Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 is clearly ahead on the aggregate, 68 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.05 input / $0.40 output per 1M tokens for GPT-5 nano. That is roughly 7.0x on output cost alone. GPT-5 nano is the reasoning model in the pair, while Kimi K2.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5 nano gives you the larger context window at 400K, compared with 128K for Kimi K2.5.
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-5 nano only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Kimi K2.5
64
GPT-5 nano
71.2
Kimi K2.5
76.8
GPT-5 nano
85.2
Kimi K2.5 is ahead overall, 68 to 31. The biggest single separator in this matchup is AIME 2025, where the scores are 78 and 85.2.
GPT-5 nano has the edge for knowledge tasks in this comparison, averaging 71.2 versus 64. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GPT-5 nano has the edge for math in this comparison, averaging 85.2 versus 76.8. Inside this category, AIME 2025 is the benchmark that creates the most daylight between them.
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