DeepSeek-R1 vs DeepSeek-R1 Instruct: which model should you choose?
We compared DeepSeek-R1 and DeepSeek-R1 Instruct on price, context window and 12 benchmarks. DeepSeek-R1 Instruct has the stronger average benchmark score; DeepSeek-R1 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 Instruct
DeepSeek-R1 Instruct is a frontier model from DeepSeek. It offers a 1,000,000-token context window, costs $5.90 per million input tokens and $26.49 per million output tokens, and averages 83.1 across the index's benchmark suite.
- Input
- $5.90 / 1M
- Output
- $26.49 / 1M
- Context
- 1,000,000
- Avg score
- 83.1
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 | DeepSeek-R1 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $5.90 | A |
| Output price / 1M | $25.23 | $26.49 | A |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 4,096 | 8,192 | B |
| Average score | 82.8 | 83.1 | B |
| Arena Elo | 1454.1 | 1447.7 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 wins 5, DeepSeek-R1 Instruct wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | DeepSeek-R1 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 84.9 | 1.1 | DeepSeek-R1 Instruct |
| GPQA Diamond | 77.8 | 81.4 | 3.6 | DeepSeek-R1 Instruct |
| SWE-bench Verified | 72.2 | 72.4 | 0.2 | DeepSeek-R1 Instruct |
| HumanEval | 86.4 | 88.0 | 1.6 | DeepSeek-R1 Instruct |
| MATH-500 | 89.1 | 86.2 | 2.9 | DeepSeek-R1 |
| AIME 2025 | 81.1 | 77.6 | 3.5 | DeepSeek-R1 |
| LiveCodeBench | 77.4 | 79.6 | 2.2 | DeepSeek-R1 Instruct |
| IFEval | 91.0 | 88.9 | 2.1 | DeepSeek-R1 |
| TruthfulQA | 82.9 | 82.2 | 0.7 | DeepSeek-R1 |
| HellaSwag | 89.8 | 91.1 | 1.3 | DeepSeek-R1 Instruct |
| MMMU | 79.5 | 81.5 | 2.0 | DeepSeek-R1 Instruct |
| Arena Elo | 1454.1 | 1447.7 | 6.4 | 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 Instruct costs about $11.05. DeepSeek-R1 is roughly 3% cheaper than DeepSeek-R1 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Instruct has the larger context window at 1,000,000 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 5, DeepSeek-R1 Instruct wins 7, and 0 are tied. On the index average, DeepSeek-R1 Instruct leads DeepSeek-R1 by 0.3 points (83.1 versus 82.8).
The price picture favours DeepSeek-R1. The context picture favours DeepSeek-R1 Instruct. 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 Instruct for quality-sensitive work and DeepSeek-R1 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 if
- Unit cost matters and you want the cheaper blended price.
Choose DeepSeek-R1 Instruct if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
- You need the larger 8,192-token output limit.
FAQ
Is DeepSeek-R1 better than DeepSeek-R1 Instruct?
On the index average, DeepSeek-R1 Instruct leads DeepSeek-R1 by 0.3 points (83.1 versus 82.8). The better choice depends on price, context and your task. DeepSeek-R1 wins 5 benchmarks, DeepSeek-R1 Instruct wins 7.
Which is cheaper, DeepSeek-R1 or DeepSeek-R1 Instruct?
DeepSeek-R1 is cheaper on a blended 3:1 basis at about $10.68 per million tokens, versus $11.05 for DeepSeek-R1 Instruct.
Which has the larger context window?
DeepSeek-R1 Instruct has the larger context window at 1,000,000 tokens. DeepSeek-R1 has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 is open weights; DeepSeek-R1 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 Instruct for quality-sensitive work and DeepSeek-R1 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.