Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro (High)
83
Kimi K2.6
84
Verified leaderboard positions: DeepSeek V4 Pro (High) #10 · Kimi K2.6 #9
Pick Kimi K2.6 if you want the stronger benchmark profile. DeepSeek V4 Pro (High) only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+3.1 difference
Coding
+1.8 difference
Knowledge
+8.8 difference
DeepSeek V4 Pro (High)
Kimi K2.6
$1.74 / $3.48
$0.95 / $4
N/A
N/A
N/A
N/A
1M
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. DeepSeek V4 Pro (High) only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi K2.6 finishes one point ahead on BenchLM's provisional leaderboard, 84 to 83. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Kimi K2.6's sharpest advantage is in agentic, where it averages 73.1 against 70. The single biggest benchmark swing on the page is SWE-bench Pro, 54.4% to 58.6%. DeepSeek V4 Pro (High) does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $1.74 input / $3.48 output per 1M tokens for DeepSeek V4 Pro (High). DeepSeek V4 Pro (High) gives you the larger context window at 1M, compared with 256K for Kimi K2.6.
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 84 to 83. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 54.4% and 58.6%.
DeepSeek V4 Pro (High) has the edge for knowledge tasks in this comparison, averaging 62.6 versus 53.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (High) has the edge for coding in this comparison, averaging 73.8 versus 72. Inside this category, AA-SciCode 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 70. Inside this category, GDPval-AA 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.