Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MAI-Thinking-1
65
Qwen3.5 397B
63
Verified leaderboard positions: MAI-Thinking-1 #23 · Qwen3.5 397B #20
Pick MAI-Thinking-1 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+10.2 difference
Coding
+10.7 difference
Knowledge
+4.7 difference
Inst. Following
+7.6 difference
MAI-Thinking-1
Qwen3.5 397B
N/A
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
256K
128K
Pick MAI-Thinking-1 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
MAI-Thinking-1 has the cleaner provisional overall profile here, landing at 65 versus 63. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MAI-Thinking-1's sharpest advantage is in coding, where it averages 71 against 60.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 46% to 52.5%. Qwen3.5 397B does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
MAI-Thinking-1 is the reasoning model in the pair, while Qwen3.5 397B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. MAI-Thinking-1 gives you the larger context window at 256K, compared with 128K for Qwen3.5 397B.
MAI-Thinking-1 is ahead on BenchLM's provisional leaderboard, 65 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 46% and 52.5%.
MAI-Thinking-1 has the edge for knowledge tasks in this comparison, averaging 69.9 versus 65.2. 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 60.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.2 versus 46. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for instruction following in this comparison, averaging 92.6 versus 85. MAI-Thinking-1 stays close enough that the answer can still flip depending on your workload.
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