Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash (Max)
77
Kimi 2.6
86
Verified leaderboard positions: DeepSeek V4 Flash (Max) #12 · Kimi 2.6 #5
Pick Kimi 2.6 if you want the stronger benchmark profile. DeepSeek V4 Flash (Max) only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+9.8 difference
Coding
+1.7 difference
Knowledge
+6.2 difference
DeepSeek V4 Flash (Max)
Kimi 2.6
$0.14 / $0.28
$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 Flash (Max) only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Kimi 2.6 is clearly ahead on the provisional aggregate, 86 to 77. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi 2.6's sharpest advantage is in agentic, where it averages 73.1 against 63.3. The single biggest benchmark swing on the page is BrowseComp, 73.2% to 83.2%. DeepSeek V4 Flash (Max) 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 $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (Max). That is roughly 14.3x on output cost alone. DeepSeek V4 Flash (Max) 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 77. The biggest single separator in this matchup is BrowseComp, where the scores are 73.2% and 83.2%.
DeepSeek V4 Flash (Max) has the edge for knowledge tasks in this comparison, averaging 60 versus 53.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V4 Flash (Max) has the edge for coding in this comparison, averaging 73.7 versus 72. Inside this category, Terminal-Bench 2.0 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 63.3. 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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