Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
40
Kimi K2.5
64
Verified leaderboard positions: Claude 3.5 Sonnet unranked · Kimi K2.5 #15
Pick Kimi K2.5 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Coding
+15.2 difference
Knowledge
+5.7 difference
Claude 3.5 Sonnet
Kimi K2.5
$3 / $15
$0.6 / $3
N/A
45 t/s
N/A
2.38s
200K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 40. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.5's sharpest advantage is in coding, where it averages 64.2 against 49. The single biggest benchmark swing on the page is GPQA, 59.4% to 87.6%.
Claude 3.5 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.60 input / $3.00 output per 1M tokens for Kimi K2.5. That is roughly 5.0x on output cost alone. Kimi K2.5 gives you the larger context window at 256K, compared with 200K for Claude 3.5 Sonnet.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 40. The biggest single separator in this matchup is GPQA, where the scores are 59.4% and 87.6%.
Kimi K2.5 has the edge for knowledge tasks in this comparison, averaging 65.1 versus 59.4. Inside this category, GPQA 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 49. 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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