Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Mistral 8x7B v0.2 is clearly ahead on the aggregate, 33 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Mistral 8x7B v0.2's sharpest advantage is in mathematics, where it averages 31.9 against 9.8. The single biggest benchmark swing on the page is MMLU, 29 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.
GPT-4.1 nano gives you the larger context window at 1M, compared with 32K for Mistral 8x7B v0.2.
Pick Mistral 8x7B v0.2 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.
Mistral 8x7B v0.2
27
GPT-4.1 nano
65.2
Mistral 8x7B v0.2
31.9
GPT-4.1 nano
9.8
Mistral 8x7B v0.2
67
GPT-4.1 nano
83.2
Mistral 8x7B v0.2 is ahead overall, 33 to 23. The biggest single separator in this matchup is MMLU, where the scores are 29 and 80.1.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 65.2 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Mistral 8x7B v0.2 has the edge for math in this comparison, averaging 31.9 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 67. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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