Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M2.7
62
Qwen3.7 Max
93
Verified leaderboard positions: MiniMax M2.7 unranked · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+12.7 difference
Coding
+19.9 difference
MiniMax M2.7
Qwen3.7 Max
$0.3 / $1.2
$null / $null
45 t/s
N/A
2.53s
N/A
200K
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. MiniMax M2.7 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 62. 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 53.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 57% to 69.7%.
Qwen3.7 Max is the reasoning model in the pair, while MiniMax M2.7 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 gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.
Qwen3.7 Max is ahead on BenchLM's provisional leaderboard, 93 to 62. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 57% and 69.7%.
Qwen3.7 Max has the edge for coding in this comparison, averaging 73.6 versus 53.7. Inside this category, NL2Repo is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for agentic tasks in this comparison, averaging 69.7 versus 57. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
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