DeepSeek-V3.2 vs GPT-4.1 Turbo: which model should you choose?
We compared DeepSeek-V3.2 and GPT-4.1 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
GPT-4.1 Turbo
GPT-4.1 Turbo is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $16.12 per million input tokens and $69.91 per million output tokens, and averages 82.9 across the index's benchmark suite.
- Input
- $16.12 / 1M
- Output
- $69.91 / 1M
- Context
- 1,000,000
- Avg score
- 82.9
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 | GPT-4.1 Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $5.88 | $16.12 | A |
| Output price / 1M | $24.70 | $69.91 | A |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 128,000 | B |
| Average score | 84.0 | 82.9 | A |
| Arena Elo | 1455.6 | 1446.2 | A |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 wins 9, GPT-4.1 Turbo wins 3, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 | GPT-4.1 Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.9 | 84.6 | 2.3 | DeepSeek-V3.2 |
| GPQA Diamond | 79.7 | 76.1 | 3.6 | DeepSeek-V3.2 |
| SWE-bench Verified | 74.4 | 74.7 | 0.3 | GPT-4.1 Turbo |
| HumanEval | 89.7 | 89.8 | 0.1 | GPT-4.1 Turbo |
| MATH-500 | 89.7 | 86.9 | 2.8 | DeepSeek-V3.2 |
| AIME 2025 | 79.6 | 77.4 | 2.2 | DeepSeek-V3.2 |
| LiveCodeBench | 80.4 | 78.7 | 1.7 | DeepSeek-V3.2 |
| IFEval | 89.4 | 88.2 | 1.2 | DeepSeek-V3.2 |
| TruthfulQA | 84.5 | 87.0 | 2.5 | GPT-4.1 Turbo |
| HellaSwag | 90.2 | 90.0 | 0.2 | DeepSeek-V3.2 |
| MMMU | 79.9 | 78.5 | 1.4 | DeepSeek-V3.2 |
| Arena Elo | 1455.6 | 1446.2 | 9.4 | 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 GPT-4.1 Turbo costs about $29.57. DeepSeek-V3.2 is roughly 64% cheaper than GPT-4.1 Turbo. 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-V3.2 wins 9, GPT-4.1 Turbo wins 3, and 0 are tied. On the index average, DeepSeek-V3.2 leads GPT-4.1 Turbo by 1.1 points (84.0 versus 82.9).
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-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 open weights for self-hosting or fine-tuning.
Choose GPT-4.1 Turbo if
- You need the larger 128,000-token output limit.
FAQ
Is DeepSeek-V3.2 better than GPT-4.1 Turbo?
On the index average, DeepSeek-V3.2 leads GPT-4.1 Turbo by 1.1 points (84.0 versus 82.9). The better choice depends on price, context and your task. DeepSeek-V3.2 wins 9 benchmarks, GPT-4.1 Turbo wins 3.
Which is cheaper, DeepSeek-V3.2 or GPT-4.1 Turbo?
DeepSeek-V3.2 is cheaper on a blended 3:1 basis at about $10.59 per million tokens, versus $29.57 for GPT-4.1 Turbo.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
DeepSeek-V3.2 is open weights; GPT-4.1 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.