Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
35
MiMo-V2.5-Pro
86
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+18.0 difference
Knowledge
+22.0 difference
DeepSeek V3
MiMo-V2.5-Pro
$0.27 / $1.1
$null / $null
N/A
N/A
N/A
N/A
128K
1M
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
MiMo-V2.5-Pro is clearly ahead on the provisional aggregate, 86 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5-Pro's sharpest advantage is in coding, where it averages 57.2 against 39.2. DeepSeek V3 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5-Pro is the reasoning model in the pair, while DeepSeek V3 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.
MiMo-V2.5-Pro is ahead on BenchLM's provisional leaderboard, 86 to 35.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 48. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
MiMo-V2.5-Pro has the edge for coding in this comparison, averaging 57.2 versus 39.2. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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