Gemini 2.5 Pro vs GPT-4.1: which model should you choose?
We compared Gemini 2.5 Pro and GPT-4.1 on price, context window and 12 benchmarks. GPT-4.1 has the stronger average benchmark score; Gemini 2.5 Pro is cheaper on a blended 3:1 basis.
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
GPT-4.1
GPT-4.1 is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $15.71 per million input tokens and $64.52 per million output tokens, and averages 82.4 across the index's benchmark suite.
- Input
- $15.71 / 1M
- Output
- $64.52 / 1M
- Context
- 1,000,000
- Avg score
- 82.4
- Open weights
- No
Specification comparison
| Spec | Gemini 2.5 Pro | GPT-4.1 | Edge |
|---|---|---|---|
| Input price / 1M | $15.20 | $15.71 | A |
| Output price / 1M | $57.83 | $64.52 | A |
| Context window | 200,000 | 1,000,000 | B |
| Max output | 16,384 | 16,384 | Tie |
| Average score | 81.6 | 82.4 | B |
| Arena Elo | 1455.0 | 1432.4 | A |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 2.5 Pro wins 4, GPT-4.1 wins 7, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 2.5 Pro | GPT-4.1 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.6 | 86.2 | 2.6 | GPT-4.1 |
| GPQA Diamond | 76.5 | 76.1 | 0.4 | Gemini 2.5 Pro |
| SWE-bench Verified | 70.2 | 72.2 | 2.0 | GPT-4.1 |
| HumanEval | 87.7 | 88.3 | 0.6 | GPT-4.1 |
| MATH-500 | 86.8 | 86.8 | 0.0 | Tie |
| AIME 2025 | 76.1 | 79.9 | 3.8 | GPT-4.1 |
| LiveCodeBench | 76.2 | 77.8 | 1.6 | GPT-4.1 |
| IFEval | 87.6 | 90.4 | 2.8 | GPT-4.1 |
| TruthfulQA | 82.8 | 81.8 | 1.0 | Gemini 2.5 Pro |
| HellaSwag | 89.7 | 90.3 | 0.6 | GPT-4.1 |
| MMMU | 80.5 | 76.3 | 4.2 | Gemini 2.5 Pro |
| Arena Elo | 1455.0 | 1432.4 | 22.6 | Gemini 2.5 Pro |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 2.5 Pro costs about $25.86 per million tokens and GPT-4.1 costs about $27.91. Gemini 2.5 Pro is roughly 7% cheaper than GPT-4.1. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-4.1 has the larger context window at 1,000,000 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, Gemini 2.5 Pro wins 4, GPT-4.1 wins 7, and 1 are tied. On the index average, GPT-4.1 leads Gemini 2.5 Pro by 0.8 points (82.4 versus 81.6).
The price picture favours Gemini 2.5 Pro. The context picture favours GPT-4.1. 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 for quality-sensitive work and Gemini 2.5 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Gemini 2.5 Pro if
- Unit cost matters and you want the cheaper blended price.
Choose GPT-4.1 if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
FAQ
Is Gemini 2.5 Pro better than GPT-4.1?
On the index average, GPT-4.1 leads Gemini 2.5 Pro by 0.8 points (82.4 versus 81.6). The better choice depends on price, context and your task. Gemini 2.5 Pro wins 4 benchmarks, GPT-4.1 wins 7.
Which is cheaper, Gemini 2.5 Pro or GPT-4.1?
Gemini 2.5 Pro is cheaper on a blended 3:1 basis at about $25.86 per million tokens, versus $27.91 for GPT-4.1.
Which has the larger context window?
GPT-4.1 has the larger context window at 1,000,000 tokens. Gemini 2.5 Pro has 200,000 tokens.
Are these models open weights?
Gemini 2.5 Pro is proprietary; GPT-4.1 is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-4.1 for quality-sensitive work and Gemini 2.5 Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.