Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Qwen3.5 397B
66
Qwen3.6-27B
72
Verified leaderboard positions: Qwen3.5 397B #11 · Qwen3.6-27B #10
Pick Qwen3.6-27B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+3.1 difference
Coding
+10.3 difference
Knowledge
+3.0 difference
Multimodal
+3.2 difference
Qwen3.5 397B
Qwen3.6-27B
$0 / $0
$0 / $0
96 t/s
N/A
2.44s
N/A
128K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multimodal & grounded is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 72 to 66. 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 coding, where it averages 70.6 against 60.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 52.5% to 59.3%. Qwen3.5 397B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 128K for Qwen3.5 397B.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 72 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 52.5% and 59.3%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 62.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for coding in this comparison, averaging 70.6 versus 60.3. Inside this category, SWE-bench Pro 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 56.2. 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 75.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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