Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiMo-V2-Flash
59
MiniMax M2.7
53
Pick MiMo-V2-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.
Coding
+19.7 difference
MiMo-V2-Flash
MiniMax M2.7
$0 / $0
$0.3 / $1.2
129 t/s
45 t/s
2.14s
2.53s
256K
200K
Pick MiMo-V2-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.
MiMo-V2-Flash is clearly ahead on the provisional aggregate, 59 to 53. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2-Flash's sharpest advantage is in coding, where it averages 73.4 against 53.7.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for MiMo-V2-Flash. That is roughly Infinityx on output cost alone. MiMo-V2-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. MiMo-V2-Flash gives you the larger context window at 256K, compared with 200K for MiniMax M2.7.
MiMo-V2-Flash is ahead on BenchLM's provisional leaderboard, 59 to 53.
MiMo-V2-Flash has the edge for coding in this comparison, averaging 73.4 versus 53.7. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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