Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiMo-V2.5-Pro
86
Qwen3.5 397B
63
Verified leaderboard positions: MiMo-V2.5-Pro unranked · Qwen3.5 397B #19
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. Qwen3.5 397B 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.
Agentic
+12.2 difference
Coding
+3.1 difference
Knowledge
+17.2 difference
MiMo-V2.5-Pro
Qwen3.5 397B
$null / $null
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
1M
128K
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. Qwen3.5 397B 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 63. 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 agentic, where it averages 68.4 against 56.2. The single biggest benchmark swing on the page is HLE, 48% to 28.7%. Qwen3.5 397B 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 Qwen3.5 397B 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 Qwen3.5 397B.
MiMo-V2.5-Pro is ahead on BenchLM's provisional leaderboard, 86 to 63. The biggest single separator in this matchup is HLE, where the scores are 48% and 28.7%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 48. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for coding in this comparison, averaging 60.3 versus 57.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
MiMo-V2.5-Pro has the edge for agentic tasks in this comparison, averaging 68.4 versus 56.2. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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