Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.5
64
Kimi K2.6
84
Verified leaderboard positions: Kimi K2.5 #11 · Kimi K2.6 #6
Pick Kimi K2.6 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+18.5 difference
Coding
+7.8 difference
Knowledge
+11.3 difference
Multimodal
+1.2 difference
Kimi K2.5
Kimi K2.6
$0.6 / $3
$0.95 / $4
45 t/s
N/A
2.38s
N/A
256K
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.6 is clearly ahead on the provisional aggregate, 84 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in agentic, where it averages 73.1 against 54.6. The single biggest benchmark swing on the page is BrowseComp, 60.6% to 83.2%. Kimi K2.5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. Kimi K2.6 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.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 64. The biggest single separator in this matchup is BrowseComp, where the scores are 60.6% and 83.2%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 53.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for coding in this comparison, averaging 72 versus 64.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 54.6. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for multimodal and grounded tasks in this comparison, averaging 79.7 versus 78.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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