Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
42
Qwen3.6-35B-A3B
64
Verified leaderboard positions: Claude 3.5 Sonnet unranked · Qwen3.6-35B-A3B #13
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+17.9 difference
Knowledge
+1.1 difference
Claude 3.5 Sonnet
Qwen3.6-35B-A3B
$null / $null
N/A
N/A
N/A
N/A
N/A
200K
262K
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-35B-A3B is clearly ahead on the provisional aggregate, 64 to 42. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-35B-A3B's sharpest advantage is in coding, where it averages 66.9 against 49. The single biggest benchmark swing on the page is GPQA, 59.4% to 86%.
Qwen3.6-35B-A3B is the reasoning model in the pair, while Claude 3.5 Sonnet 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 200K for Claude 3.5 Sonnet.
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 64 to 42. The biggest single separator in this matchup is GPQA, where the scores are 59.4% and 86%.
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 60.5 versus 59.4. Inside this category, GPQA 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 49. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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