DeepSeek-R1 Base vs DeepSeek-V3.2 Base: which model should you choose?
We compared DeepSeek-R1 Base and DeepSeek-V3.2 Base on price, context window and 12 benchmarks. DeepSeek-V3.2 Base has the stronger average benchmark score; DeepSeek-R1 Base is cheaper on a blended 3:1 basis.
DeepSeek-R1 Base
DeepSeek-R1 Base is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.05 per million input tokens and $26.24 per million output tokens, and averages 80.1 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $26.24 / 1M
- Context
- 400,000
- Avg score
- 80.1
- Open weights
- Yes
DeepSeek-V3.2 Base
DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $27.10 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Base | DeepSeek-V3.2 Base | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.05 | Tie |
| Output price / 1M | $26.24 | $27.10 | A |
| Context window | 400,000 | 128,000 | A |
| Max output | 65,536 | 128,000 | B |
| Average score | 80.1 | 80.2 | B |
| Arena Elo | 1442.9 | 1417.9 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Base wins 6, DeepSeek-V3.2 Base wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | DeepSeek-V3.2 Base | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 84.3 | 3.3 | DeepSeek-V3.2 Base |
| GPQA Diamond | 76.2 | 77.1 | 0.9 | DeepSeek-V3.2 Base |
| SWE-bench Verified | 71.2 | 69.1 | 2.1 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 82.8 | 0.6 | DeepSeek-R1 Base |
| MATH-500 | 84.9 | 83.7 | 1.2 | DeepSeek-R1 Base |
| AIME 2025 | 73.8 | 74.3 | 0.5 | DeepSeek-V3.2 Base |
| LiveCodeBench | 76.0 | 73.1 | 2.9 | DeepSeek-R1 Base |
| IFEval | 88.2 | 86.9 | 1.3 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 83.5 | 1.3 | DeepSeek-V3.2 Base |
| HellaSwag | 90.0 | 90.2 | 0.2 | DeepSeek-V3.2 Base |
| MMMU | 74.2 | 76.7 | 2.5 | DeepSeek-V3.2 Base |
| Arena Elo | 1442.9 | 1417.9 | 25.0 | DeepSeek-R1 Base |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and DeepSeek-V3.2 Base costs about $10.56. DeepSeek-R1 Base is roughly 2% cheaper than DeepSeek-V3.2 Base. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Base has the larger context window at 400,000 tokens, compared with 128,000 for DeepSeek-V3.2 Base. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 6, DeepSeek-V3.2 Base wins 6, and 0 are tied. On the index average, DeepSeek-V3.2 Base leads DeepSeek-R1 Base by 0.1 points (80.2 versus 80.1).
The price picture favours DeepSeek-R1 Base. The context picture favours DeepSeek-R1 Base. 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 Base for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Base if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 400,000-token context window.
Choose DeepSeek-V3.2 Base if
- You want the higher average benchmark score in this index.
- You need the larger 128,000-token output limit.
FAQ
Is DeepSeek-R1 Base better than DeepSeek-V3.2 Base?
On the index average, DeepSeek-V3.2 Base leads DeepSeek-R1 Base by 0.1 points (80.2 versus 80.1). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 6 benchmarks, DeepSeek-V3.2 Base wins 6.
Which is cheaper, DeepSeek-R1 Base or DeepSeek-V3.2 Base?
DeepSeek-R1 Base is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
DeepSeek-R1 Base has the larger context window at 400,000 tokens. DeepSeek-V3.2 Base has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 Base is open weights; DeepSeek-V3.2 Base is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.