Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M3
76
Qwen3.7 Max
91
Verified leaderboard positions: MiniMax M3 #12 · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+2.2 difference
Coding
+6.6 difference
MiniMax M3
Qwen3.7 Max
$0.3 / $1.2
$null / $null
N/A
N/A
N/A
N/A
1M
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. MiniMax M3 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.7 Max is clearly ahead on the provisional aggregate, 91 to 76. 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 67. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 66% to 69.7%. MiniMax M3 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.7 Max is the reasoning model in the pair, while MiniMax M3 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, 91 to 76. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 66% and 69.7%.
Qwen3.7 Max has the edge for coding in this comparison, averaging 73.6 versus 67. Inside this category, NL2Repo is the benchmark that creates the most daylight between them.
MiniMax M3 has the edge for agentic tasks in this comparison, averaging 71.9 versus 69.7. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
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