Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M2.7
54
Step 3.7 Flash
72
Pick Step 3.7 Flash 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
+8.9 difference
Coding
+2.6 difference
MiniMax M2.7
Step 3.7 Flash
$0.3 / $1.2
$0.2 / $1.15
45 t/s
N/A
2.53s
N/A
200K
256K
Pick Step 3.7 Flash 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.
Step 3.7 Flash is clearly ahead on the provisional aggregate, 72 to 54. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Step 3.7 Flash's sharpest advantage is in agentic, where it averages 65.9 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 57% to 59.5%.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. Step 3.7 Flash 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. Step 3.7 Flash gives you the larger context window at 256K, compared with 200K for MiniMax M2.7.
Step 3.7 Flash is ahead on BenchLM's provisional leaderboard, 72 to 54. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 57% and 59.5%.
Step 3.7 Flash has the edge for coding in this comparison, averaging 56.3 versus 53.7. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
Step 3.7 Flash has the edge for agentic tasks in this comparison, averaging 65.9 versus 57. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
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