Gemini 2.5 Pro vs GPT-4.1 Vision: which model should you choose?
We compared Gemini 2.5 Pro and GPT-4.1 Vision on price, context window and 12 benchmarks. GPT-4.1 Vision 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 Vision
GPT-4.1 Vision is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $14.88 per million input tokens and $81.24 per million output tokens, and averages 82.0 across the index's benchmark suite.
- Input
- $14.88 / 1M
- Output
- $81.24 / 1M
- Context
- 1,000,000
- Avg score
- 82.0
- Open weights
- No
Specification comparison
| Spec | Gemini 2.5 Pro | GPT-4.1 Vision | Edge |
|---|---|---|---|
| Input price / 1M | $15.20 | $14.88 | B |
| Output price / 1M | $57.83 | $81.24 | A |
| Context window | 200,000 | 1,000,000 | B |
| Max output | 16,384 | 8,192 | A |
| Average score | 81.6 | 82.0 | B |
| Arena Elo | 1455.0 | 1451.8 | A |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 2.5 Pro wins 4, GPT-4.1 Vision 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 Vision | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.6 | 84.0 | 0.4 | GPT-4.1 Vision |
| GPQA Diamond | 76.5 | 79.3 | 2.8 | GPT-4.1 Vision |
| SWE-bench Verified | 70.2 | 72.4 | 2.2 | GPT-4.1 Vision |
| HumanEval | 87.7 | 86.8 | 0.9 | Gemini 2.5 Pro |
| MATH-500 | 86.8 | 87.5 | 0.7 | GPT-4.1 Vision |
| AIME 2025 | 76.1 | 77.2 | 1.1 | GPT-4.1 Vision |
| LiveCodeBench | 76.2 | 74.3 | 1.9 | Gemini 2.5 Pro |
| IFEval | 87.6 | 88.7 | 1.1 | GPT-4.1 Vision |
| TruthfulQA | 82.8 | 84.4 | 1.6 | GPT-4.1 Vision |
| HellaSwag | 89.7 | 89.7 | 0.0 | Tie |
| MMMU | 80.5 | 77.5 | 3.0 | Gemini 2.5 Pro |
| Arena Elo | 1455.0 | 1451.8 | 3.2 | 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 Vision costs about $31.47. Gemini 2.5 Pro is roughly 18% cheaper than GPT-4.1 Vision. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-4.1 Vision 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 Vision wins 7, and 1 are tied. On the index average, GPT-4.1 Vision leads Gemini 2.5 Pro by 0.4 points (82.0 versus 81.6).
The price picture favours Gemini 2.5 Pro. The context picture favours GPT-4.1 Vision. 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 Vision 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.
- You need the larger 16,384-token output limit.
- The workload needs multi-step reasoning.
Choose GPT-4.1 Vision 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 Vision?
On the index average, GPT-4.1 Vision leads Gemini 2.5 Pro by 0.4 points (82.0 versus 81.6). The better choice depends on price, context and your task. Gemini 2.5 Pro wins 4 benchmarks, GPT-4.1 Vision wins 7.
Which is cheaper, Gemini 2.5 Pro or GPT-4.1 Vision?
Gemini 2.5 Pro is cheaper on a blended 3:1 basis at about $25.86 per million tokens, versus $31.47 for GPT-4.1 Vision.
Which has the larger context window?
GPT-4.1 Vision 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 Vision is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-4.1 Vision 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.