Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash
59
MiMo-V2.5
74
Verified leaderboard positions: DeepSeek V4 Flash #23 · MiMo-V2.5 unranked
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+16.7 difference
Coding
+1.0 difference
DeepSeek V4 Flash
MiMo-V2.5
$0.14 / $0.28
$null / $null
N/A
N/A
N/A
N/A
1M
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if coding is the priority 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 59. 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 agentic, where it averages 65.8 against 49.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 49.1% to 65.8%. DeepSeek V4 Flash does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5 is the reasoning model in the pair, while DeepSeek V4 Flash 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 is ahead on BenchLM's provisional leaderboard, 74 to 59. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 49.1% and 65.8%.
DeepSeek V4 Flash has the edge for coding in this comparison, averaging 57.1 versus 56.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 49.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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