GPT-4.1 Vision vs Mistral Large 3: which model should you choose?
We compared GPT-4.1 Vision and Mistral Large 3 on price, context window and 12 benchmarks. GPT-4.1 Vision has the stronger average benchmark score; Mistral Large 3 is cheaper on a blended 3:1 basis.
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
Mistral Large 3
Mistral Large 3 is a multimodal model from Mistral AI. It offers a 128,000-token context window, costs $5.41 per million input tokens and $23.83 per million output tokens, and averages 81.7 across the index's benchmark suite.
- Input
- $5.41 / 1M
- Output
- $23.83 / 1M
- Context
- 128,000
- Avg score
- 81.7
- Open weights
- Yes
Specification comparison
| Spec | GPT-4.1 Vision | Mistral Large 3 | Edge |
|---|---|---|---|
| Input price / 1M | $14.88 | $5.41 | B |
| Output price / 1M | $81.24 | $23.83 | B |
| Context window | 1,000,000 | 128,000 | A |
| Max output | 8,192 | 4,096 | A |
| Average score | 82.0 | 81.7 | A |
| Arena Elo | 1451.8 | 1448.6 | A |
| Open weights | No | Yes | B |
Benchmark comparison
GPT-4.1 Vision wins 6, Mistral Large 3 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-4.1 Vision | Mistral Large 3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 84.6 | 0.6 | Mistral Large 3 |
| GPQA Diamond | 79.3 | 79.6 | 0.3 | Mistral Large 3 |
| SWE-bench Verified | 72.4 | 71.3 | 1.1 | GPT-4.1 Vision |
| HumanEval | 86.8 | 88.6 | 1.8 | Mistral Large 3 |
| MATH-500 | 87.5 | 84.1 | 3.4 | GPT-4.1 Vision |
| AIME 2025 | 77.2 | 78.3 | 1.1 | Mistral Large 3 |
| LiveCodeBench | 74.3 | 74.4 | 0.1 | Mistral Large 3 |
| IFEval | 88.7 | 89.8 | 1.1 | Mistral Large 3 |
| TruthfulQA | 84.4 | 83.7 | 0.7 | GPT-4.1 Vision |
| HellaSwag | 89.7 | 88.2 | 1.5 | GPT-4.1 Vision |
| MMMU | 77.5 | 76.2 | 1.3 | GPT-4.1 Vision |
| Arena Elo | 1451.8 | 1448.6 | 3.2 | GPT-4.1 Vision |
Price analysis
On a blended 3:1 input-to-output basis, GPT-4.1 Vision costs about $31.47 per million tokens and Mistral Large 3 costs about $10.02. Mistral Large 3 is roughly 68% 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 128,000 for Mistral Large 3. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-4.1 Vision wins 6, Mistral Large 3 wins 6, and 0 are tied. On the index average, GPT-4.1 Vision leads Mistral Large 3 by 0.3 points (82.0 versus 81.7).
The price picture favours Mistral Large 3. 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 Mistral Large 3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
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.
- You need the larger 8,192-token output limit.
Choose Mistral Large 3 if
- Unit cost matters and you want the cheaper blended price.
- You need open weights for self-hosting or fine-tuning.
FAQ
Is GPT-4.1 Vision better than Mistral Large 3?
On the index average, GPT-4.1 Vision leads Mistral Large 3 by 0.3 points (82.0 versus 81.7). The better choice depends on price, context and your task. GPT-4.1 Vision wins 6 benchmarks, Mistral Large 3 wins 6.
Which is cheaper, GPT-4.1 Vision or Mistral Large 3?
Mistral Large 3 is cheaper on a blended 3:1 basis at about $10.02 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. Mistral Large 3 has 128,000 tokens.
Are these models open weights?
GPT-4.1 Vision is proprietary; Mistral Large 3 is open weights.
Which should I choose for production?
If you need a single default, start with GPT-4.1 Vision for quality-sensitive work and Mistral Large 3 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.