Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 4 Sonnet
51
Qwen3.5-35B-A3B
56
Verified leaderboard positions: Claude 4 Sonnet unranked · Qwen3.5-35B-A3B #18
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+14.3 difference
Claude 4 Sonnet
Qwen3.5-35B-A3B
$3 / $15
$0 / $0
40 t/s
N/A
1.33s
N/A
200K
262K
Pick Qwen3.5-35B-A3B if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-35B-A3B is clearly ahead on the provisional aggregate, 56 to 51. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude 4 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-35B-A3B. That is roughly Infinityx on output cost alone. Qwen3.5-35B-A3B is the reasoning model in the pair, while Claude 4 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. Qwen3.5-35B-A3B gives you the larger context window at 262K, compared with 200K for Claude 4 Sonnet.
Qwen3.5-35B-A3B is ahead on BenchLM's provisional leaderboard, 56 to 51. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.7% and 69.2%.
Claude 4 Sonnet has the edge for coding in this comparison, averaging 72.7 versus 58.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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