DeepSeek-R1 vs DeepSeek-R1 Chat: which model should you choose?
We compared DeepSeek-R1 and DeepSeek-R1 Chat on price, context window and 12 benchmarks. DeepSeek-R1 has the stronger average benchmark score; DeepSeek-R1 Chat is cheaper on a blended 3:1 basis.
DeepSeek-R1
DeepSeek-R1 is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.83 per million input tokens and $25.23 per million output tokens, and averages 82.8 across the index's benchmark suite.
- Input
- $5.83 / 1M
- Output
- $25.23 / 1M
- Context
- 128,000
- Avg score
- 82.8
- Open weights
- Yes
DeepSeek-R1 Chat
DeepSeek-R1 Chat is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.62 per million input tokens and $21.49 per million output tokens, and averages 82.4 across the index's benchmark suite.
- Input
- $5.62 / 1M
- Output
- $21.49 / 1M
- Context
- 400,000
- Avg score
- 82.4
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 | DeepSeek-R1 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $5.62 | B |
| Output price / 1M | $25.23 | $21.49 | B |
| Context window | 128,000 | 400,000 | B |
| Max output | 4,096 | 128,000 | B |
| Average score | 82.8 | 82.4 | A |
| Arena Elo | 1454.1 | 1433.3 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 wins 8, DeepSeek-R1 Chat wins 3, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | DeepSeek-R1 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 83.4 | 0.4 | DeepSeek-R1 |
| GPQA Diamond | 77.8 | 80.0 | 2.2 | DeepSeek-R1 Chat |
| SWE-bench Verified | 72.2 | 71.7 | 0.5 | DeepSeek-R1 |
| HumanEval | 86.4 | 84.9 | 1.5 | DeepSeek-R1 |
| MATH-500 | 89.1 | 89.1 | 0.0 | Tie |
| AIME 2025 | 81.1 | 78.6 | 2.5 | DeepSeek-R1 |
| LiveCodeBench | 77.4 | 79.3 | 1.9 | DeepSeek-R1 Chat |
| IFEval | 91.0 | 89.5 | 1.5 | DeepSeek-R1 |
| TruthfulQA | 82.9 | 82.3 | 0.6 | DeepSeek-R1 |
| HellaSwag | 89.8 | 90.6 | 0.8 | DeepSeek-R1 Chat |
| MMMU | 79.5 | 77.1 | 2.4 | DeepSeek-R1 |
| Arena Elo | 1454.1 | 1433.3 | 20.8 | DeepSeek-R1 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 costs about $10.68 per million tokens and DeepSeek-R1 Chat costs about $9.59. DeepSeek-R1 Chat is roughly 10% cheaper than DeepSeek-R1. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Chat has the larger context window at 400,000 tokens, compared with 128,000 for DeepSeek-R1. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 wins 8, DeepSeek-R1 Chat wins 3, and 1 are tied. On the index average, DeepSeek-R1 leads DeepSeek-R1 Chat by 0.4 points (82.8 versus 82.4).
The price picture favours DeepSeek-R1 Chat. The context picture favours DeepSeek-R1 Chat. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with DeepSeek-R1 for quality-sensitive work and DeepSeek-R1 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 if
- You want the higher average benchmark score in this index.
Choose DeepSeek-R1 Chat if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 400,000-token context window.
- You need the larger 128,000-token output limit.
FAQ
Is DeepSeek-R1 better than DeepSeek-R1 Chat?
On the index average, DeepSeek-R1 leads DeepSeek-R1 Chat by 0.4 points (82.8 versus 82.4). The better choice depends on price, context and your task. DeepSeek-R1 wins 8 benchmarks, DeepSeek-R1 Chat wins 3.
Which is cheaper, DeepSeek-R1 or DeepSeek-R1 Chat?
DeepSeek-R1 Chat is cheaper on a blended 3:1 basis at about $9.59 per million tokens, versus $10.68 for DeepSeek-R1.
Which has the larger context window?
DeepSeek-R1 Chat has the larger context window at 400,000 tokens. DeepSeek-R1 has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 is open weights; DeepSeek-R1 Chat is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 for quality-sensitive work and DeepSeek-R1 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.