Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
58
Kimi K2.6
84
Verified leaderboard positions: Claude Haiku 4.5 unranked · Kimi K2.6 #6
Pick Kimi K2.6 if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+1.3 difference
Claude Haiku 4.5
Kimi K2.6
$1 / $5
$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 Haiku 4.5 only becomes the better choice if coding 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 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude Haiku 4.5 is also the more expensive model on tokens at $1.00 input / $5.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. Kimi K2.6 is the reasoning model in the pair, while Claude Haiku 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 Haiku 4.5.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 58. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 80.2%.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 73.3 versus 72. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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