Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Qwen2.5-VL-32B is clearly ahead on the aggregate, 41 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen2.5-VL-32B's sharpest advantage is in mathematics, where it averages 44.1 against 23.1. The single biggest benchmark swing on the page is MMLU, 43 to 87.5. 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 gives you the larger context window at 1M, compared with 32K for Qwen2.5-VL-32B.
Pick Qwen2.5-VL-32B 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.
Qwen2.5-VL-32B
36.3
GPT-4.1 mini
75.9
Qwen2.5-VL-32B
21
GPT-4.1 mini
23.6
Qwen2.5-VL-32B
44.1
GPT-4.1 mini
23.1
Qwen2.5-VL-32B
67
GPT-4.1 mini
88.5
Qwen2.5-VL-32B is ahead overall, 41 to 35. The biggest single separator in this matchup is MMLU, where the scores are 43 and 87.5.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 75.9 versus 36.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 mini has the edge for coding in this comparison, averaging 23.6 versus 21. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen2.5-VL-32B has the edge for math in this comparison, averaging 44.1 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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