Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 (Reasoning) is clearly ahead on the aggregate, 82 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5 (Reasoning)'s sharpest advantage is in multilingual, where it averages 88 against 87. The single biggest benchmark swing on the page is MMLU, 92 to 82. GPT-4o mini does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 (Reasoning) is the reasoning model in the pair, while GPT-4o mini 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.
Pick Kimi K2.5 (Reasoning) if you want the stronger benchmark profile. GPT-4o mini only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.5 (Reasoning)
77.3
GPT-4o mini
82
Kimi K2.5 (Reasoning)
69
GPT-4o mini
87.2
Kimi K2.5 (Reasoning)
88
GPT-4o mini
87
Kimi K2.5 (Reasoning) is ahead overall, 82 to 43. The biggest single separator in this matchup is MMLU, where the scores are 92 and 82.
GPT-4o mini has the edge for knowledge tasks in this comparison, averaging 82 versus 77.3. 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 69. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Kimi K2.5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 88 versus 87. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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