GPT-5.2 Max vs GPT-5.2 Pro: which model should you choose?
We compared GPT-5.2 Max and GPT-5.2 Pro on price, context window and 12 benchmarks. GPT-5.2 Pro has the stronger average benchmark score; GPT-5.2 Pro is cheaper on a blended 3:1 basis.
GPT-5.2 Max
GPT-5.2 Max is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $21.39 per million input tokens and $90.69 per million output tokens, and averages 89.2 across the index's benchmark suite.
- Input
- $21.39 / 1M
- Output
- $90.69 / 1M
- Context
- 1,000,000
- Avg score
- 89.2
- Open weights
- No
GPT-5.2 Pro
GPT-5.2 Pro is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $21.32 per million input tokens and $76.33 per million output tokens, and averages 89.6 across the index's benchmark suite.
- Input
- $21.32 / 1M
- Output
- $76.33 / 1M
- Context
- 2,000,000
- Avg score
- 89.6
- Open weights
- No
Specification comparison
| Spec | GPT-5.2 Max | GPT-5.2 Pro | Edge |
|---|---|---|---|
| Input price / 1M | $21.39 | $21.32 | B |
| Output price / 1M | $90.69 | $76.33 | B |
| Context window | 1,000,000 | 2,000,000 | B |
| Max output | 128,000 | 32,768 | A |
| Average score | 89.2 | 89.6 | B |
| Arena Elo | 1495.3 | 1485.0 | A |
| Open weights | No | No | Tie |
Benchmark comparison
GPT-5.2 Max wins 6, GPT-5.2 Pro wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-5.2 Max | GPT-5.2 Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 87.6 | 91.6 | 4.0 | GPT-5.2 Pro |
| GPQA Diamond | 83.4 | 83.2 | 0.2 | GPT-5.2 Max |
| SWE-bench Verified | 80.5 | 81.7 | 1.2 | GPT-5.2 Pro |
| HumanEval | 93.6 | 96.8 | 3.2 | GPT-5.2 Pro |
| MATH-500 | 96.7 | 91.8 | 4.9 | GPT-5.2 Max |
| AIME 2025 | 88.2 | 91.0 | 2.8 | GPT-5.2 Pro |
| LiveCodeBench | 84.8 | 84.6 | 0.2 | GPT-5.2 Max |
| IFEval | 95.2 | 95.6 | 0.4 | GPT-5.2 Pro |
| TruthfulQA | 88.1 | 90.0 | 1.9 | GPT-5.2 Pro |
| HellaSwag | 96.1 | 95.3 | 0.8 | GPT-5.2 Max |
| MMMU | 86.9 | 84.4 | 2.5 | GPT-5.2 Max |
| Arena Elo | 1495.3 | 1485.0 | 10.3 | GPT-5.2 Max |
Price analysis
On a blended 3:1 input-to-output basis, GPT-5.2 Max costs about $38.72 per million tokens and GPT-5.2 Pro costs about $35.07. GPT-5.2 Pro is roughly 9% cheaper than GPT-5.2 Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-5.2 Pro has the larger context window at 2,000,000 tokens, compared with 1,000,000 for GPT-5.2 Max. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-5.2 Max wins 6, GPT-5.2 Pro wins 6, and 0 are tied. On the index average, GPT-5.2 Pro leads GPT-5.2 Max by 0.4 points (89.6 versus 89.2).
The price picture favours GPT-5.2 Pro. The context picture favours GPT-5.2 Pro. 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-5.2 Pro for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose GPT-5.2 Max if
- You need the larger 128,000-token output limit.
Choose GPT-5.2 Pro if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need the larger 2,000,000-token context window.
FAQ
Is GPT-5.2 Max better than GPT-5.2 Pro?
On the index average, GPT-5.2 Pro leads GPT-5.2 Max by 0.4 points (89.6 versus 89.2). The better choice depends on price, context and your task. GPT-5.2 Max wins 6 benchmarks, GPT-5.2 Pro wins 6.
Which is cheaper, GPT-5.2 Max or GPT-5.2 Pro?
GPT-5.2 Pro is cheaper on a blended 3:1 basis at about $35.07 per million tokens, versus $38.72 for GPT-5.2 Max.
Which has the larger context window?
GPT-5.2 Pro has the larger context window at 2,000,000 tokens. GPT-5.2 Max has 1,000,000 tokens.
Are these models open weights?
GPT-5.2 Max is proprietary; GPT-5.2 Pro is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-5.2 Pro for quality-sensitive work, and check whether its price holds at your expected volume before committing.