Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiMo-V2.5-Pro
86
Qwen3.6-27B
73
Verified leaderboard positions: MiMo-V2.5-Pro unranked · Qwen3.6-27B #18
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if knowledge is the priority.
Agentic
+9.1 difference
Coding
+13.4 difference
Knowledge
+14.2 difference
MiMo-V2.5-Pro
Qwen3.6-27B
$null / $null
$0 / $0
N/A
N/A
N/A
N/A
1M
262K
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if knowledge is the priority.
MiMo-V2.5-Pro is clearly ahead on the provisional aggregate, 86 to 73. 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 59.3. The single biggest benchmark swing on the page is HLE, 48% to 24%. Qwen3.6-27B 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 gives you the larger context window at 1M, compared with 262K for Qwen3.6-27B.
MiMo-V2.5-Pro is ahead on BenchLM's provisional leaderboard, 86 to 73. The biggest single separator in this matchup is HLE, where the scores are 48% and 24%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 48. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for coding in this comparison, averaging 70.6 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 59.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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