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A head-to-head benchmark comparison of DeepSeek-R1 across 11 evaluations.
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (.
Full DeepSeek-R1 specs →| Benchmark | DeepSeek-R1 |
|---|---|
| code | |
| Aider Polyglot | 53.3% |
| Codeforces Elo | 2,029 |
| LiveCodeBench v5 | 65.9% |
| math | |
| AIME 2024 | 86.7% |
| AIME 2025 | 80% |
| HMMT February 2025 | 47.08% |
| MATH-500 | 97.3% |
| Omni-MATH | 85 |
| Omni-MATH-HARD | 51.33% |
| reasoning | |
| GPQA Diamond | 71.5% |
| Humanity's Last Exam | 8.6% |
Best result per row highlighted in cyan. Each benchmark links to its definition and sources; each model links to its full scorecard.
DeepSeek-R1 has the strongest result on Aider Polyglot among the models compared here. See the code-category rows in the table for the full picture.
This comparison covers 11 benchmarks on which at least one of the selected models has a published score.
Scores are aggregated from official model cards, technical reports and standard public evaluations, and link back to each benchmark's source. They are updated as new results are published.
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