Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen3.5 397B is clearly ahead on the aggregate, 71 to 35. 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 23.1. The single biggest benchmark swing on the page is AIME 2024, 85 to 23.1. GPT-4.1 mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 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 mini 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 mini only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Qwen3.5 397B
67.7
GPT-4.1 mini
75.9
Qwen3.5 397B
52
GPT-4.1 mini
23.6
Qwen3.5 397B
81.9
GPT-4.1 mini
23.1
Qwen3.5 397B
82
GPT-4.1 mini
88.5
Qwen3.5 397B is ahead overall, 71 to 35. The biggest single separator in this matchup is AIME 2024, where the scores are 85 and 23.1.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 75.9 versus 67.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for coding in this comparison, averaging 52 versus 23.6. Inside this category, SWE-bench Verified 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 23.1. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 82. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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