DeepSeek-V3.2 vs o3: which model should you choose?
We compared DeepSeek-V3.2 and o3 on price, context window and 12 benchmarks. o3 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
o3 is a frontier model from OpenAI. It offers a 400,000-token context window, costs $16.80 per million input tokens and $81.80 per million output tokens, and averages 84.5 across the index's benchmark suite.
- Input
- $16.80 / 1M
- Output
- $81.80 / 1M
- Context
- 400,000
- Avg score
- 84.5
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 | o3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.88 | $16.80 | A |
| Output price / 1M | $24.70 | $81.80 | A |
| Context window | 1,000,000 | 400,000 | A |
| Max output | 8,192 | 4,096 | A |
| Average score | 84.0 | 84.5 | B |
| Arena Elo | 1455.6 | 1449.0 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 wins 5, o3 wins 6, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 | o3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.9 | 86.9 | 0.0 | Tie |
| GPQA Diamond | 79.7 | 78.3 | 1.4 | DeepSeek-V3.2 |
| SWE-bench Verified | 74.4 | 72.9 | 1.5 | DeepSeek-V3.2 |
| HumanEval | 89.7 | 89.8 | 0.1 | o3 |
| MATH-500 | 89.7 | 87.9 | 1.8 | DeepSeek-V3.2 |
| AIME 2025 | 79.6 | 84.4 | 4.8 | o3 |
| LiveCodeBench | 80.4 | 80.7 | 0.3 | o3 |
| IFEval | 89.4 | 88.7 | 0.7 | DeepSeek-V3.2 |
| TruthfulQA | 84.5 | 84.6 | 0.1 | o3 |
| HellaSwag | 90.2 | 92.7 | 2.5 | o3 |
| MMMU | 79.9 | 82.3 | 2.4 | o3 |
| Arena Elo | 1455.6 | 1449.0 | 6.6 | DeepSeek-V3.2 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 costs about $10.59 per million tokens and o3 costs about $33.05. DeepSeek-V3.2 is roughly 68% cheaper than o3. 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 400,000 for o3. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 wins 5, o3 wins 6, and 1 are tied. On the index average, o3 leads DeepSeek-V3.2 by 0.5 points (84.5 versus 84.0).
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 o3 for quality-sensitive work and DeepSeek-V3.2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 if
- 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.
- You need open weights for self-hosting or fine-tuning.
Choose o3 if
- You want the higher average benchmark score in this index.
FAQ
Is DeepSeek-V3.2 better than o3?
On the index average, o3 leads DeepSeek-V3.2 by 0.5 points (84.5 versus 84.0). The better choice depends on price, context and your task. DeepSeek-V3.2 wins 5 benchmarks, o3 wins 6.
Which is cheaper, DeepSeek-V3.2 or o3?
DeepSeek-V3.2 is cheaper on a blended 3:1 basis at about $10.59 per million tokens, versus $33.05 for o3.
Which has the larger context window?
DeepSeek-V3.2 has the larger context window at 1,000,000 tokens. o3 has 400,000 tokens.
Are these models open weights?
DeepSeek-V3.2 is open weights; o3 is proprietary.
Which should I choose for production?
If you need a single default, start with o3 for quality-sensitive work and DeepSeek-V3.2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.