Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
60
MiMo-V2.5-Pro
82
Pick MiMo-V2.5-Pro 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
+3.7 difference
DeepSeek V3.2
MiMo-V2.5-Pro
$0 / $0
$1 / $3
35 t/s
N/A
3.75s
N/A
128K
1M
Pick MiMo-V2.5-Pro 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-Pro is clearly ahead on the provisional aggregate, 82 to 60. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5-Pro is also the more expensive model on tokens at $1.00 input / $3.00 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-Pro 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-Pro gives you the larger context window at 1M, compared with 128K for DeepSeek V3.2.
MiMo-V2.5-Pro is ahead on BenchLM's provisional leaderboard, 82 to 60.
DeepSeek V3.2 has the edge for coding in this comparison, averaging 60.9 versus 57.2. MiMo-V2.5-Pro stays close enough that the answer can still flip depending on your workload.
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