Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro
71
MiMo-V2-Omni
83
Verified leaderboard positions: DeepSeek V4 Pro #22 · MiMo-V2-Omni unranked
Pick MiMo-V2-Omni if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+16.0 difference
DeepSeek V4 Pro
MiMo-V2-Omni
$1.74 / $3.48
N/A
N/A
N/A
N/A
N/A
1M
262K
Pick MiMo-V2-Omni if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
MiMo-V2-Omni is clearly ahead on the provisional aggregate, 83 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2-Omni's sharpest advantage is in coding, where it averages 74.8 against 58.8. The single biggest benchmark swing on the page is SWE-bench Verified, 73.6% to 74.8%.
MiMo-V2-Omni is the reasoning model in the pair, while DeepSeek V4 Pro 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. DeepSeek V4 Pro gives you the larger context window at 1M, compared with 262K for MiMo-V2-Omni.
MiMo-V2-Omni is ahead on BenchLM's provisional leaderboard, 83 to 71. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.6% and 74.8%.
MiMo-V2-Omni has the edge for coding in this comparison, averaging 74.8 versus 58.8. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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