Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
64
Kimi K2.5
64
Verified leaderboard positions: Gemini 2.5 Pro unranked · Kimi K2.5 #15
Treat this as a split decision. Gemini 2.5 Pro makes more sense if you need the larger 1M context window; Kimi K2.5 is the better fit if knowledge is the priority or you want the cheaper token bill.
Coding
+0.4 difference
Knowledge
+24.3 difference
Gemini 2.5 Pro
Kimi K2.5
$1.25 / $10
$0.6 / $3
117 t/s
45 t/s
21.19s
2.38s
1M
256K
Treat this as a split decision. Gemini 2.5 Pro makes more sense if you need the larger 1M context window; Kimi K2.5 is the better fit if knowledge is the priority or you want the cheaper token bill.
Gemini 2.5 Pro and Kimi K2.5 finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $10.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 3.3x on output cost alone. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Gemini 2.5 Pro and Kimi K2.5 are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 40.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 63.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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