Head-to-head · frontier vs frontier

GPT-4.1 Pro vs Qwen3-Max Turbo: which model should you choose?

We compared GPT-4.1 Pro and Qwen3-Max Turbo on price, context window and 12 benchmarks. GPT-4.1 Pro has the stronger average benchmark score; Qwen3-Max Turbo is cheaper on a blended 3:1 basis.

OpenAI

GPT-4.1 Pro

GPT-4.1 Pro is a frontier model from OpenAI. It offers a 400,000-token context window, costs $17.56 per million input tokens and $91.14 per million output tokens, and averages 85.2 across the index's benchmark suite.

Input
$17.56 / 1M
Output
$91.14 / 1M
Context
400,000
Avg score
85.2
Open weights
No
Full GPT-4.1 Pro page
Alibaba Cloud

Qwen3-Max Turbo

Qwen3-Max Turbo is a frontier model from Alibaba Cloud. It offers a 1,000,000-token context window, costs $17.70 per million input tokens and $63.95 per million output tokens, and averages 84.7 across the index's benchmark suite.

Input
$17.70 / 1M
Output
$63.95 / 1M
Context
1,000,000
Avg score
84.7
Open weights
No
Full Qwen3-Max Turbo page

Specification comparison

SpecGPT-4.1 ProQwen3-Max TurboEdge
Input price / 1M$17.56$17.70A
Output price / 1M$91.14$63.95B
Context window400,0001,000,000B
Max output8,192128,000B
Average score85.284.7A
Arena Elo1449.61456.9B
Open weightsNoNoTie

Benchmark comparison

GPT-4.1 Pro wins 7, Qwen3-Max Turbo wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.

BenchmarkGPT-4.1 ProQwen3-Max TurboGapWinner
MMLU-Pro86.986.80.1GPT-4.1 Pro
GPQA Diamond78.278.30.1Qwen3-Max Turbo
SWE-bench Verified77.375.91.4GPT-4.1 Pro
HumanEval91.992.50.6Qwen3-Max Turbo
MATH-50088.891.93.1Qwen3-Max Turbo
AIME 202582.881.21.6GPT-4.1 Pro
LiveCodeBench76.776.80.1Qwen3-Max Turbo
IFEval92.292.10.1GPT-4.1 Pro
TruthfulQA87.786.31.4GPT-4.1 Pro
HellaSwag93.790.82.9GPT-4.1 Pro
MMMU81.578.82.7GPT-4.1 Pro
Arena Elo1449.61456.97.3Qwen3-Max Turbo

Price analysis

On a blended 3:1 input-to-output basis, GPT-4.1 Pro costs about $35.95 per million tokens and Qwen3-Max Turbo costs about $29.26. Qwen3-Max Turbo is roughly 19% cheaper than GPT-4.1 Pro. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.

Qwen3-Max Turbo has the larger context window at 1,000,000 tokens, compared with 400,000 for GPT-4.1 Pro. That matters for long documents, large codebases and multi-step agent traces.

Verdict

Across 12 shared benchmarks, GPT-4.1 Pro wins 7, Qwen3-Max Turbo wins 5, and 0 are tied. On the index average, GPT-4.1 Pro leads Qwen3-Max Turbo by 0.5 points (85.2 versus 84.7).

The price picture favours Qwen3-Max Turbo. The context picture favours Qwen3-Max 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 GPT-4.1 Pro for quality-sensitive work and Qwen3-Max Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

Choose GPT-4.1 Pro if

  • You want the higher average benchmark score in this index.

Choose Qwen3-Max Turbo if

  • Unit cost matters and you want the cheaper blended price.
  • You need the larger 1,000,000-token context window.
  • You need the larger 128,000-token output limit.

FAQ

Is GPT-4.1 Pro better than Qwen3-Max Turbo?

On the index average, GPT-4.1 Pro leads Qwen3-Max Turbo by 0.5 points (85.2 versus 84.7). The better choice depends on price, context and your task. GPT-4.1 Pro wins 7 benchmarks, Qwen3-Max Turbo wins 5.

Which is cheaper, GPT-4.1 Pro or Qwen3-Max Turbo?

Qwen3-Max Turbo is cheaper on a blended 3:1 basis at about $29.26 per million tokens, versus $35.95 for GPT-4.1 Pro.

Which has the larger context window?

Qwen3-Max Turbo has the larger context window at 1,000,000 tokens. GPT-4.1 Pro has 400,000 tokens.

Are these models open weights?

GPT-4.1 Pro is proprietary; Qwen3-Max Turbo is proprietary.

Which should I choose for production?

If you need a single default, start with GPT-4.1 Pro for quality-sensitive work and Qwen3-Max Turbo for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.

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