Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.5
66
Kimi K2.6
84
Verified leaderboard positions: Claude Sonnet 4.5 unranked · Kimi K2.6 #6
Pick Kimi K2.6 if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+17.8 difference
Coding
+5.2 difference
Knowledge
+29.6 difference
Claude Sonnet 4.5
Kimi K2.6
$3 / $15
$0.95 / $4
N/A
N/A
N/A
N/A
200K
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. Claude Sonnet 4.5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Kimi K2.6 is clearly ahead on the provisional aggregate, 84 to 66. 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 55.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 50% to 66.7%. Claude Sonnet 4.5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.5 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. That is roughly 3.8x on output cost alone. Kimi K2.6 is the reasoning model in the pair, while Claude Sonnet 4.5 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 gives you the larger context window at 256K, compared with 200K for Claude Sonnet 4.5.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 50% and 66.7%.
Claude Sonnet 4.5 has the edge for knowledge tasks in this comparison, averaging 83.4 versus 53.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Claude Sonnet 4.5 has the edge for coding in this comparison, averaging 77.2 versus 72. Inside this category, SWE-bench Verified 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 55.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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