Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
Kimi K2.5
64
Verified leaderboard positions: DeepSeek V3 unranked · Kimi K2.5 #11
Pick Kimi K2.5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+25.0 difference
Knowledge
+4.9 difference
Inst. Following
+7.8 difference
DeepSeek V3
Kimi K2.5
$0.27 / $1.1
$0.6 / $3
N/A
45 t/s
N/A
2.38s
128K
256K
Pick Kimi K2.5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.5 is clearly ahead on the provisional aggregate, 64 to 36. 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 39.2. The single biggest benchmark swing on the page is LiveCodeBench, 37.6% to 85%. DeepSeek V3 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 2.7x on output cost alone. Kimi K2.5 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.
Kimi K2.5 is ahead on BenchLM's provisional leaderboard, 64 to 36. The biggest single separator in this matchup is LiveCodeBench, where the scores are 37.6% and 85%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 65.1. 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 39.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Kimi K2.5 has the edge for instruction following in this comparison, averaging 93.9 versus 86.1. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.