DeepSeek-R1 Instruct vs DeepSeek-V3.2: which model should you choose?
We compared DeepSeek-R1 Instruct and DeepSeek-V3.2 on price, context window and 12 benchmarks. DeepSeek-V3.2 has the stronger average benchmark score; DeepSeek-V3.2 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
DeepSeek-V3.2 is a frontier model from DeepSeek. It offers a 1,000,000-token context window, costs $5.88 per million input tokens and $24.70 per million output tokens, and averages 84.0 across the index's benchmark suite.
- Input
- $5.88 / 1M
- Output
- $24.70 / 1M
- Context
- 1,000,000
- Avg score
- 84.0
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Instruct | DeepSeek-V3.2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.90 | $5.88 | B |
| Output price / 1M | $26.49 | $24.70 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 83.1 | 84.0 | B |
| Arena Elo | 1447.7 | 1455.6 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Instruct wins 3, DeepSeek-V3.2 wins 9, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Instruct | DeepSeek-V3.2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.9 | 86.9 | 2.0 | DeepSeek-V3.2 |
| GPQA Diamond | 81.4 | 79.7 | 1.7 | DeepSeek-R1 Instruct |
| SWE-bench Verified | 72.4 | 74.4 | 2.0 | DeepSeek-V3.2 |
| HumanEval | 88.0 | 89.7 | 1.7 | DeepSeek-V3.2 |
| MATH-500 | 86.2 | 89.7 | 3.5 | DeepSeek-V3.2 |
| AIME 2025 | 77.6 | 79.6 | 2.0 | DeepSeek-V3.2 |
| LiveCodeBench | 79.6 | 80.4 | 0.8 | DeepSeek-V3.2 |
| IFEval | 88.9 | 89.4 | 0.5 | DeepSeek-V3.2 |
| TruthfulQA | 82.2 | 84.5 | 2.3 | DeepSeek-V3.2 |
| HellaSwag | 91.1 | 90.2 | 0.9 | DeepSeek-R1 Instruct |
| MMMU | 81.5 | 79.9 | 1.6 | DeepSeek-R1 Instruct |
| Arena Elo | 1447.7 | 1455.6 | 7.9 | DeepSeek-V3.2 |
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 costs about $10.59. DeepSeek-V3.2 is roughly 4% 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 3, DeepSeek-V3.2 wins 9, and 0 are tied. On the index average, DeepSeek-V3.2 leads DeepSeek-R1 Instruct by 0.9 points (84.0 versus 83.1).
The price picture favours DeepSeek-V3.2. 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-V3.2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 Instruct if
- You prefer DeepSeek's tooling or already use it.
Choose DeepSeek-V3.2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-R1 Instruct better than DeepSeek-V3.2?
On the index average, DeepSeek-V3.2 leads DeepSeek-R1 Instruct by 0.9 points (84.0 versus 83.1). The better choice depends on price, context and your task. DeepSeek-R1 Instruct wins 3 benchmarks, DeepSeek-V3.2 wins 9.
Which is cheaper, DeepSeek-R1 Instruct or DeepSeek-V3.2?
DeepSeek-V3.2 is cheaper on a blended 3:1 basis at about $10.59 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 is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.