DeepSeek-V3.2 Instruct vs Gemini 2.5 Pro Reasoning: which model should you choose?
We compared DeepSeek-V3.2 Instruct and Gemini 2.5 Pro Reasoning on price, context window and 12 benchmarks. Gemini 2.5 Pro Reasoning has the stronger average benchmark score; DeepSeek-V3.2 Instruct is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Instruct
DeepSeek-V3.2 Instruct is a frontier model from DeepSeek. It offers a 128,000-token context window, costs $5.91 per million input tokens and $29.01 per million output tokens, and averages 83.7 across the index's benchmark suite.
- Input
- $5.91 / 1M
- Output
- $29.01 / 1M
- Context
- 128,000
- Avg score
- 83.7
- Open weights
- Yes
Gemini 2.5 Pro Reasoning
Gemini 2.5 Pro Reasoning is a frontier model from Google DeepMind. It offers a 128,000-token context window, costs $16.81 per million input tokens and $67.95 per million output tokens, and averages 83.8 across the index's benchmark suite.
- Input
- $16.81 / 1M
- Output
- $67.95 / 1M
- Context
- 128,000
- Avg score
- 83.8
- Open weights
- No
Specification comparison
| Spec | DeepSeek-V3.2 Instruct | Gemini 2.5 Pro Reasoning | Edge |
|---|---|---|---|
| Input price / 1M | $5.91 | $16.81 | A |
| Output price / 1M | $29.01 | $67.95 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 4,096 | 4,096 | Tie |
| Average score | 83.7 | 83.8 | B |
| Arena Elo | 1454.4 | 1472.1 | B |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-V3.2 Instruct wins 6, Gemini 2.5 Pro Reasoning wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Instruct | Gemini 2.5 Pro Reasoning | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.6 | 86.8 | 2.2 | Gemini 2.5 Pro Reasoning |
| GPQA Diamond | 80.9 | 80.0 | 0.9 | DeepSeek-V3.2 Instruct |
| SWE-bench Verified | 75.2 | 71.2 | 4.0 | DeepSeek-V3.2 Instruct |
| HumanEval | 90.2 | 86.7 | 3.5 | DeepSeek-V3.2 Instruct |
| MATH-500 | 86.3 | 87.9 | 1.6 | Gemini 2.5 Pro Reasoning |
| AIME 2025 | 83.9 | 84.2 | 0.3 | Gemini 2.5 Pro Reasoning |
| LiveCodeBench | 75.2 | 74.8 | 0.4 | DeepSeek-V3.2 Instruct |
| IFEval | 91.3 | 90.8 | 0.5 | DeepSeek-V3.2 Instruct |
| TruthfulQA | 84.3 | 84.0 | 0.3 | DeepSeek-V3.2 Instruct |
| HellaSwag | 88.8 | 92.1 | 3.3 | Gemini 2.5 Pro Reasoning |
| MMMU | 80.4 | 82.8 | 2.4 | Gemini 2.5 Pro Reasoning |
| Arena Elo | 1454.4 | 1472.1 | 17.7 | Gemini 2.5 Pro Reasoning |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Instruct costs about $11.69 per million tokens and Gemini 2.5 Pro Reasoning costs about $29.59. DeepSeek-V3.2 Instruct is roughly 61% cheaper than Gemini 2.5 Pro Reasoning. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Instruct wins 6, Gemini 2.5 Pro Reasoning wins 6, and 0 are tied. On the index average, Gemini 2.5 Pro Reasoning leads DeepSeek-V3.2 Instruct by 0.1 points (83.8 versus 83.7).
The price picture favours DeepSeek-V3.2 Instruct. 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 Reasoning for quality-sensitive work and DeepSeek-V3.2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Instruct 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 Reasoning if
- You want the higher average benchmark score in this index.
FAQ
Is DeepSeek-V3.2 Instruct better than Gemini 2.5 Pro Reasoning?
On the index average, Gemini 2.5 Pro Reasoning leads DeepSeek-V3.2 Instruct by 0.1 points (83.8 versus 83.7). The better choice depends on price, context and your task. DeepSeek-V3.2 Instruct wins 6 benchmarks, Gemini 2.5 Pro Reasoning wins 6.
Which is cheaper, DeepSeek-V3.2 Instruct or Gemini 2.5 Pro Reasoning?
DeepSeek-V3.2 Instruct is cheaper on a blended 3:1 basis at about $11.69 per million tokens, versus $29.59 for Gemini 2.5 Pro Reasoning.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Instruct is open weights; Gemini 2.5 Pro Reasoning is proprietary.
Which should I choose for production?
If you need a single default, start with Gemini 2.5 Pro Reasoning for quality-sensitive work and DeepSeek-V3.2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.