GPT-5.1 vs o3 Reasoning: which model should you choose?
We compared GPT-5.1 and o3 Reasoning on price, context window and 12 benchmarks. GPT-5.1 has the stronger average benchmark score; o3 Reasoning is cheaper on a blended 3:1 basis.
GPT-5.1
GPT-5.1 is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $19.01 per million input tokens and $69.84 per million output tokens, and averages 86.0 across the index's benchmark suite.
- Input
- $19.01 / 1M
- Output
- $69.84 / 1M
- Context
- 2,000,000
- Avg score
- 86.0
- Open weights
- No
o3 Reasoning
o3 Reasoning is a frontier model from OpenAI. It offers a 200,000-token context window, costs $18.07 per million input tokens and $70.65 per million output tokens, and averages 85.3 across the index's benchmark suite.
- Input
- $18.07 / 1M
- Output
- $70.65 / 1M
- Context
- 200,000
- Avg score
- 85.3
- Open weights
- No
Specification comparison
| Spec | GPT-5.1 | o3 Reasoning | Edge |
|---|---|---|---|
| Input price / 1M | $19.01 | $18.07 | B |
| Output price / 1M | $69.84 | $70.65 | A |
| Context window | 2,000,000 | 200,000 | A |
| Max output | 128,000 | 16,384 | A |
| Average score | 86.0 | 85.3 | A |
| Arena Elo | 1462.5 | 1456.9 | A |
| Open weights | No | No | Tie |
Benchmark comparison
GPT-5.1 wins 8, o3 Reasoning wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-5.1 | o3 Reasoning | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 86.1 | 87.7 | 1.6 | o3 Reasoning |
| GPQA Diamond | 82.3 | 79.6 | 2.7 | GPT-5.1 |
| SWE-bench Verified | 76.1 | 74.1 | 2.0 | GPT-5.1 |
| HumanEval | 90.6 | 91.3 | 0.7 | o3 Reasoning |
| MATH-500 | 90.5 | 92.0 | 1.5 | o3 Reasoning |
| AIME 2025 | 85.6 | 80.5 | 5.1 | GPT-5.1 |
| LiveCodeBench | 78.9 | 82.7 | 3.8 | o3 Reasoning |
| IFEval | 90.9 | 90.6 | 0.3 | GPT-5.1 |
| TruthfulQA | 89.2 | 86.5 | 2.7 | GPT-5.1 |
| HellaSwag | 92.2 | 90.9 | 1.3 | GPT-5.1 |
| MMMU | 83.3 | 82.3 | 1.0 | GPT-5.1 |
| Arena Elo | 1462.5 | 1456.9 | 5.6 | GPT-5.1 |
Price analysis
On a blended 3:1 input-to-output basis, GPT-5.1 costs about $31.72 per million tokens and o3 Reasoning costs about $31.22. o3 Reasoning is roughly 2% cheaper than GPT-5.1. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-5.1 has the larger context window at 2,000,000 tokens, compared with 200,000 for o3 Reasoning. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-5.1 wins 8, o3 Reasoning wins 4, and 0 are tied. On the index average, GPT-5.1 leads o3 Reasoning by 0.7 points (86.0 versus 85.3).
The price picture favours o3 Reasoning. The context picture favours GPT-5.1. 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-5.1 for quality-sensitive work and o3 Reasoning for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GPT-5.1 if
- You want the higher average benchmark score in this index.
- You need the larger 2,000,000-token context window.
- You need the larger 128,000-token output limit.
Choose o3 Reasoning if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is GPT-5.1 better than o3 Reasoning?
On the index average, GPT-5.1 leads o3 Reasoning by 0.7 points (86.0 versus 85.3). The better choice depends on price, context and your task. GPT-5.1 wins 8 benchmarks, o3 Reasoning wins 4.
Which is cheaper, GPT-5.1 or o3 Reasoning?
o3 Reasoning is cheaper on a blended 3:1 basis at about $31.22 per million tokens, versus $31.72 for GPT-5.1.
Which has the larger context window?
GPT-5.1 has the larger context window at 2,000,000 tokens. o3 Reasoning has 200,000 tokens.
Are these models open weights?
GPT-5.1 is proprietary; o3 Reasoning is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-5.1 for quality-sensitive work and o3 Reasoning for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.