Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
MiniMax M2.5 is clearly ahead on the aggregate, 66 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.5's sharpest advantage is in mathematics, where it averages 73.1 against 26.4. The single biggest benchmark swing on the page is AIME 2024, 75 to 26.4. GPT-4.1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.5. That is roughly 6.7x on output cost alone. GPT-4.1 gives you the larger context window at 1M, compared with 128K for MiniMax M2.5.
Pick MiniMax M2.5 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
MiniMax M2.5
61
GPT-4.1
78.3
MiniMax M2.5
48.3
GPT-4.1
54.6
MiniMax M2.5
73.1
GPT-4.1
26.4
MiniMax M2.5
85
GPT-4.1
87.4
MiniMax M2.5 is ahead overall, 66 to 43. The biggest single separator in this matchup is AIME 2024, where the scores are 75 and 26.4.
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 78.3 versus 61. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for coding in this comparison, averaging 54.6 versus 48.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
MiniMax M2.5 has the edge for math in this comparison, averaging 73.1 versus 26.4. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
GPT-4.1 has the edge for instruction following in this comparison, averaging 87.4 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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