Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
37
MiMo-V2.5
74
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+16.9 difference
DeepSeek V3
MiMo-V2.5
$0.27 / $1.1
$0.4 / $2
N/A
N/A
N/A
N/A
128K
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill 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, 74 to 37. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5's sharpest advantage is in coding, where it averages 56.1 against 39.2.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. MiMo-V2.5 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 gives you the larger context window at 1M, compared with 128K for DeepSeek V3.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 37.
MiMo-V2.5 has the edge for coding in this comparison, averaging 56.1 versus 39.2. DeepSeek V3 stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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