DeepSeek-R1 Chat vs Gemini 2.5 Pro: which model should you choose?
We compared DeepSeek-R1 Chat and Gemini 2.5 Pro on price, context window and 12 benchmarks. DeepSeek-R1 Chat has the stronger average benchmark score; DeepSeek-R1 Chat is cheaper on a blended 3:1 basis.
DeepSeek-R1 Chat
DeepSeek-R1 Chat is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.62 per million input tokens and $21.49 per million output tokens, and averages 82.4 across the index's benchmark suite.
- Input
- $5.62 / 1M
- Output
- $21.49 / 1M
- Context
- 400,000
- Avg score
- 82.4
- Open weights
- Yes
Gemini 2.5 Pro
Gemini 2.5 Pro is a multimodal model from Google DeepMind. It offers a 200,000-token context window, costs $15.20 per million input tokens and $57.83 per million output tokens, and averages 81.6 across the index's benchmark suite.
- Input
- $15.20 / 1M
- Output
- $57.83 / 1M
- Context
- 200,000
- Avg score
- 81.6
- Open weights
- No
Specification comparison
| Spec | DeepSeek-R1 Chat | Gemini 2.5 Pro | Edge |
|---|---|---|---|
| Input price / 1M | $5.62 | $15.20 | A |
| Output price / 1M | $21.49 | $57.83 | A |
| Context window | 400,000 | 200,000 | A |
| Max output | 128,000 | 16,384 | A |
| Average score | 82.4 | 81.6 | A |
| Arena Elo | 1433.3 | 1455.0 | B |
| Open weights | Yes | No | A |
Benchmark comparison
DeepSeek-R1 Chat wins 7, Gemini 2.5 Pro wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Chat | Gemini 2.5 Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.4 | 83.6 | 0.2 | Gemini 2.5 Pro |
| GPQA Diamond | 80.0 | 76.5 | 3.5 | DeepSeek-R1 Chat |
| SWE-bench Verified | 71.7 | 70.2 | 1.5 | DeepSeek-R1 Chat |
| HumanEval | 84.9 | 87.7 | 2.8 | Gemini 2.5 Pro |
| MATH-500 | 89.1 | 86.8 | 2.3 | DeepSeek-R1 Chat |
| AIME 2025 | 78.6 | 76.1 | 2.5 | DeepSeek-R1 Chat |
| LiveCodeBench | 79.3 | 76.2 | 3.1 | DeepSeek-R1 Chat |
| IFEval | 89.5 | 87.6 | 1.9 | DeepSeek-R1 Chat |
| TruthfulQA | 82.3 | 82.8 | 0.5 | Gemini 2.5 Pro |
| HellaSwag | 90.6 | 89.7 | 0.9 | DeepSeek-R1 Chat |
| MMMU | 77.1 | 80.5 | 3.4 | Gemini 2.5 Pro |
| Arena Elo | 1433.3 | 1455.0 | 21.7 | Gemini 2.5 Pro |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Chat costs about $9.59 per million tokens and Gemini 2.5 Pro costs about $25.86. DeepSeek-R1 Chat is roughly 63% cheaper than Gemini 2.5 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Chat has the larger context window at 400,000 tokens, compared with 200,000 for Gemini 2.5 Pro. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Chat wins 7, Gemini 2.5 Pro wins 5, and 0 are tied. On the index average, DeepSeek-R1 Chat leads Gemini 2.5 Pro by 0.8 points (82.4 versus 81.6).
The price picture favours DeepSeek-R1 Chat. The context picture favours DeepSeek-R1 Chat. 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-R1 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 Chat 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 400,000-token context window.
- You need the larger 128,000-token output limit.
Choose Gemini 2.5 Pro if
- You prefer Google DeepMind's tooling or already use it.
FAQ
Is DeepSeek-R1 Chat better than Gemini 2.5 Pro?
On the index average, DeepSeek-R1 Chat leads Gemini 2.5 Pro by 0.8 points (82.4 versus 81.6). The better choice depends on price, context and your task. DeepSeek-R1 Chat wins 7 benchmarks, Gemini 2.5 Pro wins 5.
Which is cheaper, DeepSeek-R1 Chat or Gemini 2.5 Pro?
DeepSeek-R1 Chat is cheaper on a blended 3:1 basis at about $9.59 per million tokens, versus $25.86 for Gemini 2.5 Pro.
Which has the larger context window?
DeepSeek-R1 Chat has the larger context window at 400,000 tokens. Gemini 2.5 Pro has 200,000 tokens.
Are these models open weights?
DeepSeek-R1 Chat is open weights; Gemini 2.5 Pro is proprietary.
Which should I choose for production?
If you need a single default, start with DeepSeek-R1 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.