ARC-Challenge
Models answer grade-school science multiple-choice questions that retrieval and word co-occurrence baselines got wrong. Scored by accuracy.
Saturated by frontier models, which score above 95%.
- Made by
- Allen Institute for AI
- Released
- March 2018
- Kind
- Exam questions
- Level
- School
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
- Strongest model tested
-
DeepSeek-V3 #154 on the capability index
- Last in a launch post
- August 2025, dropped after it saturated
- Last model added
- October 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC question set is partitioned into a Challenge Set and an Easy Set, where the Challenge Set contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurence algorithm. The dataset contains only natural, grade-school science questions (authored for human tests), and is the largest public-domain set of this kind (7,787 questions). We test several baselines on the Challenge Set, including leading neural models from the SQuAD and SNLI tasks, and find that none are able to significantly outperform a random baseline, reflecting the difficult nature of this task. We are also releasing the ARC Corpus, a corpus of 14M science sentences relevant to the task, and implementations of the three neural baseline models tested. Can your model perform better? We pose ARC as a challenge to the community.
Nemotron 3 Ultra 550B-A55B Base leads with 97.3%
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.
Nemotron 3 Ultra 550B-A55B Base leads with 97.3%, 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.
- 97.3
Nemotron 3 Ultra 550B-A55B Base
- 96.9
Llama 3.1-405B
- 96.4 Claude 3 Opus
- 96.3 GPT-4
- 96.0
Nemotron 3.5 Super 120B-A12B Base
- 95.3
DeepSeek-V3
- 95.1 PaLM 2-L
- 95.0 Sarvam-M
- 94.8
Llama 3.1-70B
- 94.0
Mistral Large
- 93.2 Claude 3 Sonnet
- 93.2
Nemotron 3.5 Lightning 30B-A3B Base
- 93.0
Llama 3-70B
- 92.3
DeepSeek-V2 (MoE-236B, May 2024)
- 91.9
Nemotron 3 Nano 30B-A3B Base
- 91.6
phi-3-medium 14B
- 91.3
Mistral Small 3
- 91.0 Claude 2
- 91.0
Phi-3.5-MoE
- 90.7
phi-3-small 7.4B
- 89.2 Claude 3 Haiku
- 85.8
Mixtral 8x7B
- 85.0
Apertus 70B
- 84.9
phi-3-mini 3.8B
- 84.6
Phi-3.5-mini
- 83.7
Phi-4 Mini
- 83.4
Llama 3.1-8B
- 78.6
Llama 3-8B
- 72.4
Qwen2.5-72B
- 71.4
Gemma 2 27B
- 70.6
Gemma 3 27B
- 68.9
Qwen2-72B
- 68.9
Gemma 3 12B
- 68.4
Gemma 2 9B
- 64.1
DeepSeek LLM 67B
- 61.6
Gemma 3n E4B
- 61.0
Falcon-H1-34B
- 57.4
Llama 2-70B
- 56.2
Gemma 3 4B
- 55.5
Mistral 7B v0.1
- 54.5
Llama 2-34B
- 53.5
Falcon-Arabic-7B
- 53.2
Gemma 7B
- 49.9
BitNet b1.58 2B4T
- 49.4
Llama 2-13B
- 45.9
Llama 2-7B
- 42.1
Gemma 2B
- 38.4
Gemma 3 1B
- 28.2
Gemma 3 270M
- 1
Nemotron 3 Ultra 550B-A55B Base 97.3%
- 2
Llama 3.1-405B 96.9%
- 3 Claude 3 Opus 96.4%
- 4 GPT-4 Mar 2023 96.3%
- 5
Nemotron 3.5 Super 120B-A12B Base 96%
- 6
DeepSeek-V3 95.3%
- 7 PaLM 2-L 95.1%
- 8 Sarvam-M 95%
- 9
Llama 3.1-70B 94.8%
- 10
Mistral Large 94%
- 11 Claude 3 Sonnet 93.2%
- 12
Nemotron 3.5 Lightning 30B-A3B Base 93.2%
- 13
Llama 3-70B 93%
- 14
DeepSeek-V2 (MoE-236B, May 2024) 92.3%
- 15
Nemotron 3 Nano 30B-A3B Base 91.9%
- 16
phi-3-medium 14B 91.6%
- 17
Mistral Small 3 91.3%
- 18 Claude 2 91%
- 19
