DeepSeek-V4-Pro-Max
DeepSeek-V4-Pro-Max is the maximum reasoning effort mode of DeepSeek-V4-Pro, a 1.6T-parameter MoE model with 49B activated parameters and a 1M-token context window. It introduces a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) for dramatically improved long-context efficiency, requiring only 27% of single-token inference FLOPs and 10% of KV cache compared with DeepSeek-V3.2 at 1M-token context. The model also incorporates Manifold-Constrained Hyper-Connections (mHC) for stable signal propagation and is trained with the Muon optimizer for faster convergence. Pre-trained on more than 32T tokens, V4-Pro-Max significantly advances open-source knowledge capabilities, achieves top-tier performance in coding benchmarks, and bridges the gap with leading closed-source models on reasoning and agentic tasks.
Benchmark results
| Benchmark | Score | Tags | Source |
|---|---|---|---|
| BrowseComp | 83.4% | self-reported llm-stats | link → |
| CodeForces | 100.0% | self-reported llm-stats | link → |
| CorpusQA 1M | 62.0% | self-reported llm-stats | link → |
| CSimpleQA | 84.4% | self-reported llm-stats | link → |
| GDPval-AA | 1,554 | self-reported llm-stats | link → |
| GPQA | 90.1% | self-reported llm-stats | link → |
| HMMT Feb 26 | 95.2% | self-reported llm-stats | link → |
| Humanity's Last Exam | 48.2% | self-reported llm-stats | link → |
| IMO-AnswerBench | 89.8% | self-reported llm-stats | link → |
| LiveCodeBench | 93.5% | self-reported llm-stats | link → |
| MathArena Apex | 90.2% | self-reported llm-stats | link → |
| MCP Atlas | 73.6% | self-reported llm-stats | link → |
| MMLU-Pro | 87.5% | self-reported llm-stats | link → |
| MRCR 1M | 83.5% | self-reported llm-stats | link → |
| SimpleQA | 57.9% | self-reported llm-stats | link → |
| SWE-bench Multilingual | 76.2% | self-reported llm-stats | link → |
| SWE-Bench Pro | 55.4% | self-reported llm-stats | link → |
| SWE-Bench Verified | 80.6% | self-reported llm-stats | link → |
| Terminal-Bench 2.0 | 67.9% | self-reported llm-stats | link → |
| Toolathlon | 51.8% | self-reported llm-stats | link → |