Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
Holo3-122B-A10B
100
Verified leaderboard positions: GLM-5 #21 · Holo3-122B-A10B unranked
Pick Holo3-122B-A10B if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you need the larger 200K context window.
Agentic
+22.7 difference
GLM-5
Holo3-122B-A10B
$1 / $3.2
$0.4 / $3
74 t/s
N/A
1.64s
N/A
200K
64K
Pick Holo3-122B-A10B if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you need the larger 200K context window.
Holo3-122B-A10B is clearly ahead on the provisional aggregate, 100 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-122B-A10B's sharpest advantage is in agentic, where it averages 78.9 against 56.2.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.40 input / $3.00 output per 1M tokens for Holo3-122B-A10B. GLM-5 gives you the larger context window at 200K, compared with 64K for Holo3-122B-A10B.
Holo3-122B-A10B is ahead on BenchLM's provisional leaderboard, 100 to 67.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 56.2. GLM-5 stays close enough that the answer can still flip depending on your workload.
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