Phi-3.5-MoE 91%
- 20
phi-3-small 7.4B 90.7%
- 21 Claude 3 Haiku 89.2%
- 22
Mixtral 8x7B 85.8%
- 23
Apertus 70B 85%
- 24
phi-3-mini 3.8B 84.9%
- 25
Phi-3.5-mini 84.6%
- 26
Phi-4 Mini 83.7%
- 27
Llama 3.1-8B 83.4%
- 28
Llama 3-8B 78.6%
- 29
Qwen2.5-72B 72.4%
- 30
Gemma 2 27B 71.4%
- 31
Gemma 3 27B 70.6%
- 32
Qwen2-72B 68.9%
- 33
Gemma 3 12B 68.9%
- 34
Gemma 2 9B 68.4%
- 35
DeepSeek LLM 67B 64.1%
- 36
Gemma 3n E4B 61.6%
- 37
Falcon-H1-34B 61%
- 38
Llama 2-70B 57.4%
- 39
Gemma 3 4B 56.2%
- 40
Mistral 7B v0.1 55.5%
- 41
Llama 2-34B 54.5%
- 42
Falcon-Arabic-7B 53.5%
- 43
Gemma 7B 53.2%
- 44
BitNet b1.58 2B4T 49.9%
- 45
Llama 2-13B 49.4%
- 46
Llama 2-7B 45.9%
- 47
Gemma 2B 42.1%
- 48
Gemma 3 1B 38.4%
- 49
Gemma 3 270M 28.2%
Source: Labs’ model cards
- 97.3
Nemotron 3 Ultra 550B-A55B Base
- 96.9
Llama 3.1-405B
- 96.4 Claude 3 Opus
- 96.3 GPT-4
- 96.0
Nemotron 3.5 Super 120B-A12B Base
- 95.3
DeepSeek-V3
- 95.1 PaLM 2-L
- 95.0 Sarvam-M
- 94.8
Llama 3.1-70B
- 94.5
Qwen2.5-72B
- 94.0
Mistral Large
- 93.2 Claude 3 Sonnet
- 93.2
Nemotron 3.5 Lightning 30B-A3B Base
- 93.0
Llama 3-70B
- 92.3
DeepSeek-V2 (MoE-236B, May 2024)
- 91.9
Nemotron 3 Nano 30B-A3B Base
- 91.6
phi-3-medium 14B
- 91.3
Mistral Small 3
- 91.0 Claude 2
- 91.0
Phi-3.5-MoE
- 90.7
phi-3-small 7.4B
- 89.2 Claude 3 Haiku
- 87.4 GPT-3.5 Turbo
- 87.3
Mixtral 8x7B
- 86.3 Claude Instant
- 86.1
Stable Beluga 2
- 85.2 davinci-002
- 85.2
PaLM (540B)
- 85.0
Apertus 70B
- 84.9
phi-3-mini 3.8B
- 84.6
Phi-3.5-mini
- 84.4
Qwen-14B
- 83.7
Phi-4 Mini
- 83.4
Llama 3.1-8B
- 82.8
Llama 3-8B
- 81.7 internlm-20b
- 78.6
Mistral 7B v0.1
- 78.3
Llama 2-70B
- 78.3
Gemma 7B
- 75.9
Phi-2
- 75.3
Qwen-7B
- 71.4
Gemma 2 27B
- 70.6
Gemma 3 27B
- 70.5
Qwen2.5-Coder-32B
- 69.5
LLaMA-65B
- 69.5 internlm-7b
- 68.9
Qwen2-72B
- 68.9
Gemma 3 12B
- 68.4
Gemma 2 9B
- 67.8
Falcon-180B
- 67.5
LLaMA-33B
- 66.0 Qwen2.5-Coder-14B
- 64.9 PaLM 2-M
- 64.3
DeepSeek-Coder-V2 236B
- 64.1
DeepSeek LLM 67B
- 61.9
Falcon-40B
- 61.6
Gemma 3n E4B
- 61.0
Falcon-H1-34B
- 61.0 chatglm2-6b
- 60.9
Qwen2.5-Coder (7B)
- 60.3
Llama 2-13B
- 59.6 PaLM 2-S
- 57.3 DeepSeek-Coder-V2-Lite-Base
- 56.2
Gemma 3 4B
- 55.6 Yi-9B
- 55.5
Nemotron-4 15B
- 54.5 INTELLECT-1
- 54.5
Llama 2-34B
- 53.5
Falcon-Arabic-7B
- 53.2 InstructGPT 175B
- 53.2 Qwen-1_8B
- 52.9 Qwen2.5-Coder-3B
- 52.7
LLaMA-13B
- 52.5 PaLM 62B
- 50.6
MPT-30B
- 50.3
Yi 6B
- 49.9
BitNet b1.58 2B4T
- 47.9
Falcon-7B
- 47.6
LLaMA-7B
- 47.2
StarCoder 2 15B
- 45.9
Llama 2-7B
- 45.2
Qwen2.5-Coder (1.5B)
- 44.4
Phi-1.5
- 43.2 vicuna-13b-v1.1
- 42.6
MPT-7B
- 42.2
DeepSeek Coder 33B
- 42.1
Gemma 2B
- 41.2
XGen-7B
- 41.1 GPT-NeoX-20B
- 39.6
Dolly 2.0-12b
- 39.1 RedPajama-INCITE-7B-Base
- 38.7 open_llama_7b
- 38.7
StarCoder 2 7B
- 38.4
Gemma 3 1B
- 38.0
Baichuan2-13B
- 36.4
DeepSeek Coder 6.7B
- 36.3 GPT-J-6B
- 35.8
OPT-1.3B
- 35.7 CodeQwen1.5-7B
- 34.4 Qwen2.5-Coder-0.5B
- 34.2
StarCoder 2 3B
- 32.5
Baichuan 2-7B
- 32.4
Cerebras-GPT-13B
- 28.2
Gemma 3 270M
- 27.0 stablelm-tuned-alpha-7b
- 25.4
DeepSeek Coder 1.3B
- 25.0 GPT-2 (1.5B)
- 1
Nemotron 3 Ultra 550B-A55B Base 97.3%
- 2
Llama 3.1-405B 96.9%
- 3 Claude 3 Opus 96.4%
- 4 GPT-4 Mar 2023 96.3%
- 5
Nemotron 3.5 Super 120B-A12B Base 96%
- 6
