DeepSeek-V3.2 Chat vs Gemini 2.5 Pro Turbo: which model should you choose?
We compared DeepSeek-V3.2 Chat and Gemini 2.5 Pro Turbo on price, context window and 12 benchmarks. Gemini 2.5 Pro Turbo has the stronger average benchmark score; DeepSeek-V3.2 Chat is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Chat
DeepSeek-V3.2 Chat is a multimodal model from DeepSeek. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $26.43 per million output tokens, and averages 82.5 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $26.43 / 1M
- Context
- 1,000,000
- Avg score
- 82.5
- Open weights
- Yes
Gemini 2.5 Pro Turbo
Gemini 2.5 Pro Turbo is a frontier model from Google DeepMind. It offers a 1,000,000-token context window, costs $16.32 per million input tokens and $72.61 per million output tokens, and averages 83.9 across the index's benchmark suite.
- Input
- $16.32 / 1M
- Output
- $72.61 / 1M
- Context
- 1,000,000
- Avg score
- 83.9
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 Chat | Gemini 2.5 Pro Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $5.80 | $16.32 | A |
| Output price / 1M | $26.43 | $72.61 | A |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 128,000 | B |
| Average score | 82.5 | 83.9 | B |
| Arena Elo | 1462.2 | 1464.0 | B |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 Chat wins 1, Gemini 2.5 Pro Turbo wins 11, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Chat | Gemini 2.5 Pro Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 82.5 | 82.9 | 0.4 | Gemini 2.5 Pro Turbo |
| GPQA Diamond | 78.0 | 81.7 | 3.7 | Gemini 2.5 Pro Turbo |
| SWE-bench Verified | 69.6 | 71.4 | 1.8 | Gemini 2.5 Pro Turbo |
| HumanEval | 87.9 | 88.1 | 0.2 | Gemini 2.5 Pro Turbo |
| MATH-500 | 90.2 | 90.6 | 0.4 | Gemini 2.5 Pro Turbo |
| AIME 2025 | 81.0 | 83.7 | 2.7 | Gemini 2.5 Pro Turbo |
| LiveCodeBench | 77.9 | 78.6 | 0.7 | Gemini 2.5 Pro Turbo |
| IFEval | 88.6 | 90.7 | 2.1 | Gemini 2.5 Pro Turbo |
| TruthfulQA | 86.3 | 85.9 | 0.4 | DeepSeek-V3.2 Chat |
| HellaSwag | 89.2 | 91.2 | 2.0 | Gemini 2.5 Pro Turbo |
| MMMU | 76.0 | 78.4 | 2.4 | Gemini 2.5 Pro Turbo |
| Arena Elo | 1462.2 | 1464.0 | 1.8 | Gemini 2.5 Pro Turbo |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Chat costs about $10.96 per million tokens and Gemini 2.5 Pro Turbo costs about $30.39. DeepSeek-V3.2 Chat is roughly 64% cheaper than Gemini 2.5 Pro 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 Chat wins 1, Gemini 2.5 Pro Turbo wins 11, and 0 are tied. On the index average, Gemini 2.5 Pro Turbo leads DeepSeek-V3.2 Chat by 1.4 points (83.9 versus 82.5).
The price picture favours DeepSeek-V3.2 Chat. 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 Gemini 2.5 Pro Turbo for quality-sensitive work and DeepSeek-V3.2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Chat if
- Unit cost matters and you want the cheaper blended price.
- You need open weights for self-hosting or fine-tuning.
Choose Gemini 2.5 Pro Turbo if
- You want the higher average benchmark score in this index.
- You need the larger 128,000-token output limit.
FAQ
Is DeepSeek-V3.2 Chat better than Gemini 2.5 Pro Turbo?
On the index average, Gemini 2.5 Pro Turbo leads DeepSeek-V3.2 Chat by 1.4 points (83.9 versus 82.5). The better choice depends on price, context and your task. DeepSeek-V3.2 Chat wins 1 benchmarks, Gemini 2.5 Pro Turbo wins 11.
Which is cheaper, DeepSeek-V3.2 Chat or Gemini 2.5 Pro Turbo?
DeepSeek-V3.2 Chat is cheaper on a blended 3:1 basis at about $10.96 per million tokens, versus $30.39 for Gemini 2.5 Pro 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 Chat is open weights; Gemini 2.5 Pro Turbo is proprietary.
Which should I choose for production?
If you need a single default, start with Gemini 2.5 Pro Turbo for quality-sensitive work and DeepSeek-V3.2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.