DeepSeek R1 671B
By DeepSeek · China
Overview
The reference open reasoning model — a 671B MoE with 37B active, released under MIT. Scores 97.3 on MATH-500, 79.8 on AIME, and 90.8 on MMLU.
When to pick this model
- You're running a dedicated inference server and need frontier reasoning
- You want the strongest open math, code, and logic model available
- You need an MIT-licensed model with no commercial restrictions
- You're benchmarking against closed frontier models like o1 or o3
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 400 GB |
| Q5_K_M | 480 GB |
| Q8_0 | 720 GB |
| FP16 (no quantization) | 1342 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
Published benchmark scores
| Benchmark | Score |
|---|---|
| MMLU | 90.8 |
| GPQA Diamond | 71.5 |
| MATH-500 | 97.3 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- MIT license — no commercial restrictions
- Reference open reasoning model
- MATH-500 score of 97.3
- R1-0528 update further sharpens reasoning
Limitations
- 400GB+ in Q4 — server-class hardware required
- Out of reach for any single-machine local setup
- Very long reasoning traces drive up latency
Architecture & training
Architecture: MoE (inherited from V3) · Multi-head Latent Attention · auxiliary-loss-free · RL-trained
Training: Distillation + multi-stage RL. R1-0528 update (May 2025).
The open reasoning gold standard — if you have the hardware to host it.
Quick start
ollama run deepseek-r1:671bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.