Gemini 3 Pro Turbo vs GPT-5.2: which model should you choose?
We compared Gemini 3 Pro Turbo and GPT-5.2 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.
Gemini 3 Pro Turbo
Gemini 3 Pro Turbo is a frontier model from Google DeepMind. It offers a 400,000-token context window, costs $19.62 per million input tokens and $89.91 per million output tokens, and averages 86.4 across the index's benchmark suite.
- Input
- $19.62 / 1M
- Output
- $89.91 / 1M
- Context
- 400,000
- Avg score
- 86.4
- Open weights
- No
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
Specification comparison
| Spec | Gemini 3 Pro Turbo | GPT-5.2 | Edge |
|---|---|---|---|
| Input price / 1M | $19.62 | $19.71 | A |
| Output price / 1M | $89.91 | $85.71 | B |
| Context window | 400,000 | 2,000,000 | B |
| Max output | 16,384 | 16,384 | Tie |
| Average score | 86.4 | 88.0 | B |
| Arena Elo | 1494.9 | 1478.0 | A |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 3 Pro Turbo wins 4, GPT-5.2 wins 8, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 3 Pro Turbo | GPT-5.2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 85.6 | 89.9 | 4.3 | GPT-5.2 |
| GPQA Diamond | 81.0 | 84.9 | 3.9 | GPT-5.2 |
| SWE-bench Verified | 77.4 | 80.2 | 2.8 | GPT-5.2 |
| HumanEval | 90.3 | 91.0 | 0.7 | GPT-5.2 |
| MATH-500 | 92.4 | 90.6 | 1.8 | Gemini 3 Pro Turbo |
| AIME 2025 | 82.8 | 90.6 | 7.8 | GPT-5.2 |
| LiveCodeBench | 82.7 | 79.7 | 3.0 | Gemini 3 Pro Turbo |
| IFEval | 93.4 | 93.6 | 0.2 | GPT-5.2 |
| TruthfulQA | 87.8 | 89.7 | 1.9 | GPT-5.2 |
| HellaSwag | 94.6 | 92.6 | 2.0 | Gemini 3 Pro Turbo |
| MMMU | 82.7 | 85.3 | 2.6 | GPT-5.2 |
| Arena Elo | 1494.9 | 1478.0 | 16.9 | Gemini 3 Pro Turbo |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 3 Pro Turbo costs about $37.19 per million tokens and GPT-5.2 costs about $36.21. GPT-5.2 is roughly 3% cheaper than Gemini 3 Pro 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 400,000 for Gemini 3 Pro Turbo. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 3 Pro Turbo wins 4, GPT-5.2 wins 8, and 0 are tied. On the index average, GPT-5.2 leads Gemini 3 Pro Turbo by 1.6 points (88.0 versus 86.4).
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 Gemini 3 Pro Turbo if
- You prefer Google DeepMind's tooling or already use it.
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.
FAQ
Is Gemini 3 Pro Turbo better than GPT-5.2?
On the index average, GPT-5.2 leads Gemini 3 Pro Turbo by 1.6 points (88.0 versus 86.4). The better choice depends on price, context and your task. Gemini 3 Pro Turbo wins 4 benchmarks, GPT-5.2 wins 8.
Which is cheaper, Gemini 3 Pro Turbo or GPT-5.2?
GPT-5.2 is cheaper on a blended 3:1 basis at about $36.21 per million tokens, versus $37.19 for Gemini 3 Pro Turbo.
Which has the larger context window?
GPT-5.2 has the larger context window at 2,000,000 tokens. Gemini 3 Pro Turbo has 400,000 tokens.
Are these models open weights?
Gemini 3 Pro Turbo is proprietary; GPT-5.2 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.