Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Kimi K2.6
84
MAI-Thinking-1
65
Verified leaderboard positions: Kimi K2.6 #10 · MAI-Thinking-1 #23
Pick Kimi K2.6 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Agentic
+27.1 difference
Coding
+1.0 difference
Knowledge
+16.1 difference
Kimi K2.6
MAI-Thinking-1
$0.95 / $4
N/A
N/A
N/A
N/A
N/A
256K
256K
Pick Kimi K2.6 if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Kimi K2.6 is clearly ahead on the provisional aggregate, 84 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in agentic, where it averages 73.1 against 46. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 66.7% to 46%. MAI-Thinking-1 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 ahead on BenchLM's provisional leaderboard, 84 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 66.7% and 46%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 53.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Kimi K2.6 has the edge for coding in this comparison, averaging 72 versus 71. Inside this category, Terminal-Bench 2.0 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 46. Inside this category, Terminal-Bench 2.0 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.