Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
Holo3-122B-A10B
75
Verified leaderboard positions: GLM-5.1 #21 · Holo3-122B-A10B unranked
Pick GLM-5.1 if you want the stronger benchmark profile. Holo3-122B-A10B only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+13.6 difference
GLM-5.1
Holo3-122B-A10B
$1.4 / $4.4
$null / $null
N/A
N/A
N/A
N/A
203K
64K
Pick GLM-5.1 if you want the stronger benchmark profile. Holo3-122B-A10B only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 75. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1 is the reasoning model in the pair, while Holo3-122B-A10B 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. GLM-5.1 gives you the larger context window at 203K, compared with 64K for Holo3-122B-A10B.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 75.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 65.3. GLM-5.1 stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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