Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3.5 397B
66
Qwen3.6-35B-A3B
64
Verified leaderboard positions: Qwen3.5 397B #8 · Qwen3.6-35B-A3B #13
Pick Qwen3.5 397B if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if coding is the priority or you need the larger 262K context window.
Agentic
+4.7 difference
Coding
+6.6 difference
Knowledge
+4.7 difference
Multimodal
+3.7 difference
Qwen3.5 397B
Qwen3.6-35B-A3B
$0 / $0
N/A
96 t/s
N/A
2.44s
N/A
128K
262K
Pick Qwen3.5 397B if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if coding is the priority or you need the larger 262K context window.
Qwen3.5 397B has the cleaner provisional overall profile here, landing at 66 versus 64. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5 397B's sharpest advantage is in agentic, where it averages 56.2 against 51.5. The single biggest benchmark swing on the page is Claw-Eval, 48.1% to 68.7%. Qwen3.6-35B-A3B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-35B-A3B 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. Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 128K for Qwen3.5 397B.
Qwen3.5 397B is ahead on BenchLM's provisional leaderboard, 66 to 64. The biggest single separator in this matchup is Claw-Eval, where the scores are 48.1% and 68.7%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 60.5. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-35B-A3B has the edge for coding in this comparison, averaging 66.9 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 51.5. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79 versus 75.3. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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