Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
60
Kimi K2.5
68
Verified leaderboard positions: DeepSeek V3.2 unranked · Kimi K2.5 #9
Pick Kimi K2.5 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill.
Coding
+3.3 difference
DeepSeek V3.2
Kimi K2.5
$0 / $0
$0.5 / $2.8
35 t/s
45 t/s
3.75s
2.38s
128K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill.
Kimi K2.5 is clearly ahead on the provisional aggregate, 68 to 60. 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 60.9. The single biggest benchmark swing on the page is SWE-Rebench, 60.9% to 58.5%.
Kimi K2.5 is also the more expensive model on tokens at $0.50 input / $2.80 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DeepSeek V3.2. That is roughly Infinityx on output cost alone. Kimi K2.5 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 68 to 60. The biggest single separator in this matchup is SWE-Rebench, where the scores are 60.9% and 58.5%.
Kimi K2.5 has the edge for coding in this comparison, averaging 64.2 versus 60.9. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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