Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3.5 397B is clearly ahead on the aggregate, 71 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B's sharpest advantage is in mathematics, where it averages 81.9 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 85 to 9.8. GPT-4.1 nano does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5 397B. That is roughly Infinityx on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Qwen3.5 397B.
Pick Qwen3.5 397B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.
Qwen3.5 397B
67.7
GPT-4.1 nano
65.2
Qwen3.5 397B
81.9
GPT-4.1 nano
9.8
Qwen3.5 397B
82
GPT-4.1 nano
83.2
Qwen3.5 397B is ahead overall, 71 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 85 and 9.8.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 67.7 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for math in this comparison, averaging 81.9 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 82. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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