Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3.5 397B (Reasoning) is clearly ahead on the aggregate, 82 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5 397B (Reasoning)'s sharpest advantage is in mathematics, where it averages 92.1 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 95 to 9.8.
Qwen3.5 397B (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 Qwen3.5 397B (Reasoning).
Pick Qwen3.5 397B (Reasoning) if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5 397B (Reasoning)
77
GPT-4.1 nano
65.2
Qwen3.5 397B (Reasoning)
92.1
GPT-4.1 nano
9.8
Qwen3.5 397B (Reasoning)
89
GPT-4.1 nano
83.2
Qwen3.5 397B (Reasoning) is ahead overall, 82 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 95 and 9.8.
Qwen3.5 397B (Reasoning) has the edge for knowledge tasks in this comparison, averaging 77 versus 65.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5 397B (Reasoning) has the edge for math in this comparison, averaging 92.1 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Qwen3.5 397B (Reasoning) has the edge for instruction following in this comparison, averaging 89 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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