Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MAI-Thinking-1
65
Qwen3.6-27B
73
Verified leaderboard positions: MAI-Thinking-1 #23 · Qwen3.6-27B #19
Pick Qwen3.6-27B if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Agentic
+13.3 difference
Coding
+0.4 difference
Knowledge
+7.7 difference
MAI-Thinking-1
Qwen3.6-27B
N/A
$0 / $0
N/A
N/A
N/A
N/A
256K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 73 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in agentic, where it averages 59.3 against 46. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 46% to 59.3%. MAI-Thinking-1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-27B gives you the larger context window at 262K, compared with 256K for MAI-Thinking-1.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 73 to 65. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 46% and 59.3%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 62.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
MAI-Thinking-1 has the edge for coding in this comparison, averaging 71 versus 70.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 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.
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