Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
MiMo-V2.5
72
Verified leaderboard positions: GLM-5 #21 · MiMo-V2.5 unranked
Pick MiMo-V2.5 if you want the stronger benchmark profile. GLM-5 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
+9.6 difference
Coding
+7.1 difference
GLM-5
MiMo-V2.5
$1 / $3.2
$null / $null
74 t/s
N/A
1.64s
N/A
200K
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. GLM-5 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, 72 to 67. 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 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 65.8%. GLM-5 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 GLM-5 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 gives you the larger context window at 1M, compared with 200K for GLM-5.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 72 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 56.2% and 65.8%.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 56.1. Inside this category, SWE-bench Pro 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 56.2. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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