via Epoch AI | |
| 128 | | 33.8% | May 2023 | Stanford HELM, via Epoch AI | |
| 129 | PaLM 62B | 33% | Apr 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 130 | | 32.7% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 131 | chatglm2-6b | 32.4% | Jun 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 132 | internlm-7b | 31.2% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 133 | vicuna-13b-v1.1 | 28.1% | Apr 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 134 | Qwen-1_8B | 27.8% | Sep 2023 | Lab model card | |
| 135 | | 26.8% | Jul 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 136 | | 24.6% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 137 | | 22.6% | Jun 2023 | Stanford HELM, via Epoch AI | |
| 138 | | 21.6% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 139 | | 21.3% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 140 | | 20.6% | Feb 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 141 | | 17.7% | Mar 2024 | Lab model card | |
| 142 | | 16.7% | Jul 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 143 | internlm-chat-20b | 15.7% | Sep 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 144 | | 11% | Feb 2023 | Claim in Llama 2: Open Foundation and Fine-Tuned Chat Models, via Epoch AI | |
| 145 | | 9.50% | Jul 2022 | Stanford HELM, via Epoch AI | |
| 146 | | 9.20% | Jun 2023 | Claim in Baichuan 2: Open Large-scale Language Models, via Epoch AI | |
| 147 | | 9.10% | May 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 148 | GPT-3 175B (davinci) | 9.00% | May 2020 | Stanford HELM, via Epoch AI | |
| 149 | | 6.80% | Apr 2023 | Claim in Llama 2: Open Foundation and Fine-Tuned Chat Models, via Epoch AI | |
| 150 | | 4.40% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 151 | | 4.00% | May 2022 | Stanford HELM, via Epoch AI | |
| 152 | | 1.80% | May 2022 | Stanford HELM, via Epoch AI | |
| 153 | GPT-3 6.7B | 1.60% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 154 | GPT-3 XL (babbage) | 0.70% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 155 | GPT-3 Medium | 0.60% | Jun 2020 | Stanford HELM, via Epoch AI | |
| 156 | InstructGPT 6B | 0.60% | Jan 2022 | Stanford HELM, via Epoch AI | |
| 157 | InstructGPT 350M | 0.40% | Jan 2022 | Stanford HELM, via Epoch AI | |
| 158 | InstructGPT 1.3B | 0% | Jan 2022 | Stanford HELM, via Epoch AI |
Epoch AI copied scores for 24 models from Stanford HELM. Epoch AI collected claims for 70 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. We draw one row each for Stanford HELM, which Epoch AI copied and what labs claim in their cards.
Sources: Stanford HELM, via Epoch AI (CC BY 4.0) and 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: Other boards, via Epoch AI (CC BY 4.0), labs’ model cards and claims collected by Epoch AI (CC BY 4.0)
Labs headline it less often now, last in September 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 |
|---|---|---|---|
| Sep 10, 2026 | 93 | GSM8K (EM) | |
| Apr 29, 2026 | Granite 4.1 30B | 94.2 | GSM8K (8-shot) |
| Apr 24, 2026 | 92.6 | GSM8K (EM) | |
