Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
60
MiMo-V2.5
74
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 want the cheaper token bill.
Coding
+4.8 difference
DeepSeek V3.2
MiMo-V2.5
$0 / $0
$0.4 / $2
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 want the cheaper token bill.
MiMo-V2.5 is clearly ahead on the provisional aggregate, 74 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for DeepSeek V3.2. That is roughly Infinityx on output cost alone. 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, 74 to 60.
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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