GPT-4.1 Vision vs o3 Vision: which model should you choose?
We compared GPT-4.1 Vision and o3 Vision on price, context window and 12 benchmarks. GPT-4.1 Vision has the stronger average benchmark score; o3 Vision 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
o3 Vision
o3 Vision is a multimodal model from OpenAI. It offers a 1,000,000-token context window, costs $15.24 per million input tokens and $65.25 per million output tokens, and averages 81.8 across the index's benchmark suite.
- Input
- $15.24 / 1M
- Output
- $65.25 / 1M
- Context
- 1,000,000
- Avg score
- 81.8
- Open weights
- No
Specification comparison
| Spec | GPT-4.1 Vision | o3 Vision | Edge |
|---|---|---|---|
| Input price / 1M | $14.88 | $15.24 | A |
| Output price / 1M | $81.24 | $65.25 | B |
| Context window | 1,000,000 | 1,000,000 | Tie |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 82.0 | 81.8 | A |
| Arena Elo | 1451.8 | 1436.5 | A |
| Open weights | No | No | Tie |
Benchmark comparison
GPT-4.1 Vision wins 8, o3 Vision wins 3, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-4.1 Vision | o3 Vision | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 83.6 | 0.4 | GPT-4.1 Vision |
| GPQA Diamond | 79.3 | 77.8 | 1.5 | GPT-4.1 Vision |
| SWE-bench Verified | 72.4 | 71.8 | 0.6 | GPT-4.1 Vision |
| HumanEval | 86.8 | 85.6 | 1.2 | GPT-4.1 Vision |
| MATH-500 | 87.5 | 84.4 | 3.1 | GPT-4.1 Vision |
| AIME 2025 | 77.2 | 82.4 | 5.2 | o3 Vision |
| LiveCodeBench | 74.3 | 73.7 | 0.6 | GPT-4.1 Vision |
| IFEval | 88.7 | 89.3 | 0.6 | o3 Vision |
| TruthfulQA | 84.4 | 85.8 | 1.4 | o3 Vision |
| HellaSwag | 89.7 | 89.7 | 0.0 | Tie |
| MMMU | 77.5 | 75.7 | 1.8 | GPT-4.1 Vision |
| Arena Elo | 1451.8 | 1436.5 | 15.3 | 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 o3 Vision costs about $27.74. o3 Vision is roughly 12% 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.
Both models list a 1,000,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-4.1 Vision wins 8, o3 Vision wins 3, and 1 are tied. On the index average, GPT-4.1 Vision leads o3 Vision by 0.2 points (82.0 versus 81.8).
The price picture favours o3 Vision. 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 Vision for quality-sensitive work and o3 Vision 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.
Choose o3 Vision if
- Unit cost matters and you want the cheaper blended price.
- The workload needs multi-step reasoning.
FAQ
Is GPT-4.1 Vision better than o3 Vision?
On the index average, GPT-4.1 Vision leads o3 Vision by 0.2 points (82.0 versus 81.8). The better choice depends on price, context and your task. GPT-4.1 Vision wins 8 benchmarks, o3 Vision wins 3.
Which is cheaper, GPT-4.1 Vision or o3 Vision?
o3 Vision is cheaper on a blended 3:1 basis at about $27.74 per million tokens, versus $31.47 for GPT-4.1 Vision.
Which has the larger context window?
Both models list a 1,000,000-token context window.
Are these models open weights?
GPT-4.1 Vision is proprietary; o3 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 o3 Vision for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.