| Jan 20, 2026 | LFM2.5-1.2B-Thinking | 85.6 | GSM8K |
| Oct 28, 2025 | granite-4.0-1b | 76.3 | GSM8K (8-shot) |
| Oct 7, 2025 | LFM2-8B-A1B | 84.4 | GSM8K |
| Oct 2, 2025 | Granite 4.0 H Small | 87.3 | GSM8K (8-shot) |
| Jul 31, 2025 | 96.2 | GSM8K (thinking) | |
| Jul 10, 2025 | LFM2-1.2B | 58.3 | GSM8K |
| Jun 30, 2025 | 96.6 | GSM8K | |
| May 23, 2025 | Sarvam-M | 94 | GSM-8K (0.94) |
| May 20, 2025 | 83.6 | GSM8k | |
| Apr 29, 2025 | 68.3 | GSM8K | |
| Apr 16, 2025 | Granite 3.3 8B Instruct | 80.9 | GSM8K |
| Apr 15, 2025 | 58.4 | GSM8K | |
| Apr 8, 2025 | 96.4 | GSM8K (thinking) | |
| Mar 27, 2025 | 88.7 | GSM8K | |
| Mar 16, 2025 | GSM8K | ||
| Mar 13, 2025 | 87.6 | GSM8k | |
| Mar 12, 2025 | 95.9 | GSM8K 0-shot (IT); PT 8-shot = 82.6 | |
| Feb 26, 2025 | 88.6 | GSM8K (8-shot, CoT) | |
| Feb 26, 2025 | Granite 3.2 8B Instruct | 81.7 | GSM8K |
| Feb 22, 2025 | Moonlight-16B-A3B | 77.4 | GSM8K |
| Jan 30, 2025 | 95.5 | GSM8K (8 shot, CoT) | |
| Jan 30, 2025 | 80.7 | GSM8K (5-shot, maj@1) [base] | |
| Jan 28, 2025 | GSM8K | ||
| Jan 15, 2025 | 94.8 | GSM8k* | |
| Dec 26, 2024 | 89.3 | GSM8K (EM) | |
| Sep 19, 2024 | 95.8 | GSM8K | |
| Aug 20, 2024 | 88.7 | GSM8K (8-shot, CoT) | |
| Jul 24, 2024 | 93 | GSM8K (8-shot) | |
| Jul 23, 2024 | 96.8 | GSM8K (8-shot, CoT) | |
| Jun 27, 2024 | 74 | GSM8K (5-shot, maj@1) | |
| Jun 20, 2024 | Claude 3.5 Sonnet | 96.4 | GSM8K (0-shot CoT) |
| Jun 7, 2024 | 91.1 | GSM8K | |
| May 14, 2024 | 90.8 | GSM8K, 11-shot | |
| May 7, 2024 | 92.2 | GSM8K | |
| Apr 22, 2024 | 90.3 | GSM-8K (0-Shot; CoT) | |
| Apr 18, 2024 | 93 | GSM-8K (8-shot, CoT) [instruct] | |
| Mar 28, 2024 | Grok-1.5 | 90 | GSM8K (8-shot, maj@1) |
| Mar 4, 2024 | Claude 3 Opus | 95 | GSM8K (0-shot CoT) |
| Feb 26, 2024 | 91.2 | GSM8K maj@8 (8-shot) [maj@1 (5-shot) = 81.0] | |
| Feb 21, 2024 | 46.4 | GSM8K (maj@1) | |
| Feb 15, 2024 | 91.7 | GSM8K, 11-shot | |
| Feb 5, 2024 | 79.5 | GSM8K | |
| Jan 16, 2024 | GLM-4 | 87.6 | GSM8K (5-shot) |
| Dec 12, 2023 | 61.1 | Math (GSM8k, 8-shot) | |
| Dec 11, 2023 | 58.4 | GSM-8K (5-shot) [detailed table: 74.4] | |
| Dec 6, 2023 | 94.4 | GSM8K, Maj1@32 | |
| Nov 29, 2023 | 84.1 | GSM8K | |
| Nov 3, 2023 | Grok-1 | 62.9 | GSM8k (8-shot) |
| Oct 27, 2023 | ChatGLM3-6B | 72.3 | GSM8K |
| Sep 27, 2023 | 52.1 | GSM8K (8-shot, maj@8) | |
| Sep 25, 2023 | 60.1 | GSM8K (0-shot) | |
| Jul 11, 2023 | Claude 2 | 88 | GSM8k (0-shot CoT) |
| May 10, 2023 | 91 | GSM8K, CoT + self-consistency (greedy CoT = 80.7) | |
| Mar 14, 2023 | GPT-4 | 92 | GSM-8K (5-shot chain-of-thought) |
72 system cards report it, from 17 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 | | 91.7 | GSM8K, 8-shot CoT exact match, Table 6 | Capabilities |
| Oct 15, 2026 | | 91.7 | GSM8K, 8-shot CoT exact match, Table 6 | Capabilities |
| Sep 10, 2026 | | 93 | GSM8K, EM, 8-shot | Capabilities |
| Jul 31, 2026 | | 96.3 | GSM8K, TD, 95.8 no-think (Table 3) | Capabilities |
| Jun 4, 2026 | | 88.1 | GSM8K, 8-shot CoT EM | Capabilities |
| Feb 17, 2026 | GLM-5 technical report | 68.8 | GLM-5-Base, GSM8K (EM) (Table 11) | Capabilities |
