Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
Kimi K2.5 is clearly ahead on the aggregate, 68 to 39. 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 multilingual, where it averages 83 against 80.6. The single biggest benchmark swing on the page is GPQA, 76 to 56.1. Phi-4 does hit back in coding, 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 $0.00 input / $0.00 output per 1M tokens for Phi-4. That is roughly Infinityx on output cost alone. Kimi K2.5 gives you the larger context window at 128K, compared with 16K for Phi-4.
Pick Kimi K2.5 if you want the stronger benchmark profile. Phi-4 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Kimi K2.5
64
Phi-4
70.5
Kimi K2.5
49.3
Phi-4
82.6
Kimi K2.5
83
Phi-4
80.6
Kimi K2.5 is ahead overall, 68 to 39. The biggest single separator in this matchup is GPQA, where the scores are 76 and 56.1.
Phi-4 has the edge for knowledge tasks in this comparison, averaging 70.5 versus 64. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 82.6 versus 49.3. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for multilingual tasks in this comparison, averaging 83 versus 80.6. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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