Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro
71
MiMo-V2.5
74
Verified leaderboard positions: DeepSeek V4 Pro #22 · MiMo-V2.5 unranked
Pick MiMo-V2.5 if you want the stronger benchmark profile. DeepSeek V4 Pro 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
+6.7 difference
Coding
+2.7 difference
DeepSeek V4 Pro
MiMo-V2.5
$1.74 / $3.48
$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 Pro 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 has the cleaner provisional overall profile here, landing at 74 versus 71. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
MiMo-V2.5's sharpest advantage is in agentic, where it averages 65.8 against 59.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 59.1% to 65.8%. DeepSeek V4 Pro 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 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.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 59.1% and 65.8%.
DeepSeek V4 Pro has the edge for coding in this comparison, averaging 58.8 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 59.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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