DeepSeek-R1-0528
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About this model
Compared to the previous version, the upgraded model shows significant improvements in handling complex reasoning tasks. For instance, in the AIME 2025 test, the model's accuracy has increased from 70% in the previous version to 87.5% in the current version. This advancement stems from enhanced thinking depth during the reasoning process: in the AIME test set, the previous model used an average of 12K tokens per question, whereas the new version averages 23K tokens per question.
Key model capabilities
Beyond its improved reasoning capabilities, this version also offers a reduced hallucination rate, enhanced support for function calling, and better experience for vibe coding.
| Category | Benchmark (Metric) | DeepSeek R1 | DeepSeek R1 0528 |
|---|---|---|---|
| General | |||
| MMLU-Redux (EM) | 92.9 | 93.4 | |
| MMLU-Pro (EM) | 84.0 | 85.0 | |
| GPQA-Diamond (Pass@1) | 71.5 | 81.0 | |
| SimpleQA (Correct) | 30.1 | 27.8 | |
| FRAMES (Acc.) | 82.5 | 83.0 | |
| Humanity's Last Exam (Pass@1) | 8.5 | 17.7 | |
| Code | |||
| LiveCodeBench (2408-2505) (Pass@1) | 63.5 | 73.3 | |
| Codeforces-Div1 (Rating) | 1530 | 1930 | |
| SWE Verified (Resolved) | 49.2 | 57.6 | |
| Aider-Polyglot (Acc.) | 53.3 | 71.6 | |
| Math | |||
| AIME 2024 (Pass@1) | 79.8 | 91.4 | |
| AIME 2025 (Pass@1) | 70.0 | 87.5 | |
| HMMT 2025 (Pass@1) | 41.7 | 79.4 | |
| CNMO 2024 (Pass@1) | 78.8 | 86.9 | |
| Tools | |||
| BFCL_v3_MultiTurn (Acc) | - | 37.0 | |
| Tau-Bench (Pass@1) | - | 53.5(Airline)/63.9(Retail) |
Note: We use Agentless framework to evaluate model performance on SWE-Verified. We only evaluate text-only prompts in HLE testsets. GPT-4.1 is employed to act user role in Tau-bench evaluation.