o4 Pro vs Qwen3-Max Pro: which model should you choose?
We compared o4 Pro and Qwen3-Max Pro on price, context window and 12 benchmarks. o4 Pro has the stronger average benchmark score; o4 Pro is cheaper on a blended 3:1 basis.
o4 Pro
o4 Pro is a frontier model from OpenAI. It offers a 1,000,000-token context window, costs $19.37 per million input tokens and $70.19 per million output tokens, and averages 87.3 across the index's benchmark suite.
- Input
- $19.37 / 1M
- Output
- $70.19 / 1M
- Context
- 1,000,000
- Avg score
- 87.3
- 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 Pro | Qwen3-Max Pro | Edge |
|---|---|---|---|
| Input price / 1M | $19.37 | $18.87 | B |
| Output price / 1M | $70.19 | $84.39 | A |
| Context window | 1,000,000 | 400,000 | A |
| Max output | 16,384 | 8,192 | A |
| Average score | 87.3 | 86.2 | A |
| Arena Elo | 1471.2 | 1466.4 | A |
| Open weights | No | No | Tie |
Benchmark comparison
o4 Pro wins 7, Qwen3-Max Pro wins 4, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | o4 Pro | Qwen3-Max Pro | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 87.7 | 88.7 | 1.0 | Qwen3-Max Pro |
| GPQA Diamond | 82.7 | 80.0 | 2.7 | o4 Pro |
| SWE-bench Verified | 78.5 | 75.6 | 2.9 | o4 Pro |
| HumanEval | 92.1 | 92.1 | 0.0 | Tie |
| MATH-500 | 89.8 | 91.8 | 2.0 | Qwen3-Max Pro |
| AIME 2025 | 89.9 | 85.4 | 4.5 | o4 Pro |
| LiveCodeBench | 81.8 | 81.0 | 0.8 | o4 Pro |
| IFEval | 90.9 | 93.2 | 2.3 | Qwen3-Max Pro |
| TruthfulQA | 87.9 | 88.6 | 0.7 | Qwen3-Max Pro |
| HellaSwag | 94.4 | 92.4 | 2.0 | o4 Pro |
| MMMU | 84.1 | 79.5 | 4.6 | o4 Pro |
| Arena Elo | 1471.2 | 1466.4 | 4.8 | o4 Pro |
Price analysis
On a blended 3:1 input-to-output basis, o4 Pro costs about $32.08 per million tokens and Qwen3-Max Pro costs about $35.25. o4 Pro is roughly 9% 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 Pro has the larger context window at 1,000,000 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 Pro wins 7, Qwen3-Max Pro wins 4, and 1 are tied. On the index average, o4 Pro leads Qwen3-Max Pro by 1.1 points (87.3 versus 86.2).
The price picture favours o4 Pro. The context picture favours o4 Pro. 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 Pro for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose o4 Pro 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 1,000,000-token context window.
- You need the larger 16,384-token output limit.
Choose Qwen3-Max Pro if
- You prefer Alibaba Cloud's tooling or already use it.
FAQ
Is o4 Pro better than Qwen3-Max Pro?
On the index average, o4 Pro leads Qwen3-Max Pro by 1.1 points (87.3 versus 86.2). The better choice depends on price, context and your task. o4 Pro wins 7 benchmarks, Qwen3-Max Pro wins 4.
Which is cheaper, o4 Pro or Qwen3-Max Pro?
o4 Pro is cheaper on a blended 3:1 basis at about $32.08 per million tokens, versus $35.25 for Qwen3-Max Pro.
Which has the larger context window?
o4 Pro has the larger context window at 1,000,000 tokens. Qwen3-Max Pro has 400,000 tokens.
Are these models open weights?
o4 Pro is proprietary; Qwen3-Max Pro is proprietary.
Which should I choose for production?
If you need a single default, start with o4 Pro for quality-sensitive work, and check whether its price holds at your expected volume before committing.