DeepSeek-V3.2 vs o3 Turbo: which model should you choose?
We compared DeepSeek-V3.2 and o3 Turbo 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-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
o3 Turbo
o3 Turbo is a frontier model from OpenAI. It offers a 200,000-token context window, costs $16.63 per million input tokens and $83.71 per million output tokens, and averages 83.4 across the index's benchmark suite.
- Input
- $16.63 / 1M
- Output
- $83.71 / 1M
- Context
- 200,000
- Avg score
- 83.4
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 | o3 Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $5.88 | $16.63 | A |
| Output price / 1M | $24.70 | $83.71 | A |
| Context window | 1,000,000 | 200,000 | A |
| Max output | 8,192 | 4,096 | A |
| Average score | 84.0 | 83.4 | A |
| Arena Elo | 1455.6 | 1456.2 | B |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 wins 7, o3 Turbo wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 | o3 Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.9 | 82.7 | 4.2 | DeepSeek-V3.2 |
| GPQA Diamond | 79.7 | 77.7 | 2.0 | DeepSeek-V3.2 |
| SWE-bench Verified | 74.4 | 70.7 | 3.7 | DeepSeek-V3.2 |
| HumanEval | 89.7 | 87.4 | 2.3 | DeepSeek-V3.2 |
| MATH-500 | 89.7 | 90.6 | 0.9 | o3 Turbo |
| AIME 2025 | 79.6 | 83.0 | 3.4 | o3 Turbo |
| LiveCodeBench | 80.4 | 80.7 | 0.3 | o3 Turbo |
| IFEval | 89.4 | 89.1 | 0.3 | DeepSeek-V3.2 |
| TruthfulQA | 84.5 | 83.6 | 0.9 | DeepSeek-V3.2 |
| HellaSwag | 90.2 | 91.8 | 1.6 | o3 Turbo |
| MMMU | 79.9 | 79.7 | 0.2 | DeepSeek-V3.2 |
| Arena Elo | 1455.6 | 1456.2 | 0.6 | o3 Turbo |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 costs about $10.59 per million tokens and o3 Turbo costs about $33.40. DeepSeek-V3.2 is roughly 68% cheaper than o3 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-V3.2 has the larger context window at 1,000,000 tokens, compared with 200,000 for o3 Turbo. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 wins 7, o3 Turbo wins 5, and 0 are tied. On the index average, DeepSeek-V3.2 leads o3 Turbo by 0.6 points (84.0 versus 83.4).
The price picture favours DeepSeek-V3.2. The context picture favours DeepSeek-V3.2. 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-V3.2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
- You need the larger 8,192-token output limit.
Choose o3 Turbo if
- You prefer OpenAI's tooling or already use it.
FAQ
Is DeepSeek-V3.2 better than o3 Turbo?
On the index average, DeepSeek-V3.2 leads o3 Turbo by 0.6 points (84.0 versus 83.4). The better choice depends on price, context and your task. DeepSeek-V3.2 wins 7 benchmarks, o3 Turbo wins 5.
Which is cheaper, DeepSeek-V3.2 or o3 Turbo?
DeepSeek-V3.2 is cheaper on a blended 3:1 basis at about $10.59 per million tokens, versus $33.40 for o3 Turbo.
Which has the larger context window?
DeepSeek-V3.2 has the larger context window at 1,000,000 tokens. o3 Turbo has 200,000 tokens.
Are these models open weights?
DeepSeek-V3.2 is open weights; o3 Turbo is proprietary.
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.