Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 is clearly ahead on the aggregate, 68 to 43. 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 $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. That is roughly 4.7x on output cost alone.
Pick Kimi K2.5 if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you want the cheaper token bill.
Kimi K2.5
64
GPT-4o mini
82
Kimi K2.5
49.3
GPT-4o mini
87.2
Kimi K2.5
83
GPT-4o mini
87
Kimi K2.5 is ahead overall, 68 to 43. The biggest single separator in this matchup is HumanEval, where the scores are 69 and 87.2.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 64. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for coding in this comparison, averaging 87.2 versus 49.3. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GPT-4o mini has the edge for multilingual tasks in this comparison, averaging 87 versus 83. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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