Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro (High)
83
Kimi 2.6
86
Verified leaderboard positions: DeepSeek V4 Pro (High) #6 · Kimi 2.6 #5
Pick Kimi 2.6 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 2.6
$1.74 / $3.48
$0.95 / $4
N/A
N/A
N/A
N/A
1M
256K
Pick Kimi 2.6 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 2.6 has the cleaner provisional overall profile here, landing at 86 versus 83. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Kimi 2.6'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 2.6 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 2.6.
Kimi 2.6 is ahead on BenchLM's provisional leaderboard, 86 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, GPQA 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, SWE-bench Pro is the benchmark that creates the most daylight between them.
Kimi 2.6 has the edge for agentic tasks in this comparison, averaging 73.1 versus 70. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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