Gemini 3 Pro Max vs GPT-5.2 Turbo: which model should you choose?
We compared Gemini 3 Pro Max and GPT-5.2 Turbo on price, context window and 12 benchmarks. Gemini 3 Pro Max has the stronger average benchmark score; GPT-5.2 Turbo is cheaper on a blended 3:1 basis.
Gemini 3 Pro Max
Gemini 3 Pro Max is a frontier model from Google DeepMind. It offers a 2,000,000-token context window, costs $20.57 per million input tokens and $112.92 per million output tokens, and averages 87.9 across the index's benchmark suite.
- Input
- $20.57 / 1M
- Output
- $112.92 / 1M
- Context
- 2,000,000
- Avg score
- 87.9
- 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 | Gemini 3 Pro Max | GPT-5.2 Turbo | Edge |
|---|---|---|---|
| Input price / 1M | $20.57 | $20.73 | A |
| Output price / 1M | $112.92 | $96.05 | B |
| Context window | 2,000,000 | 1,000,000 | A |
| Max output | 128,000 | 8,192 | A |
| Average score | 87.9 | 87.4 | A |
| Arena Elo | 1477.5 | 1500.3 | B |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 3 Pro Max wins 6, GPT-5.2 Turbo wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 3 Pro Max | GPT-5.2 Turbo | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 89.5 | 87.3 | 2.2 | Gemini 3 Pro Max |
| GPQA Diamond | 81.8 | 84.0 | 2.2 | GPT-5.2 Turbo |
| SWE-bench Verified | 76.6 | 77.4 | 0.8 | GPT-5.2 Turbo |
| HumanEval | 91.9 | 93.2 | 1.3 | GPT-5.2 Turbo |
| MATH-500 | 92.1 | 92.7 | 0.6 | GPT-5.2 Turbo |
| AIME 2025 | 89.6 | 86.7 | 2.9 | Gemini 3 Pro Max |
| LiveCodeBench | 83.6 | 82.5 | 1.1 | Gemini 3 Pro Max |
| IFEval | 94.7 | 92.0 | 2.7 | Gemini 3 Pro Max |
| TruthfulQA | 89.6 | 87.3 | 2.3 | Gemini 3 Pro Max |
| HellaSwag | 92.6 | 95.5 | 2.9 | GPT-5.2 Turbo |
| MMMU | 84.4 | 83.3 | 1.1 | Gemini 3 Pro Max |
| Arena Elo | 1477.5 | 1500.3 | 22.8 | GPT-5.2 Turbo |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 3 Pro Max costs about $43.66 per million tokens and GPT-5.2 Turbo costs about $39.56. GPT-5.2 Turbo is roughly 9% cheaper than Gemini 3 Pro Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Gemini 3 Pro Max 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, Gemini 3 Pro Max wins 6, GPT-5.2 Turbo wins 6, and 0 are tied. On the index average, Gemini 3 Pro Max leads GPT-5.2 Turbo by 0.5 points (87.9 versus 87.4).
The price picture favours GPT-5.2 Turbo. The context picture favours Gemini 3 Pro Max. 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 Gemini 3 Pro Max for quality-sensitive work and GPT-5.2 Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Gemini 3 Pro Max 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 GPT-5.2 Turbo if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is Gemini 3 Pro Max better than GPT-5.2 Turbo?
On the index average, Gemini 3 Pro Max leads GPT-5.2 Turbo by 0.5 points (87.9 versus 87.4). The better choice depends on price, context and your task. Gemini 3 Pro Max wins 6 benchmarks, GPT-5.2 Turbo wins 6.
Which is cheaper, Gemini 3 Pro Max or GPT-5.2 Turbo?
GPT-5.2 Turbo is cheaper on a blended 3:1 basis at about $39.56 per million tokens, versus $43.66 for Gemini 3 Pro Max.
Which has the larger context window?
Gemini 3 Pro Max has the larger context window at 2,000,000 tokens. GPT-5.2 Turbo has 1,000,000 tokens.
Are these models open weights?
Gemini 3 Pro Max is proprietary; GPT-5.2 Turbo is proprietary.
Which should I choose for production?
If you need a single default, start with Gemini 3 Pro Max for quality-sensitive work and GPT-5.2 Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.