o4 Turbo vs Qwen3-Max Pro: which model should you choose?
We compared o4 Turbo and Qwen3-Max Pro on price, context window and 12 benchmarks. o4 Turbo has the stronger average benchmark score; o4 Turbo is cheaper on a blended 3:1 basis.
o4 Turbo
o4 Turbo is a frontier model from OpenAI. It offers a 2,000,000-token context window, costs $19.34 per million input tokens and $82.19 per million output tokens, and averages 86.6 across the index's benchmark suite.
- Input
- $19.34 / 1M
- Output
- $82.19 / 1M
- Context
- 2,000,000
- Avg score
- 86.6
- Open weights
- No
Qwen3-Max Pro
Qwen3-Max Pro is a frontier model from Alibaba Cloud. It offers a 400,000-token context window, costs $18.87 per million input tokens and $84.39 per million output tokens, and averages 86.2 across the index's benchmark suite.
- Input
- $18.87 / 1M
- Output
- $84.39 / 1M
- Context
- 400,000
- Avg score
- 86.2
- Open weights
- No
Specification comparison
| Spec | o4 Turbo | Qwen3-Max Pro | Edge |
|---|---|---|---|
| Input price / 1M | $19.34 | $18.87 | B |
| Output price / 1M | $82.19 | $84.39 | A |
| Context window | 2,000,000 | 400,000 | A |
| Max output | 8,192 | 8,192 | Tie |
| Average score | 86.6 | 86.2 | A |
| Arena Elo | 1468.1 | 1466.4 | A |
| Open weights | No | No | Tie |
Benchmark comparison
o4 Turbo wins 6, Qwen3-Max Pro wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | o4 Turbo | Qwen3-Max Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 88.1 | 88.7 | 0.6 | Qwen3-Max Pro |
| GPQA Diamond | 84.8 | 80.0 | 4.8 | o4 Turbo |
| SWE-bench Verified | 78.4 | 75.6 | 2.8 | o4 Turbo |
| HumanEval | 91.0 | 92.1 | 1.1 | Qwen3-Max Pro |
| MATH-500 | 91.7 | 91.8 | 0.1 | Qwen3-Max Pro |
| AIME 2025 | 85.9 | 85.4 | 0.5 | o4 Turbo |
| LiveCodeBench | 80.1 | 81.0 | 0.9 | Qwen3-Max Pro |
| IFEval | 92.6 | 93.2 | 0.6 | Qwen3-Max Pro |
| TruthfulQA | 88.8 | 88.6 | 0.2 | o4 Turbo |
| HellaSwag | 91.3 | 92.4 | 1.1 | Qwen3-Max Pro |
| MMMU | 79.6 | 79.5 | 0.1 | o4 Turbo |
| Arena Elo | 1468.1 | 1466.4 | 1.7 | o4 Turbo |
Price analysis
On a blended 3:1 input-to-output basis, o4 Turbo costs about $35.05 per million tokens and Qwen3-Max Pro costs about $35.25. o4 Turbo is roughly 1% cheaper than Qwen3-Max Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
o4 Turbo has the larger context window at 2,000,000 tokens, compared with 400,000 for Qwen3-Max Pro. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, o4 Turbo wins 6, Qwen3-Max Pro wins 6, and 0 are tied. On the index average, o4 Turbo leads Qwen3-Max Pro by 0.4 points (86.6 versus 86.2).
The price picture favours o4 Turbo. The context picture favours o4 Turbo. 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 o4 Turbo for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose o4 Turbo 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.
Choose Qwen3-Max Pro if
- You prefer Alibaba Cloud's tooling or already use it.
FAQ
Is o4 Turbo better than Qwen3-Max Pro?
On the index average, o4 Turbo leads Qwen3-Max Pro by 0.4 points (86.6 versus 86.2). The better choice depends on price, context and your task. o4 Turbo wins 6 benchmarks, Qwen3-Max Pro wins 6.
Which is cheaper, o4 Turbo or Qwen3-Max Pro?
o4 Turbo is cheaper on a blended 3:1 basis at about $35.05 per million tokens, versus $35.25 for Qwen3-Max Pro.
Which has the larger context window?
o4 Turbo has the larger context window at 2,000,000 tokens. Qwen3-Max Pro has 400,000 tokens.
Are these models open weights?
o4 Turbo is proprietary; Qwen3-Max Pro is proprietary.
Which should I choose for production?
If you need a single default, start with o4 Turbo for quality-sensitive work, and check whether its price holds at your expected volume before committing.