ARC-C
The AI2 Reasoning Challenge (ARC) Challenge Set is a multiple-choice question-answering benchmark containing grade-school level science questions that require advanced reasoning capabilities. ARC-C specifically contains questions that were answered incorrectly by both retrieval-based and word co-occurrence algorithms, making it a particularly challenging subset designed to test commonsense reasoning abilities in AI systems.
Llama 3.1 405B Instruct from Meta currently leads the ARC-C leaderboard with a score of 0.969 across 33 evaluated AI models.
Llama 3.1 405B Instruct leads with 96.9%, followed by 
Claude 3 Opus at 96.4% and 
Nova Pro at 94.8%.
Progress Over Time
Interactive timeline showing model performance evolution on ARC-C
ARC-C Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | 405B | — | — | |||
| 2 | Anthropic | — | — | — | ||
| 3 | Amazon | — | — | — | ||
| 3 | 70B | — | — | |||
| 5 | Anthropic | — | — | — | ||
| 6 |  AI21 Labs | 398B | — | — | ||
| 7 | Amazon | — | — | — | ||
| 8 |  Mistral AI | 24B | — | — | ||
| 9 |  Microsoft | 60B | — | — | ||
| 10 | Amazon | — | — | — | ||
| 11 | Anthropic | — | — | — | ||
| 12 | AI21 Labs | 52B | — | — | ||
| 13 | Microsoft | 4B | — | — | ||
| 14 | Microsoft | 4B | — | — | ||
| 15 | 8B | — | — | |||
| 16 | 3B | — | — | |||
| 17 | Mistral AI | 8B | — | — | ||
| 18 |  Google | 27B | — | — | ||
| 19 |  Cohere | 104B | — | — | ||
| 20 |  Alibaba Cloud / Qwen Team | 32B | — | — | ||
| 21 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 22 | 70B | — | — | |||
| 23 | Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 24 | Google | 9B | — | — | ||
| 25 | Alibaba Cloud / Qwen Team | 15B | — | — | ||
| 26 |  Nous Research | 70B | — | — | ||
| 27 | Google | 8B | — | — | ||
| 27 | 2B | — | — | |||
| 29 | Alibaba Cloud / Qwen Team | 7B | — | — | ||
| 30 | 2B | — | — | |||
| 30 | Google | 8B | — | — | ||
| 32 | 8B | — | — | |||
| 33 |  Baidu | 21B | — | — |
FAQ
Common questions about ARC-C.
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