Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 nano
27
Qwen3.6 Plus
73
Verified leaderboard positions: GPT-4.1 nano unranked · Qwen3.6 Plus #12
Pick Qwen3.6 Plus if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+15.7 difference
Inst. Following
+4.6 difference
GPT-4.1 nano
Qwen3.6 Plus
$0.1 / $0.4
$null / $null
181 t/s
N/A
0.63s
N/A
1M
1M
Pick Qwen3.6 Plus if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6 Plus is clearly ahead on the provisional aggregate, 73 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6 Plus's sharpest advantage is in knowledge, where it averages 66 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 90.4%.
Qwen3.6 Plus 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.
Qwen3.6 Plus is ahead on BenchLM's provisional leaderboard, 73 to 27. The biggest single separator in this matchup is GPQA, where the scores are 50.3% and 90.4%.
Qwen3.6 Plus has the edge for knowledge tasks in this comparison, averaging 66 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for instruction following in this comparison, averaging 87.8 versus 83.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.