V4 absorbed R1's job: reasoning is integrated into the flagship rather than a separate R-series release, with a longer context and better economics. R1 keeps two niches — its published RL methodology makes it the reference open reasoning model for research, and its 7B–70B distills remain the best way to run DeepSeek-style reasoning on consumer hardware.
DeepSeek R1 vs V4 at a glance
| DeepSeek R1 | DeepSeek V4 | |
|---|---|---|
| Vendor | DeepSeek (China) | DeepSeek (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | MIT — weights free to download, self-host, fine-tune, and use commercially | MIT — weights free to download, self-host, fine-tune, and use commercially |
| Parameters | 671B (37B active) | 1.6T (49B active) |
| Context window | 128K tokens | 1M tokens |
| Modalities | text | text |
| Pricing | Varies by hosting provider (open weights) | $1.74 in / $3.48 out per 1M tokens |
| Released | 2025-05-28 | 2026-04-24 |
Specs and pricing verified July 2026.
About DeepSeek R1
DeepSeek R1 is the model that made open-weight reasoning mainstream. The January 2025 release — and the stronger R1-0528 update — showed that RL-trained chain-of-thought reasoning could be published under MIT and still compete with proprietary reasoning models on math and code.
By mid-2026 R1 is no longer DeepSeek's frontier, but it remains historically important and practically useful: its distilled variants (1.5B to 70B) are still among the most-downloaded reasoning models on Hugging Face, and 'run DeepSeek R1 locally' remains the entry point through which many developers first self-host a serious model.
Full specs, benchmarks, and hardware guidance: the DeepSeek R1 page.
About DeepSeek V4
DeepSeek V4 Pro is the flagship of DeepSeek's V4 family, released on April 24, 2026, and the strongest argument yet that open-weight models compete at the frontier. It is a 1.6-trillion-parameter mixture-of-experts model that activates just 49B parameters per token, pairs a 1M-token context window with up to 384K tokens of output, and ships under a plain MIT license — no usage thresholds, no acceptable-use gate.
The headline engineering story is the attention stack. V4 combines Compressed Sparse Attention (CSA), which compresses KV entries 4× along the sequence with softmax-gated pooling, with Heavily Compressed Attention (HCA) at 128× compression. The practical result: at 1M-token context, V4 Pro needs roughly 27% of the single-token inference FLOPs and 10% of the KV cache of DeepSeek-V3.2. Long context stopped being a luxury and became the default across DeepSeek's services.
Full specs, benchmarks, and hardware guidance: the DeepSeek V4 page.
Choosing between them
Choose DeepSeek R1 for:
- Open-weight math and logic reasoning with visible chains of thought
- Local deployment via the distilled 7B–70B variants
- Research on RL-trained reasoning (fully published methodology)
Choose DeepSeek V4 for:
- Frontier-level reasoning and coding on an open license
- Very long documents and repositories (1M-token context as the default)
- Teams that need Anthropic/OpenAI API compatibility with open-weight economics
Frequently asked questions
Is DeepSeek R1 better than DeepSeek V4?
V4 absorbed R1's job: reasoning is integrated into the flagship rather than a separate R-series release, with a longer context and better economics. R1 keeps two niches — its published RL methodology makes it the reference open reasoning model for research, and its 7B–70B distills remain the best way to run DeepSeek-style reasoning on consumer hardware.
Which is cheaper: DeepSeek R1 or DeepSeek V4?
DeepSeek R1: Varies by hosting provider (open weights). DeepSeek V4: $1.74 in / $3.48 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host DeepSeek R1 and DeepSeek V4?
DeepSeek R1: yes — weights are published under MIT. DeepSeek V4: yes — weights are published under MIT.