Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 is clearly ahead on the aggregate, 68 to 23. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5's sharpest advantage is in mathematics, where it averages 76.8 against 9.8. The single biggest benchmark swing on the page is AIME 2024, 79 to 9.8. 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.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 7.0x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 128K for Kimi K2.5.
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5
64
GPT-4.1 nano
65.2
Kimi K2.5
76.8
GPT-4.1 nano
9.8
Kimi K2.5
85
GPT-4.1 nano
83.2
Kimi K2.5 is ahead overall, 68 to 23. The biggest single separator in this matchup is AIME 2024, where the scores are 79 and 9.8.
GPT-4.1 nano has the edge for knowledge tasks in this comparison, averaging 65.2 versus 64. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for math in this comparison, averaging 76.8 versus 9.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 85 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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