Gemini 2.5 Pro Pro vs Qwen3-Max: which model should you choose?
We compared Gemini 2.5 Pro Pro and Qwen3-Max on price, context window and 12 benchmarks. Qwen3-Max has the stronger average benchmark score; Gemini 2.5 Pro Pro is cheaper on a blended 3:1 basis.
Gemini 2.5 Pro Pro
Gemini 2.5 Pro Pro is a frontier model from Google DeepMind. It offers a 400,000-token context window, costs $17.16 per million input tokens and $90.89 per million output tokens, and averages 83.6 across the index's benchmark suite.
- Input
- $17.16 / 1M
- Output
- $90.89 / 1M
- Context
- 400,000
- Avg score
- 83.6
- Open weights
- No
Qwen3-Max
Qwen3-Max is a frontier model from Alibaba Cloud. It offers a 400,000-token context window, costs $17.37 per million input tokens and $94.30 per million output tokens, and averages 84.4 across the index's benchmark suite.
- Input
- $17.37 / 1M
- Output
- $94.30 / 1M
- Context
- 400,000
- Avg score
- 84.4
- Open weights
- No
Specification comparison
| Spec | Gemini 2.5 Pro Pro | Qwen3-Max | Edge |
|---|---|---|---|
| Input price / 1M | $17.16 | $17.37 | A |
| Output price / 1M | $90.89 | $94.30 | A |
| Context window | 400,000 | 400,000 | Tie |
| Max output | 8,192 | 4,096 | A |
| Average score | 83.6 | 84.4 | B |
| Arena Elo | 1460.2 | 1449.2 | A |
| Open weights | No | No | Tie |
Benchmark comparison
Gemini 2.5 Pro Pro wins 4, Qwen3-Max wins 8, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Gemini 2.5 Pro Pro | Qwen3-Max | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 85.2 | 84.0 | 1.2 | Gemini 2.5 Pro Pro |
| GPQA Diamond | 81.6 | 82.3 | 0.7 | Qwen3-Max |
| SWE-bench Verified | 72.1 | 73.3 | 1.2 | Qwen3-Max |
| HumanEval | 88.4 | 88.5 | 0.1 | Qwen3-Max |
| MATH-500 | 86.8 | 89.7 | 2.9 | Qwen3-Max |
| AIME 2025 | 83.3 | 81.1 | 2.2 | Gemini 2.5 Pro Pro |
| LiveCodeBench | 76.5 | 82.2 | 5.7 | Qwen3-Max |
| IFEval | 92.0 | 92.5 | 0.5 | Qwen3-Max |
| TruthfulQA | 85.5 | 83.4 | 2.1 | Gemini 2.5 Pro Pro |
| HellaSwag | 90.6 | 92.9 | 2.3 | Qwen3-Max |
| MMMU | 78.1 | 78.7 | 0.6 | Qwen3-Max |
| Arena Elo | 1460.2 | 1449.2 | 11.0 | Gemini 2.5 Pro Pro |
Price analysis
On a blended 3:1 input-to-output basis, Gemini 2.5 Pro Pro costs about $35.59 per million tokens and Qwen3-Max costs about $36.60. Gemini 2.5 Pro Pro is roughly 3% cheaper than Qwen3-Max. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 400,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Gemini 2.5 Pro Pro wins 4, Qwen3-Max wins 8, and 0 are tied. On the index average, Qwen3-Max leads Gemini 2.5 Pro Pro by 0.8 points (84.4 versus 83.6).
The price picture favours Gemini 2.5 Pro Pro. The context picture is effectively a tie. 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 Qwen3-Max for quality-sensitive work and Gemini 2.5 Pro Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Gemini 2.5 Pro Pro if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 8,192-token output limit.
Choose Qwen3-Max if
- You want the higher average benchmark score in this index.
FAQ
Is Gemini 2.5 Pro Pro better than Qwen3-Max?
On the index average, Qwen3-Max leads Gemini 2.5 Pro Pro by 0.8 points (84.4 versus 83.6). The better choice depends on price, context and your task. Gemini 2.5 Pro Pro wins 4 benchmarks, Qwen3-Max wins 8.
Which is cheaper, Gemini 2.5 Pro Pro or Qwen3-Max?
Gemini 2.5 Pro Pro is cheaper on a blended 3:1 basis at about $35.59 per million tokens, versus $36.60 for Qwen3-Max.
Which has the larger context window?
Both models list a 400,000-token context window.
Are these models open weights?
Gemini 2.5 Pro Pro is proprietary; Qwen3-Max is proprietary.
Which should I choose for production?
If you need a single default, start with Qwen3-Max for quality-sensitive work and Gemini 2.5 Pro Pro for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.