DeepSeek-V3 95.3%
- 7 PaLM 2-L 95.1%
- 8 Sarvam-M 95%
- 9
Llama 3.1-70B 94.8%
- 10
Qwen2.5-72B 94.5%
- 11
Mistral Large 94%
- 12 Claude 3 Sonnet 93.2%
- 13
Nemotron 3.5 Lightning 30B-A3B Base 93.2%
- 14
Llama 3-70B 93%
- 15
DeepSeek-V2 (MoE-236B, May 2024) 92.3%
- 16
Nemotron 3 Nano 30B-A3B Base 91.9%
- 17
phi-3-medium 14B 91.6%
- 18
Mistral Small 3 91.3%
- 19 Claude 2 91%
- 20
Phi-3.5-MoE 91%
- 21
phi-3-small 7.4B 90.7%
- 22 Claude 3 Haiku 89.2%
- 23 GPT-3.5 Turbo Nov 2023 87.4%
- 24
Mixtral 8x7B 87.3%
- 25 Claude Instant 86.3%
- 26
Stable Beluga 2 86.1%
- 27 davinci-002 85.2%
- 28
PaLM (540B) 85.2%
- 29
Apertus 70B 85%
- 30
phi-3-mini 3.8B 84.9%
- 31
Phi-3.5-mini 84.6%
- 32
Qwen-14B 84.4%
- 33
Phi-4 Mini 83.7%
- 34
Llama 3.1-8B 83.4%
- 35
Llama 3-8B 82.8%
- 36 internlm-20b 81.7%
- 37
Mistral 7B v0.1 78.6%
- 38
Llama 2-70B 78.3%
- 39
Gemma 7B 78.3%
- 40
Phi-2 75.9%
- 41
Qwen-7B 75.3%
- 42
Gemma 2 27B 71.4%
- 43
Gemma 3 27B 70.6%
- 44
Qwen2.5-Coder-32B 70.5%
- 45
LLaMA-65B 69.5%
- 46 internlm-7b 69.5%
- 47
Qwen2-72B 68.9%
- 48
Gemma 3 12B 68.9%
- 49
Gemma 2 9B 68.4%
- 50
Falcon-180B 67.8%
- 51
LLaMA-33B 67.5%
- 52 Qwen2.5-Coder-14B 66%
- 53 PaLM 2-M 64.9%
- 54
DeepSeek-Coder-V2 236B 64.3%
- 55
DeepSeek LLM 67B 64.1%
- 56
Falcon-40B 61.9%
- 57
Gemma 3n E4B 61.6%
- 58
Falcon-H1-34B 61%
- 59 chatglm2-6b 61%
- 60
Qwen2.5-Coder (7B) 60.9%
- 61
Llama 2-13B 60.3%
- 62 PaLM 2-S 59.6%
- 63 DeepSeek-Coder-V2-Lite-Base 57.3%
- 64
Gemma 3 4B 56.2%
- 65 Yi-9B 55.6%
- 66
Nemotron-4 15B 55.5%
- 67 INTELLECT-1 54.5%
- 68
Llama 2-34B 54.5%
- 69
Falcon-Arabic-7B 53.5%
- 70 InstructGPT 175B 53.2%
- 71 Qwen-1_8B 53.2%
- 72 Qwen2.5-Coder-3B 52.9%
- 73
LLaMA-13B 52.7%
- 74 PaLM 62B 52.5%
- 75
MPT-30B 50.6%
- 76
Yi 6B 50.3%
- 77
BitNet b1.58 2B4T 49.9%
- 78
Falcon-7B 47.9%
- 79
LLaMA-7B 47.6%
- 80
StarCoder 2 15B 47.2%
- 81
Llama 2-7B 45.9%
- 82
Qwen2.5-Coder (1.5B) 45.2%
- 83
Phi-1.5 44.4%
- 84 vicuna-13b-v1.1 43.2%
- 85
MPT-7B 42.6%
- 86
DeepSeek Coder 33B 42.2%
- 87
Gemma 2B 42.1%
- 88
XGen-7B 41.2%
- 89 GPT-NeoX-20B 41.1%
- 90
Dolly 2.0-12b 39.6%
- 91 RedPajama-INCITE-7B-Base 39.1%
- 92 open_llama_7b 38.7%
- 93
StarCoder 2 7B 38.7%
- 94
Gemma 3 1B 38.4%
- 95
Baichuan2-13B 38%
- 96
DeepSeek Coder 6.7B 36.4%
- 97 GPT-J-6B 36.3%
- 98
OPT-1.3B 35.8%
- 99 CodeQwen1.5-7B 35.7%
- 100 Qwen2.5-Coder-0.5B 34.4%
- 101
StarCoder 2 3B 34.2%
- 102
Baichuan 2-7B 32.5%
- 103
Cerebras-GPT-13B 32.4%
- 104
Gemma 3 270M 28.2%
- 105 stablelm-tuned-alpha-7b 27%
- 106
DeepSeek Coder 1.3B 25.4%
- 107 GPT-2 (1.5B) 25%
Sources: Labs’ model cards and Another board, via Epoch AI (CC BY 4.0)
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 97.3% | Jun 2026 | Lab model card | |
| 2 | | 96.9% | Jul 2024 | Lab model card | |
| 3 | Claude 3 Opus | 96.4% | Mar 2024 | Lab model card | |
| 4 | GPT-4 Mar 2023 | 96.3% | Mar 2023 | Lab model card | |
| 5 | | 96% | Oct 2026 | Lab model card | |
| 6 | | 95.3% | Dec 2024 | Lab model card | |
| 7 | PaLM 2-L | 95.1% | May 2023 | Lab model card | |
| 8 | Sarvam-M | 95% | May 2025 | Lab model card | |
| 9 | | 94.8% | Jul 2024 | Lab model card | |
| 10 | | 94% | Feb 2024 | Lab model card | |
| 11 | Claude 3 Sonnet | 93.2% | Mar 2024 | Lab model card | |