| Dec 23, 2025 | | 92.3 | GSM8K, 8-shot accuracy, base model Table 2 | Capabilities |
| Nov 28, 2025 | LFM2-8B-A1B technical report | 84.4 | GSM8K, LFM2-8B-A1B (Table 7) | Capabilities |
| Nov 28, 2025 | LFM2-1.2B technical report | 58.3 | GSM8K, LFM2-1.2B (Table 6) | Capabilities |
| Nov 8, 2025 | | 67.7 | GSM8K 5-shot, English (Table 8). Arabic-GSM8K exact match 54.89 (Table 7) | Capabilities |
| Oct 8, 2025 | | 68.3 | GSM8K (Table 7, Table 23) | Capabilities |
| Sep 30, 2025 | | 93.3 | GSM8k, without reasoning (Table 13) | Capabilities |
| Sep 17, 2025 | | 77.6 | GSM8K, 8-shot CoT (Table 18) | Capabilities |
| Aug 25, 2025 | | 91.6 | GSM8K, OpenCompass (Table 14) | Capabilities |
| Aug 8, 2025 | GLM-4.5 technical report | 79.4 | GLM-4.5-Base, GSM8K (EM) (Table 2) | Capabilities |
| Jul 30, 2025 | | 83.6 | GSM8k, strict match 5-shot (Table 24) | Capabilities |
| Jul 17, 2025 | | GSM8K 8-shot, text-only, chart without printed numbers (Figure 6). Speech-synthesized GSM8k* 89.7 (Table 8) | Capabilities | |
| Jun 29, 2025 | | 96.6 | GSM8K (Table 5) | Capabilities |
| Apr 16, 2025 | | 58.4 | GSM8K (4-shot; EM) (Table 1, Table 2) | Capabilities |
| Mar 26, 2025 | | 88.7 | GSM8K (Table 1) | Capabilities |
| Mar 25, 2025 | | 89.2 | GSM8K, IT, 0-shot CoT (Table 18) | Capabilities |
| Mar 25, 2025 | | 95.9 | GSM8K, IT, 0-shot CoT (Table 18) | Capabilities |
| Mar 25, 2025 | | 62.8 | GSM8K, IT, 0-shot CoT (Table 18) | Capabilities |
| Mar 25, 2025 | | 94.4 | GSM8K, IT, 0-shot CoT (Table 18) | Capabilities |
| Mar 3, 2025 | | 88.6 | GSM-8K (8-Shot, CoT) (Table 7) | Capabilities |
| Feb 19, 2025 | | 95.3 | GSM8K (Table 4) | Capabilities |
| Jan 26, 2025 | | 94.8 | GSM8K (Table 6) | Capabilities |
| Jan 14, 2025 | | 94.8 | GSM8k, 0-shot CoT (Table 8) | Capabilities |
| Dec 31, 2024 | | 87.6 | GSM8K, 8-shot CoT (Tables 7 and 16) | Capabilities |
| Dec 19, 2024 | | 91.6 | GSM8K, Qwen2.5-7B-Instruct (Table 8) | Capabilities |
| Dec 19, 2024 | | 95.8 | GSM8K, Qwen2.5-72B-Instruct (Table 6) | Capabilities |
| Dec 19, 2024 | | 95.9 | GSM8K, Qwen2.5-32B-Instruct (Table 7) | Capabilities |
| Dec 19, 2024 | | 94.8 | GSM8K, Qwen2.5-14B-Instruct (Table 7) | Capabilities |
| Dec 19, 2024 | | 93.8 | GSM8K, Qwen2.5-Turbo (Table 7) | Capabilities |
| Nov 23, 2024 | | 84.5 | GSM8K (8-shot, CoT), post-trained (Table 2). Pretrained many-shot GSM8K (16-shot) = 60.0 (Table 14) | Capabilities |
| Nov 23, 2024 | | 95.1 | GSM8K (8-shot, CoT), post-trained (Table 2). Pretrained many-shot GSM8K (16-shot) = 83.0 (Table 14) | Capabilities |
| Nov 23, 2024 | | 96.8 | GSM8K (8-shot, CoT), post-trained (Table 2). Pretrained many-shot GSM8K (16-shot) = 90.0 (Table 14) | Capabilities |
| Nov 22, 2024 | | 95.5 | GSM8K (8 shot, CoT), RLVR (Table 4) | Capabilities |
| Aug 30, 2024 | | 89.6 | GSM-8K (8-Shot, CoT) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 82.5 | GSM-8K (8-Shot, CoT) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 91 | GSM-8K (8-Shot, CoT) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 88.7 | GSM8K (8-shot, CoT) (Table 3) | Capabilities |
