Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 is clearly ahead on the aggregate, 68 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.50 input / $2.80 output per 1M tokens for Kimi K2.5. o3-mini 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. o3-mini gives you the larger context window at 200K, compared with 128K for Kimi K2.5.
Pick Kimi K2.5 if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority or you need the larger 200K context window.
Kimi K2.5
64
o3-mini
82.1
Kimi K2.5
49.3
o3-mini
49.3
Kimi K2.5
76.8
o3-mini
87.3
Kimi K2.5
85
o3-mini
93.9
Kimi K2.5 is ahead overall, 68 to 56. The biggest single separator in this matchup is MMLU, where the scores are 77 and 86.9.
o3-mini has the edge for knowledge tasks in this comparison, averaging 82.1 versus 64. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Kimi K2.5 and o3-mini are effectively tied for coding here, both landing at 49.3 on average.
o3-mini has the edge for math in this comparison, averaging 87.3 versus 76.8. Inside this category, AIME 2024 is the benchmark that creates the most daylight between them.
o3-mini has the edge for instruction following in this comparison, averaging 93.9 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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