Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MAI-Thinking-1
65
Qwen3.6-35B-A3B
66
Verified leaderboard positions: MAI-Thinking-1 #23 · Qwen3.6-35B-A3B #27
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Agentic
+5.5 difference
Coding
+4.1 difference
Knowledge
+9.4 difference
MAI-Thinking-1
Qwen3.6-35B-A3B
N/A
N/A
N/A
N/A
N/A
N/A
256K
262K
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. MAI-Thinking-1 only becomes the better choice if knowledge is the priority.
Qwen3.6-35B-A3B finishes one point ahead on BenchLM's provisional leaderboard, 66 to 65. 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.
Qwen3.6-35B-A3B's sharpest advantage is in agentic, where it averages 51.5 against 46. The single biggest benchmark swing on the page is LiveCodeBench, 87.7% to 80.4%. MAI-Thinking-1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 256K for MAI-Thinking-1.
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 66 to 65. The biggest single separator in this matchup is LiveCodeBench, where the scores are 87.7% and 80.4%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 60.5. 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 66.9. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.6-35B-A3B has the edge for agentic tasks in this comparison, averaging 51.5 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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