Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
GLM-5
77
Verified leaderboard positions: GLM-4.7 unranked · GLM-5 #12
Pick GLM-5 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the stronger reasoning-first profile.
Agentic
+10.9 difference
Coding
+7.4 difference
Knowledge
+10.1 difference
GLM-4.7
GLM-5
$0 / $0
$0 / $0
82 t/s
74 t/s
1.10s
1.64s
200K
200K
Pick GLM-5 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the stronger reasoning-first profile.
GLM-5 is clearly ahead on the provisional aggregate, 77 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in agentic, where it averages 56.2 against 45.3. The single biggest benchmark swing on the page is HLE, 24.8% to 50.4%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-4.7 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.
GLM-5 is ahead on BenchLM's provisional leaderboard, 77 to 71. The biggest single separator in this matchup is HLE, where the scores are 24.8% and 50.4%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 60.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 63.2. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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