| 12 | | 93.2% | Oct 2026 | Lab model card | |
| 13 | | 93% | Apr 2024 | Lab model card | |
| 14 | | 92.3% | May 2024 | Lab model card | |
| 15 | | 91.9% | Dec 2025 | Lab model card | |
| 16 | | 91.6% | Aug 2024 | Lab model card | |
| 17 | | 91.3% | Jan 2025 | Lab model card | |
| 18 | Claude 2 | 91% | Jul 2023 | Lab model card | |
| 19 | | 91% | Aug 2024 | Lab model card | |
| 20 | | 90.7% | Aug 2024 | Lab model card | |
| 21 | Claude 3 Haiku | 89.2% | Mar 2024 | Lab model card | |
| 22 | | 85.8% | Dec 2023 | Lab model card | |
| 23 | | 85% | Sep 2025 | Lab model card | |
| 24 | | 84.9% | Aug 2024 | Lab model card | |
| 25 | | 84.6% | Aug 2024 | Lab model card | |
| 26 | | 83.7% | Feb 2025 | Lab model card | |
| 27 | | 83.4% | Jul 2024 | Lab model card | |
| 28 | | 78.6% | Apr 2024 | Lab model card | |
| 29 | | 72.4% | Sep 2024 | Lab model card | |
| 30 | | 71.4% | Jun 2024 | Lab model card | |
| 31 | | 70.6% | Mar 2025 | Lab model card | |
| 32 | | 68.9% | Jun 2024 | Lab model card | |
| 33 | | 68.9% | Mar 2025 | Lab model card | |
| 34 | | 68.4% | Jul 2024 | Lab model card | |
| 35 | | 64.1% | Jan 2024 | Lab model card | |
| 36 | | 61.6% | Jun 2025 | Lab model card | |
| 37 | | 61% | May 2025 | Lab model card | |
| 38 | | 57.4% | Jul 2023 | Lab model card | |
| 39 | | 56.2% | Mar 2025 | Lab model card | |
| 40 | | 55.5% | Sep 2023 | Lab model card | |
| 41 | | 54.5% | Jul 2023 | Lab model card | |
| 42 | | 53.5% | Nov 2025 | Lab model card | |
| 43 | | 53.2% | Feb 2024 | Lab model card | |
| 44 | | 49.9% | Apr 2025 | Lab model card | |
| 45 | | 49.4% | Jul 2023 | Lab model card | |
| 46 | | 45.9% | Jul 2023 | Lab model card | |
| 47 | | 42.1% | Mar 2024 | Lab model card | |
| 48 | | 38.4% | Mar 2025 | Lab model card | |
| 49 | | 28.2% | Aug 2025 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 97.3% | Jun 2026 | Lab model card | |
| 2 | | 96.9% | Jul 2024 | Lab model card | |
| 3 | Claude 3 Opus | 96.4% | Mar 2024 | Lab model card | |
| 4 | GPT-4 Mar 2023 | 96.3% | Mar 2023 | Lab model card | |
| 5 | | 96% | Oct 2026 | Lab model card | |
| 6 | | 95.3% | Dec 2024 | Lab model card | |
| 7 | PaLM 2-L | 95.1% | May 2023 | Lab model card | |
| 8 | Sarvam-M | 95% | May 2025 | Lab model card | |
| 9 | | 94.8% | Jul 2024 | Lab model card | |
| 10 | | 94.5% | Sep 2024 | Claim in DeepSeek-V3 Technical Report, via Epoch AI | |
| 11 | | 94% | Feb 2024 | Lab model card | |
| 12 | Claude 3 Sonnet | 93.2% | Mar 2024 | Lab model card | |
| 13 | | 93.2% | Oct 2026 | Lab model card | |
| 14 | | 93% | Apr 2024 | Lab model card | |
| 15 | | 92.3% | May 2024 | Lab model card | |
| 16 | | 91.9% | Dec 2025 | Lab model card | |
| 17 | | 91.6% | Aug 2024 | Lab model card | |
| 18 | | 91.3% | Jan 2025 | Lab model card | |
| 19 | Claude 2 | 91% | Jul 2023 | Lab model card | |
| 20 | | 91% | Aug 2024 | Lab model card | |
| 21 | | 90.7% | Aug 2024 | Lab model card | |
| 22 | Claude 3 Haiku | 89.2% | Mar 2024 | Lab model card | |
| 23 | GPT-3.5 Turbo Nov 2023 | 87.4% | Nov 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 24 | | 87.3% | Dec 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 25 | Claude Instant | 86.3% | Aug 2023 | Claim in Releasing Claude Instant 1.2, via Epoch AI | |
