Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1
58
Qwen3.7 Max
93
Verified leaderboard positions: GPT-4.1 unranked · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+19.0 difference
Knowledge
+4.9 difference
Inst. Following
+1.6 difference
GPT-4.1
Qwen3.7 Max
$2 / $8
$null / $null
108 t/s
N/A
1.02s
N/A
1M
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.7 Max is clearly ahead on the provisional aggregate, 93 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.7 Max's sharpest advantage is in coding, where it averages 73.6 against 54.6. The single biggest benchmark swing on the page is GPQA, 66.3% to 92.4%.
Qwen3.7 Max is the reasoning model in the pair, while GPT-4.1 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.
Qwen3.7 Max is ahead on BenchLM's provisional leaderboard, 93 to 58. The biggest single separator in this matchup is GPQA, where the scores are 66.3% and 92.4%.
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 71.2 versus 66.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for coding in this comparison, averaging 73.6 versus 54.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for instruction following in this comparison, averaging 89 versus 87.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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