Gemini 2.5 Pro Pro vs GPT-4.1 Max: which model should you choose?
We compared Gemini 2.5 Pro Pro and GPT-4.1 Max on price, context window and 12 benchmarks. GPT-4.1 Max has the stronger average benchmark score; GPT-4.1 Max is cheaper on a blended 3:1 basis.
Gemini 2.5 Pro Pro
Gemini 2.5 Pro Pro is a frontier model from Google DeepMind. It offers a 400,000-token context window, costs $17.16 per million input tokens and $90.89 per million output tokens, and averages 83.6 across the index's benchmark suite.
- Input
- $17.16 / 1M
- Output
- $90.89 / 1M
- Context
- 400,000
- Avg score
- 83.6
- Open weights
- No
GPT-4.1 Max
GPT-4.1 Max is a frontier model from OpenAI. It offers a 400,000-token context window, costs $17.34 per million input tokens and $66.38 per million output tokens, and averages 84.7 across the index's benchmark suite.
- Input
- $17.34 / 1M
- Output
- $66.38 / 1M
- Context
- 400,000
- Avg score
- 84.7
- Open weights
- No
Specification comparison
| Spec | Gemini 2.5 Pro Pro | GPT-4.1 Max | Edge |
|---|---|---|---|
| Input price / 1M | $17.16 | $17.34 | A |
| Output price / 1M | $90.89 | $66.38 | B |
| Context window | 400,000 | 400,000 | Tie |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 83.6 | 84.7 | B |
| Arena Elo | 1460.2 | 1467.6 | B |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 2.5 Pro Pro wins 2, GPT-4.1 Max wins 10, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 2.5 Pro Pro | GPT-4.1 Max | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 85.2 | 87.4 | 2.2 | GPT-4.1 Max |
| GPQA Diamond | 81.6 | 81.2 | 0.4 | Gemini 2.5 Pro Pro |
| SWE-bench Verified | 72.1 | 74.8 | 2.7 | GPT-4.1 Max |
| HumanEval | 88.4 | 88.8 | 0.4 | GPT-4.1 Max |
| MATH-500 | 86.8 | 87.3 | 0.5 | GPT-4.1 Max |
| AIME 2025 | 83.3 | 84.9 | 1.6 | GPT-4.1 Max |
| LiveCodeBench | 76.5 | 78.5 | 2.0 | GPT-4.1 Max |
| IFEval | 92.0 | 92.1 | 0.1 | GPT-4.1 Max |
| TruthfulQA | 85.5 | 85.4 | 0.1 | Gemini 2.5 Pro Pro |
| HellaSwag | 90.6 | 90.7 | 0.1 | GPT-4.1 Max |
| MMMU | 78.1 | 80.5 | 2.4 | GPT-4.1 Max |
| Arena Elo | 1460.2 | 1467.6 | 7.4 | GPT-4.1 Max |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 2.5 Pro Pro costs about $35.59 per million tokens and GPT-4.1 Max costs about $29.60. GPT-4.1 Max is roughly 17% cheaper than Gemini 2.5 Pro Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 400,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 2.5 Pro Pro wins 2, GPT-4.1 Max wins 10, and 0 are tied. On the index average, GPT-4.1 Max leads Gemini 2.5 Pro Pro by 1.1 points (84.7 versus 83.6).
The price picture favours GPT-4.1 Max. 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 GPT-4.1 Max for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose Gemini 2.5 Pro Pro if
- You prefer Google DeepMind's tooling or already use it.
Choose GPT-4.1 Max if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
FAQ
Is Gemini 2.5 Pro Pro better than GPT-4.1 Max?
On the index average, GPT-4.1 Max leads Gemini 2.5 Pro Pro by 1.1 points (84.7 versus 83.6). The better choice depends on price, context and your task. Gemini 2.5 Pro Pro wins 2 benchmarks, GPT-4.1 Max wins 10.
Which is cheaper, Gemini 2.5 Pro Pro or GPT-4.1 Max?
GPT-4.1 Max is cheaper on a blended 3:1 basis at about $29.60 per million tokens, versus $35.59 for Gemini 2.5 Pro Pro.
Which has the larger context window?
Both models list a 400,000-token context window.
Are these models open weights?
Gemini 2.5 Pro Pro is proprietary; GPT-4.1 Max is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-4.1 Max for quality-sensitive work, and check whether its price holds at your expected volume before committing.