| 26 | | 86.1% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 27 | davinci-002 | 85.2% | Mar 2022 | Claim in GPT-4 Technical Report, via Epoch AI | |
| 28 | | 85.2% | Apr 2022 | Claim in GPT-4 Technical Report, via Epoch AI | |
| 29 | | 85% | Sep 2025 | Lab model card | |
| 30 | | 84.9% | Aug 2024 | Lab model card | |
| 31 | | 84.6% | Aug 2024 | Lab model card | |
| 32 | | 84.4% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 33 | | 83.7% | Feb 2025 | Lab model card | |
| 34 | | 83.4% | Jul 2024 | Lab model card | |
| 35 | | 82.8% | Apr 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 36 | internlm-20b | 81.7% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 37 | | 78.6% | Sep 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 38 | | 78.3% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 39 | | 78.3% | Feb 2024 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 40 | | 75.9% | Dec 2023 | Claim in Phi-3 Technical Report: A Highly Capable Language Model L…, via Epoch AI | |
| 41 | | 75.3% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 42 | | 71.4% | Jun 2024 | Lab model card | |
| 43 | | 70.6% | Mar 2025 | Lab model card | |
| 44 | | 70.5% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 45 | | 69.5% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 46 | internlm-7b | 69.5% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 47 | | 68.9% | Jun 2024 | Lab model card | |
| 48 | | 68.9% | Mar 2025 | Lab model card | |
| 49 | | 68.4% | Jul 2024 | Lab model card | |
| 50 | | 67.8% | Sep 2023 | Claim in The Falcon Series of Open Language Models, via Epoch AI | |
| 51 | | 67.5% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 52 | Qwen2.5-Coder-14B | 66% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 53 | PaLM 2-M | 64.9% | May 2023 | Claim in The Falcon Series of Open Language Models, via Epoch AI | |
| 54 | | 64.3% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 55 | | 64.1% | Jan 2024 | Lab model card | |
| 56 | | 61.9% | Mar 2023 | Claim in Falcon2-11B Technical Report, via Epoch AI | |
| 57 | | 61.6% | Jun 2025 | Lab model card | |
| 58 | | 61% | May 2025 | Lab model card | |
| 59 | chatglm2-6b | 61% | Jun 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 60 | | 60.9% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 61 | | 60.3% | Jul 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 62 | PaLM 2-S | 59.6% | May 2023 | Claim in The Falcon Series of Open Language Models, via Epoch AI | |
| 63 | DeepSeek-Coder-V2-Lite-Base | 57.3% | Jun 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 64 | | 56.2% | Mar 2025 | Lab model card | |
| 65 | Yi-9B | 55.6% | Mar 2024 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 66 | | 55.5% | Feb 2024 | Claim in Nemotron-4 15B Technical Report, via Epoch AI | |
| 67 | INTELLECT-1 | 54.5% | Nov 2024 | Claim in INTELLECT-1 Technical Report, via Epoch AI | |
| 68 | | 54.5% | Jul 2023 | Lab model card | |
| 69 | | 53.5% | Nov 2025 | Lab model card | |
| 70 | InstructGPT 175B | 53.2% | Jan 2022 | Claim in GLaM: Efficient Scaling of Language Models with Mixture-o…, via Epoch AI | |
