DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning (arXiv 2501.12948) 20 Jan 2025
Capabilities
- AIME 2024 28.9
- MATH-500 83.9
- GPQA Diamond 33.8
- LiveCodeBench 16.9
- Codeforces rating 954
DeepSeek-R1-Distill-Qwen-1.5B.
Open weights
Its best score on each benchmark, and where that ranks among every model with a score there. We rank each model once, at its best setting.
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| MathIF | 17.1% | #12 of 12 | Official leaderboard |
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | #143 of 202 | Epoch AI | |
| MATH-500 | 83.9% | #56 of 67 | Model card | |
| GPQA Diamond | 33.6% | #191 of 219 | Epoch AI | |
| AIME 2024 | 28.9% | #80 of 87 | Model card | |
| HMMT 2025 | 11.7% | #50 of 53 | Official leaderboard | |
| AIME 2025 | 20% | #51 of 53 | Official leaderboard |
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| LiveCodeBench | 16.9% | #70 of 73 | Model card | |
| Codeforces rating | 954 | #40 of 41 | Model card | |
| BigCodeBench | 7.50% | #69 of 69 | Official leaderboard |
| Benchmark | Score | Rank | Source | |
|---|---|---|---|---|
| BaxBench | 0% | #34 of 34 | Official leaderboard |
Every result in the longer documents the lab published with it. 5 of them aren't in the launch post.