Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiMo-V2.5
72
MiniMax M2.7
54
Pick MiMo-V2.5 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
+8.8 difference
Coding
+2.4 difference
MiMo-V2.5
MiniMax M2.7
$null / $null
$0.3 / $1.2
N/A
45 t/s
N/A
2.53s
1M
200K
Pick MiMo-V2.5 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.
MiMo-V2.5 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.
MiMo-V2.5's sharpest advantage is in agentic, where it averages 65.8 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 65.8% to 57%.
MiMo-V2.5 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 200K for MiniMax M2.7.
MiMo-V2.5 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 65.8% and 57%.
MiMo-V2.5 has the edge for coding in this comparison, averaging 56.1 versus 53.7. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 57. Inside this category, MM-ClawBench is the benchmark that creates the most daylight between them.
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