| 71 | Qwen-1_8B | 53.2% | Nov 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 72 | Qwen2.5-Coder-3B | 52.9% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 73 | | 52.7% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 74 | PaLM 62B | 52.5% | Apr 2022 | Claim in LLaMA: Open and Efficient Foundation Language Models, via Epoch AI | |
| 75 | | 50.6% | Jun 2023 | Claim in Llama 2: Open Foundation and Fine-Tuned Chat Models, via Epoch AI | |
| 76 | | 50.3% | Nov 2023 | Claim in Yi: Open Foundation Models by 01.AI, via Epoch AI | |
| 77 | | 49.9% | Apr 2025 | Lab model card | |
| 78 | | 47.9% | Apr 2023 | Claim in Falcon2-11B Technical Report, via Epoch AI | |
| 79 | | 47.6% | Feb 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 80 | | 47.2% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 81 | | 45.9% | Jul 2023 | Lab model card | |
| 82 | | 45.2% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 83 | | 44.4% | Sep 2023 | Claim in Textbooks Are All You Need II: phi-1.5 technical report, via Epoch AI | |
| 84 | vicuna-13b-v1.1 | 43.2% | Apr 2023 | Claim in Textbooks Are All You Need II: phi-1.5 technical report, via Epoch AI | |
| 85 | | 42.6% | May 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 86 | | 42.2% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 87 | | 42.1% | Mar 2024 | Lab model card | |
| 88 | | 41.2% | Jun 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 89 | GPT-NeoX-20B | 41.1% | Apr 2022 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 90 | | 39.6% | Apr 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 91 | RedPajama-INCITE-7B-Base | 39.1% | May 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 92 | open_llama_7b | 38.7% | Jun 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 93 | | 38.7% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 94 | | 38.4% | Mar 2025 | Lab model card | |
| 95 | | 38% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 96 | | 36.4% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 97 | GPT-J-6B | 36.3% | Aug 2021 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 98 | | 35.8% | May 2022 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 99 | CodeQwen1.5-7B | 35.7% | Apr 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 100 | Qwen2.5-Coder-0.5B | 34.4% | Sep 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 101 | | 34.2% | Feb 2024 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 102 | | 32.5% | Sep 2023 | Claim in Qwen Technical Report, via Epoch AI | |
| 103 | | 32.4% | Mar 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 104 | | 28.2% | Aug 2025 | Lab model card | |
| 105 | stablelm-tuned-alpha-7b | 27% | Apr 2023 | Claim in XGen-7B Technical Report, via Epoch AI | |
| 106 | | 25.4% | Nov 2023 | Claim in Qwen2.5-Coder Technical Report, via Epoch AI | |
| 107 | GPT-2 (1.5B) | 25% | Nov 2019 | Claim in Textbooks Are All You Need II: phi-1.5 technical report, via Epoch AI |
The benchmark's own leaderboard has 7 entries, 0 of them models we track. Epoch AI collected claims for 75 models from labs’ reports and papers. Switch the scores above to rank them together.