| Aug 30, 2024 | | 86.2 | GSM8K (8-shot, CoT) (Table 3) | Capabilities |
| Jul 31, 2024 | | 68.6 | GSM8K, PT, 5-shot (Table 13) | Capabilities |
| Jul 31, 2024 | | 74 | GSM8K, PT, 5-shot (Table 13) | Capabilities |
| Jul 23, 2024 | | 84.5 | GSM-8K (CoT), 8-shot, em_maj1@1, instruction tuned (Instruction tuned models table) | Capabilities |
| Jul 23, 2024 | | 95.1 | GSM-8K (CoT), 8-shot, em_maj1@1, instruction tuned (Instruction tuned models table) | Capabilities |
| Jul 23, 2024 | | 96.8 | GSM-8K (CoT), 8-shot, em_maj1@1, instruction tuned (Instruction tuned models table) | Capabilities |
| Jul 15, 2024 | | 93.2 | GSM8K, Qwen2-72B-Instruct (Table 6) | Capabilities |
| Jun 20, 2024 | Claude 3.5 Sonnet model card | 96.4 | GSM8K, 0-shot CoT (Table 1) | Capabilities |
| Jun 18, 2024 | GLM-4 technical report | 87.6 | GLM-4 (0116), GSM8K, chain-of-thought (Table 2) | Capabilities |
| Jun 18, 2024 | ChatGLM3-6B technical report | 72.3 | ChatGLM3-6B-Base, GSM8K (Table 1) | Capabilities |
| May 7, 2024 | | 92.2 | GSM8K 8-shot, DeepSeek-V2 Chat (RL) (Table 3) | Capabilities |
| Apr 18, 2024 | | 79.6 | GSM-8K (8-shot, CoT), instruction tuned (Instruction tuned models table) | Capabilities |
| Apr 18, 2024 | | 93 | GSM-8K (8-shot, CoT), instruction tuned (Instruction tuned models table) | Capabilities |
| Mar 13, 2024 | | 46.4 | GSM8K, PT, maj@1 (Table 6). HuggingFace H6 run (Table 7): 50.9 | Capabilities |
| Mar 13, 2024 | | 17.7 | GSM8K, PT, maj@1 (Table 6) | Capabilities |
| Mar 4, 2024 | Claude 3 Sonnet model card | 92.3 | GSM8K, 0-shot CoT (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Opus model card | 95 | GSM8K, 0-shot CoT (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Haiku model card | 88.9 | GSM8K, 0-shot CoT (Table 1) | Capabilities |
| Jan 8, 2024 | | 58.4 | GSM-8K, 5-shot (Table 3). 8-shot maj@8 74.4 (Table 2) | Capabilities |
| Jan 5, 2024 | | 84.1 | GSM8K 0-shot, chain of thought, DeepSeek 67B Chat (Table 6). 86.7 with tool-integrated reasoning (Table 16) | Capabilities |
| Oct 10, 2023 | | 52.2 | GSM8K, 8-shot maj@8 (Table 2) | Capabilities |
| Sep 28, 2023 | | 54.1 | GSM8K 8-shot, higher of the two settings, 0-shot 50.3 (Table 5) | Capabilities |
| Sep 28, 2023 | | 60.1 | GSM8K 0-shot, higher of the two settings, 8-shot 59.3 (Table 5) | Capabilities |
| Sep 28, 2023 | | 27.8 | GSM8K 0-shot, higher of the two settings, 8-shot 19.5 (Table 5) | Capabilities |
| Jul 19, 2023 | | 14.6 | GSM8k, 8-shot, maj1@1, pretrained (Table 25) | Capabilities |
| Jul 19, 2023 | | 56.8 | GSM8k, 8-shot, maj1@1, pretrained (Table 25) | Capabilities |
| Jul 19, 2023 | | 42.2 | GSM8k, 8-shot, maj1@1, pretrained (Table 25) | Capabilities |
| Jul 19, 2023 | | 28.7 | GSM8k, 8-shot, maj1@1, pretrained (Table 25) | Capabilities |
| Jul 11, 2023 | Claude 2 model card | 88 | GSM8k (0-shot CoT) (4.3) | Capabilities |
| Mar 14, 2023 | GPT-4 technical report | 92 | GSM-8K, 5-shot chain-of-thought, part of the training set in pre-training mix (Table 2, base model) | Capabilities |
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