DeepSeek R1 vs DeepSeek V4-Pro — DeepSeek’s dedicated reasoning model versus its flagship. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spec | DeepSeek R1 | DeepSeek V4-Pro |
|---|---|---|
| Developer | DeepSeek | DeepSeek |
| Type | LLM (MoE, reasoning) | LLM (MoE) |
| Parameters | 671B total / 37B active (MoE) | 1.6T total / ~49B active (MoE) |
| Context window | 128K | 1M |
| Modality | Text → Text | Text → Text |
| License | MIT (open) | MIT (open) |
| Open weights | ✅ Yes | ✅ Yes |
| Input price ($/1M) | $0.50 | $0.435 |
| Output price ($/1M) | $2.15 | $0.87 |
| VRAM (4-bit) | ~400 GB | ~800 GB |
| Min GPU (local) | Multi-GPU server | Multi-GPU server (e.g. 8× H100 80GB) |
| Released | 2025 | 2026-04 |
Key differences
- Cost: DeepSeek V4-Pro is 68% cheaper than DeepSeek R1 on a blended-token basis.
- Context: DeepSeek V4-Pro wins on context window (1M vs 128K) — better for long documents, large codebases and big RAG inputs.
- Openness: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run DeepSeek R1 locally: ~~400 GB at 4-bit (min Multi-GPU server).
- Run DeepSeek V4-Pro locally: ~~800 GB at 4-bit (min Multi-GPU server (e.g. 8× H100 80GB)).
Which should you choose?
Choose DeepSeek R1 if it fits your existing stack or you prefer DeepSeek.
Choose DeepSeek V4-Pro if you want the lower per-token cost for high-volume workloads, or you need the larger context window.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM calculator · browse all 30+ models.
All specs and prices are pulled live from our AI models database and kept current. Compare either model against others, or estimate your own monthly spend with the free calculators above.