Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
28
Qwen3.6-35B-A3B
64
Verified leaderboard positions: GPT-4.1 nano unranked · Qwen3.6-35B-A3B #13
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+10.2 difference
GPT-4.1 nano
Qwen3.6-35B-A3B
$0.1 / $0.4
N/A
181 t/s
N/A
0.63s
N/A
1M
262K
Pick Qwen3.6-35B-A3B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or 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 28. 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 knowledge, where it averages 60.5 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 86%.
Qwen3.6-35B-A3B is the reasoning model in the pair, while GPT-4.1 nano 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. GPT-4.1 nano gives you the larger context window at 1M, compared with 262K for Qwen3.6-35B-A3B.
Qwen3.6-35B-A3B is ahead on BenchLM's provisional leaderboard, 64 to 28. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 86%.
Qwen3.6-35B-A3B has the edge for knowledge tasks in this comparison, averaging 60.5 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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