Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 3.5 Flash
88
Kimi K2.5
64
Verified leaderboard positions: Gemini 3.5 Flash #7 · Kimi K2.5 #13
Pick Gemini 3.5 Flash if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you want the cheaper token bill.
Agentic
+22.6 difference
Coding
+9.7 difference
Reasoning
+13.7 difference
Knowledge
+7.1 difference
Multimodal
+5.3 difference
Inst. Following
+17.6 difference
Gemini 3.5 Flash
Kimi K2.5
$1.5 / $9
$0.6 / $3
284.2 t/s
45 t/s
18.55s
2.38s
1M
256K
Pick Gemini 3.5 Flash if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if instruction following is the priority or you want the cheaper token bill.
Gemini 3.5 Flash is clearly ahead on the provisional aggregate, 88 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Gemini 3.5 Flash's sharpest advantage is in agentic, where it averages 77.2 against 54.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 76.2% to 50.8%. Kimi K2.5 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
Gemini 3.5 Flash is also the more expensive model on tokens at $1.50 input / $9.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 3.0x on output cost alone. Gemini 3.5 Flash is the reasoning model in the pair, while Kimi K2.5 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. Gemini 3.5 Flash gives you the larger context window at 1M, compared with 256K for Kimi K2.5.
Gemini 3.5 Flash is ahead on BenchLM's provisional leaderboard, 88 to 64. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 76.2% and 50.8%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 58. 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 54.5. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Gemini 3.5 Flash has the edge for reasoning in this comparison, averaging 74.7 versus 61. Kimi K2.5 stays close enough that the answer can still flip depending on your workload.
Gemini 3.5 Flash has the edge for agentic tasks in this comparison, averaging 77.2 versus 54.6. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Gemini 3.5 Flash has the edge for multimodal and grounded tasks in this comparison, averaging 83.8 versus 78.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 76.3. Gemini 3.5 Flash stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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