Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Holo3-122B-A10B
100
MiMo-V2.5
72
Pick Holo3-122B-A10B if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if you need the larger 1M context window or you want the stronger reasoning-first profile.
Agentic
+13.1 difference
Holo3-122B-A10B
MiMo-V2.5
$0.4 / $3
$null / $null
N/A
N/A
N/A
N/A
64K
1M
Pick Holo3-122B-A10B if you want the stronger benchmark profile. MiMo-V2.5 only becomes the better choice if you need the larger 1M context window or you want the stronger reasoning-first profile.
Holo3-122B-A10B is clearly ahead on the provisional aggregate, 100 to 72. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-122B-A10B's sharpest advantage is in agentic, where it averages 78.9 against 65.8.
MiMo-V2.5 is the reasoning model in the pair, while Holo3-122B-A10B 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 64K for Holo3-122B-A10B.
Holo3-122B-A10B is ahead on BenchLM's provisional leaderboard, 100 to 72.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 65.8. MiMo-V2.5 stays close enough that the answer can still flip depending on your workload.
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