Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
MiMo-V2.5-Pro
86
Verified leaderboard positions: GLM-5.1 #25 · MiMo-V2.5-Pro unranked
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if knowledge is the priority.
Agentic
+3.1 difference
Coding
+3.7 difference
Knowledge
+4.3 difference
GLM-5.1
MiMo-V2.5-Pro
$1.4 / $4.4
$null / $null
N/A
N/A
N/A
N/A
203K
1M
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if knowledge is the priority.
MiMo-V2.5-Pro is clearly ahead on the provisional aggregate, 86 to 82. 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 65.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 68.4%. GLM-5.1 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 203K for GLM-5.1.
MiMo-V2.5-Pro is ahead on BenchLM's provisional leaderboard, 86 to 82. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 68.4%.
GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 48. Inside this category, AA-HLE is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 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 65.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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