GPT-5.2 vs GPT-5.2 Turbo: which model should you choose?
We compared GPT-5.2 and GPT-5.2 Turbo on price, context window and 12 benchmarks. GPT-5.2 has the stronger average benchmark score; GPT-5.2 is cheaper on a blended 3:1 basis.
GPT-5.2
GPT-5.2 is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $19.71 per million input tokens and $85.71 per million output tokens, and averages 88.0 across the index's benchmark suite.
- Input
- $19.71 / 1M
- Output
- $85.71 / 1M
- Context
- 2,000,000
- Avg score
- 88.0
- Open weights
- No
GPT-5.2 Turbo
GPT-5.2 Turbo is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $20.73 per million input tokens and $96.05 per million output tokens, and averages 87.4 across the index's benchmark suite.
- Input
- $20.73 / 1M
- Output
- $96.05 / 1M
- Context
- 1,000,000
- Avg score
- 87.4
- Open weights
- No
Specification comparison
| Spec | GPT-5.2 | GPT-5.2 Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $19.71 | $20.73 | A |
| Output price / 1M | $85.71 | $96.05 | A |
| Context window | 2,000,000 | 1,000,000 | A |
| Max output | 16,384 | 8,192 | A |
| Average score | 88.0 | 87.4 | A |
| Arena Elo | 1478.0 | 1500.3 | B |
| Open weights | No | No | Tie |
Benchmark comparison
GPT-5.2 wins 7, GPT-5.2 Turbo wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GPT-5.2 | GPT-5.2 Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 89.9 | 87.3 | 2.6 | GPT-5.2 |
| GPQA Diamond | 84.9 | 84.0 | 0.9 | GPT-5.2 |
| SWE-bench Verified | 80.2 | 77.4 | 2.8 | GPT-5.2 |
| HumanEval | 91.0 | 93.2 | 2.2 | GPT-5.2 Turbo |
| MATH-500 | 90.6 | 92.7 | 2.1 | GPT-5.2 Turbo |
| AIME 2025 | 90.6 | 86.7 | 3.9 | GPT-5.2 |
| LiveCodeBench | 79.7 | 82.5 | 2.8 | GPT-5.2 Turbo |
| IFEval | 93.6 | 92.0 | 1.6 | GPT-5.2 |
| TruthfulQA | 89.7 | 87.3 | 2.4 | GPT-5.2 |
| HellaSwag | 92.6 | 95.5 | 2.9 | GPT-5.2 Turbo |
| MMMU | 85.3 | 83.3 | 2.0 | GPT-5.2 |
| Arena Elo | 1478.0 | 1500.3 | 22.3 | GPT-5.2 Turbo |
Price analysis
On a blended 3:1 input-to-output basis, GPT-5.2 costs about $36.21 per million tokens and GPT-5.2 Turbo costs about $39.56. GPT-5.2 is roughly 8% cheaper than GPT-5.2 Turbo. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
GPT-5.2 has the larger context window at 2,000,000 tokens, compared with 1,000,000 for GPT-5.2 Turbo. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GPT-5.2 wins 7, GPT-5.2 Turbo wins 5, and 0 are tied. On the index average, GPT-5.2 leads GPT-5.2 Turbo by 0.6 points (88.0 versus 87.4).
The price picture favours GPT-5.2. The context picture favours GPT-5.2. 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 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose GPT-5.2 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.
- You need the larger 16,384-token output limit.
Choose GPT-5.2 Turbo if
- You prefer OpenAI's tooling or already use it.
FAQ
Is GPT-5.2 better than GPT-5.2 Turbo?
On the index average, GPT-5.2 leads GPT-5.2 Turbo by 0.6 points (88.0 versus 87.4). The better choice depends on price, context and your task. GPT-5.2 wins 7 benchmarks, GPT-5.2 Turbo wins 5.
Which is cheaper, GPT-5.2 or GPT-5.2 Turbo?
GPT-5.2 is cheaper on a blended 3:1 basis at about $36.21 per million tokens, versus $39.56 for GPT-5.2 Turbo.
Which has the larger context window?
GPT-5.2 has the larger context window at 2,000,000 tokens. GPT-5.2 Turbo has 1,000,000 tokens.
Are these models open weights?
GPT-5.2 is proprietary; GPT-5.2 Turbo is proprietary.
Which should I choose for production?
If you need a single default, start with GPT-5.2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.