4 models have held number one in labs’ own numbers since 2023 5 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
A claim for davinci-002 that Epoch AI collected passed 85% in March 2022
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Labs’ model cards and claims collected by Epoch AI (CC BY 4.0)
Labs dropped it from launch posts once the best score passed 85%
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 14, 2025 | 28.2 | ARC-c 0-shot (IT) | |
| Jun 26, 2025 | 61.6 | ARC-c 25-shot (PT) | |
| May 23, 2025 | Sarvam-M | 95 | ARC-C (0.95) |
| May 20, 2025 | 61 | ARC-C | |
| Apr 15, 2025 | 49.9 | ARC-Challenge | |
| Mar 12, 2025 | 70.6 | ARC-c 25-shot (PT) | |
| Feb 26, 2025 | 83.7 | ARC Challenge (10-shot) | |
| Jan 30, 2025 | 91.3 | ARC-c (0-shot) [base] | |
| Dec 26, 2024 | 95.3 | ARC-Challenge (Acc.) | |
| Sep 19, 2024 | 72.4 | ARC-C | |
| Aug 20, 2024 | 91 | ARC Challenge (10-shot) | |
| Jul 23, 2024 | 96.9 | ARC Challenge (0-shot) | |
| Jun 27, 2024 | 71.4 | ARC-c (25-shot) | |
| Jun 7, 2024 | 68.9 | ARC-C | |
| Apr 22, 2024 | 91 | Arc-C (10-Shot) | |
| Apr 18, 2024 | 93 | ARC-Challenge (25-shot) [pretrained] | |
| Mar 4, 2024 | Claude 3 Opus | 96.4 | ARC-Challenge (25-shot) |
| Feb 26, 2024 | 94 | Best of Arc C (25) [Arc C (5) = 94.2] / French Arc-C / German Arc-C / Spanish Arc-C / Italian Arc-C | |
| Feb 21, 2024 | 53.2 | ARC-c | |
| Dec 12, 2023 | ARC challenge (in Commonsense Reasoning average only) | ||
| Dec 11, 2023 | 85.8 | Best of ARC Challenge (25-shot) [detailed table Arc-c: 59.7] / French Arc-c / German Arc-c / Spanish Arc-c / Italian Arc-c | |
| Sep 27, 2023 | 55.5 | Arc-c | |
| Jul 11, 2023 | Claude 2 | 91 | ARC-Challenge (5-shot) |
| May 10, 2023 | 95.1 | ARC-C (4), CoT + SC, Table 5 (Table 2 1-shot = 69.2) | |
| Mar 14, 2023 | GPT-4 | 96.3 | AI2 Reasoning Challenge (ARC) (25-shot) |
43 system cards report it, from 10 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 | | 96 | ARC-Challenge, 25-shot acc_norm, Table 6 | Capabilities |
| Oct 15, 2026 | | 93.2 | ARC-Challenge, 25-shot acc_norm, Table 6 | Capabilities |
| Jun 4, 2026 | | 97.3 | ARC-Challenge, 25-shot acc_norm | Capabilities |
| Dec 23, 2025 | | 91.9 | ARC-Challenge, 25-shot acc_norm, base model Table 2 | Capabilities |
| Nov 8, 2025 | | 53.5 | ARC Challenge 25-shot (Table 8) | Capabilities |
| Sep 17, 2025 | | 85 | ARC Challenge Chat, 0-shot CoT, held-out test suite (Table 21). ARC Challenge Multilingual 37.3 | Capabilities |
| Jul 30, 2025 | | 61 | ARC-C, logprobs 0-shot (Table 24) | Capabilities |
| Apr 16, 2025 | | 49.9 | ARC-Challange (0-shot; Acc,norm) (Table 1, Table 3) | Capabilities |
| Mar 25, 2025 | | 56.2 | ARC-C, PT, 25-shot (Table 9) | Capabilities |
| Mar 25, 2025 | | 70.6 | ARC-C, PT, 25-shot (Table 9) | Capabilities |
| Mar 25, 2025 | | 38.4 | ARC-C, PT, 25-shot (Table 9) | Capabilities |
