Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
45
Qwen3.7 Max
93
Verified leaderboard positions: GPT-4.1 mini unranked · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+50.0 difference
Knowledge
+7.0 difference
Inst. Following
+0.5 difference
GPT-4.1 mini
Qwen3.7 Max
$0.4 / $1.6
$null / $null
80 t/s
N/A
0.76s
N/A
1M
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. GPT-4.1 mini 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 45. 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 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 23.6% to 80.4%.
Qwen3.7 Max is the reasoning model in the pair, while GPT-4.1 mini 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 45. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 23.6% and 80.4%.
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 71.2 versus 64.2. 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 23.6. 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 88.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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