Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3 235B 2507 (Reasoning) is clearly ahead on the aggregate, 40 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3 235B 2507 (Reasoning)'s sharpest advantage is in mathematics, where it averages 41.6 against 9.8. The single biggest benchmark swing on the page is MMLU, 40 to 80.1. GPT-4.1 nano does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3 235B 2507 (Reasoning) is the reasoning model in the pair, while GPT-4.1 nano is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Qwen3 235B 2507 (Reasoning).
Pick Qwen3 235B 2507 (Reasoning) if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Qwen3 235B 2507 (Reasoning)
34.7
GPT-4.1 nano
65.2
Qwen3 235B 2507 (Reasoning)
41.6
GPT-4.1 nano
9.8
Qwen3 235B 2507 (Reasoning)
68
GPT-4.1 nano
83.2
Qwen3 235B 2507 (Reasoning) is ahead overall, 40 to 23. The biggest single separator in this matchup is MMLU, where the scores are 40 and 80.1.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 65.2 versus 34.7. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Qwen3 235B 2507 (Reasoning) has the edge for math in this comparison, averaging 41.6 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 nano has the edge for instruction following in this comparison, averaging 83.2 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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