| Mar 25, 2025 | | 68.9 | ARC-C, PT, 25-shot (Table 9) | Capabilities |
| Mar 3, 2025 | | 83.7 | Arc-C (10-Shot) (Table 7) | Capabilities |
| Nov 23, 2024 | | 83.4 | ARC Challenge (0-shot), post-trained (Table 2) | Capabilities |
| Nov 23, 2024 | | 94.8 | ARC Challenge (0-shot), post-trained (Table 2) | Capabilities |
| Nov 23, 2024 | | 96.9 | ARC Challenge (0-shot), post-trained (Table 2) | Capabilities |
| Aug 30, 2024 | | 90.7 | Arc-C (10-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 84.9 | Arc-C (10-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 91.6 | Arc-C (10-Shot) (Section 3 table) | Capabilities |
| Aug 30, 2024 | | 91 | ARC Challenge (10-shot) (Table 3) | Capabilities |
| Aug 30, 2024 | | 84.6 | ARC Challenge (10-shot) (Table 3) | Capabilities |
| Jul 31, 2024 | | 68.4 | ARC-C, PT, 25-shot (Table 13) | Capabilities |
| Jul 31, 2024 | | 71.4 | ARC-C, PT, 25-shot (Table 13) | Capabilities |
| Jul 23, 2024 | | 83.4 | ARC-C, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
| Jul 23, 2024 | | 94.8 | ARC-C, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
| Jul 23, 2024 | | 96.9 | ARC-C, 0-shot, acc, instruction tuned (Instruction tuned models table) | Capabilities |
| May 7, 2024 | | 92.3 | ARC-Challenge 25-shot, DeepSeek-V2 Chat (RL) (Table 3) | Capabilities |
| Apr 18, 2024 | | 78.6 | ARC-Challenge (25-shot), pretrained (Base pretrained models table) | Capabilities |
| Apr 18, 2024 | | 93 | ARC-Challenge (25-shot), pretrained (Base pretrained models table) | Capabilities |
| Mar 13, 2024 | | 53.2 | ARC-c, PT (Table 6). HuggingFace H6 run (Table 7): 61.9 | Capabilities |
| Mar 13, 2024 | | 42.1 | ARC-c, PT (Table 6) | Capabilities |
| Mar 4, 2024 | Claude 3 Sonnet model card | 93.2 | ARC-Challenge, 25-shot (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Opus model card | 96.4 | ARC-Challenge, 25-shot (Table 1) | Capabilities |
| Mar 4, 2024 | Claude 3 Haiku model card | 89.2 | ARC-Challenge, 25-shot (Table 1) | Capabilities |
| Jan 8, 2024 | | 85.8 | ARC Challenge, 25-shot (Table 3). 0-shot 59.7 (Table 2) | Capabilities |
| Jan 5, 2024 | | 64.1 | ARC-Challenge, DeepSeek 67B Chat (Table 6) | Capabilities |
| Oct 10, 2023 | | 55.5 | Arc-c, 0-shot (Table 2) | Capabilities |
| Jul 19, 2023 | | 45.9 | ARC-c, 0-shot, pretrained (Table 20) | Capabilities |
| Jul 19, 2023 | | 57.4 | ARC-c, 0-shot, pretrained (Table 20) | Capabilities |
| Jul 19, 2023 | | 54.5 | ARC-c, 0-shot, pretrained (Table 20) | Capabilities |
| Jul 19, 2023 | | 49.4 | ARC-c, 0-shot, pretrained (Table 20) | Capabilities |
| Jul 11, 2023 | Claude 2 model card | 91 | ARC-Challenge (5-shot) (4.3) | Capabilities |
| Mar 14, 2023 | GPT-4 technical report | 96.3 | AI2 Reasoning Challenge (ARC), Challenge-set, 25-shot (Table 2, base model) | Capabilities |
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