Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
66
Kimi K2.5
68
Verified leaderboard positions: Gemini 2.5 Pro unranked · Kimi K2.5 #9
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if you need the larger 1M context window.
Coding
+0.4 difference
Knowledge
+24.3 difference
Gemini 2.5 Pro
Kimi K2.5
$1.25 / $5
$0.5 / $2.8
117 t/s
45 t/s
21.19s
2.38s
1M
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if you need the larger 1M context window.
Kimi K2.5 has the cleaner provisional overall profile here, landing at 68 versus 66. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Kimi K2.5's sharpest advantage is in knowledge, where it averages 65.1 against 40.8. The single biggest benchmark swing on the page is SWE-bench Verified, 63.8% to 76.8%.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.50 input / $2.80 output per 1M tokens for Kimi K2.5. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 68 to 66. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 63.8% and 76.8%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 40.8. Inside this category, HLE 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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