Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
57
MiMo-V2.5
72
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+4.8 difference
DeepSeek V3.2
MiMo-V2.5
$0.28 / $0.42
$null / $null
35 t/s
N/A
3.75s
N/A
128K
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if coding is the priority or 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 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5 is the reasoning model in the pair, while DeepSeek V3.2 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 128K for DeepSeek V3.2.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 72 to 57.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 56.1. MiMo-V2.5 stays close enough that the answer can still flip depending on your workload.
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