DeepSeek-R1 Instruct vs DeepSeek-V3.2 Chat: which model should you choose?
We compared DeepSeek-R1 Instruct and DeepSeek-V3.2 Chat on price, context window and 12 benchmarks. DeepSeek-R1 Instruct has the stronger average benchmark score; DeepSeek-V3.2 Chat is cheaper on a blended 3:1 basis.
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
DeepSeek-V3.2 Chat
DeepSeek-V3.2 Chat is a multimodal model from DeepSeek. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $26.43 per million output tokens, and averages 82.5 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $26.43 / 1M
- Context
- 1,000,000
- Avg score
- 82.5
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Instruct | DeepSeek-V3.2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.90 | $5.80 | B |
| Output price / 1M | $26.49 | $26.43 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 83.1 | 82.5 | A |
| Arena Elo | 1447.7 | 1462.2 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Instruct wins 8, DeepSeek-V3.2 Chat wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Instruct | DeepSeek-V3.2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.9 | 82.5 | 2.4 | DeepSeek-R1 Instruct |
| GPQA Diamond | 81.4 | 78.0 | 3.4 | DeepSeek-R1 Instruct |
| SWE-bench Verified | 72.4 | 69.6 | 2.8 | DeepSeek-R1 Instruct |
| HumanEval | 88.0 | 87.9 | 0.1 | DeepSeek-R1 Instruct |
| MATH-500 | 86.2 | 90.2 | 4.0 | DeepSeek-V3.2 Chat |
| AIME 2025 | 77.6 | 81.0 | 3.4 | DeepSeek-V3.2 Chat |
| LiveCodeBench | 79.6 | 77.9 | 1.7 | DeepSeek-R1 Instruct |
| IFEval | 88.9 | 88.6 | 0.3 | DeepSeek-R1 Instruct |
| TruthfulQA | 82.2 | 86.3 | 4.1 | DeepSeek-V3.2 Chat |
| HellaSwag | 91.1 | 89.2 | 1.9 | DeepSeek-R1 Instruct |
| MMMU | 81.5 | 76.0 | 5.5 | DeepSeek-R1 Instruct |
| Arena Elo | 1447.7 | 1462.2 | 14.5 | DeepSeek-V3.2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Instruct costs about $11.05 per million tokens and DeepSeek-V3.2 Chat costs about $10.96. DeepSeek-V3.2 Chat is roughly 1% cheaper than DeepSeek-R1 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Instruct wins 8, DeepSeek-V3.2 Chat wins 4, and 0 are tied. On the index average, DeepSeek-R1 Instruct leads DeepSeek-V3.2 Chat by 0.6 points (83.1 versus 82.5).
The price picture favours DeepSeek-V3.2 Chat. The context picture is effectively a tie. 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-V3.2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Instruct if
- You want the higher average benchmark score in this index.
Choose DeepSeek-V3.2 Chat if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-R1 Instruct better than DeepSeek-V3.2 Chat?
On the index average, DeepSeek-R1 Instruct leads DeepSeek-V3.2 Chat by 0.6 points (83.1 versus 82.5). The better choice depends on price, context and your task. DeepSeek-R1 Instruct wins 8 benchmarks, DeepSeek-V3.2 Chat wins 4.
Which is cheaper, DeepSeek-R1 Instruct or DeepSeek-V3.2 Chat?
DeepSeek-V3.2 Chat is cheaper on a blended 3:1 basis at about $10.96 per million tokens, versus $11.05 for DeepSeek-R1 Instruct.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
DeepSeek-R1 Instruct is open weights; DeepSeek-V3.2 Chat 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-V3.2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.