Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
Holo3-35B-A3B
75
Pick Holo3-35B-A3B if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if you need the larger 200K context window or you want the stronger reasoning-first profile.
Agentic
+37.3 difference
GLM-4.7
Holo3-35B-A3B
$0 / $0
$null / $null
82 t/s
N/A
1.10s
N/A
200K
64K
Pick Holo3-35B-A3B if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if you need the larger 200K context window or you want the stronger reasoning-first profile.
Holo3-35B-A3B is clearly ahead on the provisional aggregate, 75 to 69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-35B-A3B's sharpest advantage is in agentic, where it averages 82.6 against 45.3.
GLM-4.7 is the reasoning model in the pair, while Holo3-35B-A3B 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-4.7 gives you the larger context window at 200K, compared with 64K for Holo3-35B-A3B.
Holo3-35B-A3B is ahead on BenchLM's provisional leaderboard, 75 to 69.
Holo3-35B-A3B has the edge for agentic tasks in this comparison, averaging 82.6 versus 45.3. GLM-4.7 stays close enough that the answer can still flip depending on your workload.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.