Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
42
GLM-4.7
71
Pick GLM-4.7 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+21.6 difference
Knowledge
+1.2 difference
Claude 3.5 Sonnet
GLM-4.7
$null / $null
$0 / $0
N/A
82 t/s
N/A
1.10s
200K
200K
Pick GLM-4.7 if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7 is clearly ahead on the provisional aggregate, 71 to 42. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 49. The single biggest benchmark swing on the page is GPQA, 59.4% to 85.7%.
GLM-4.7 is the reasoning model in the pair, while Claude 3.5 Sonnet 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 is ahead on BenchLM's provisional leaderboard, 71 to 42. The biggest single separator in this matchup is GPQA, where the scores are 59.4% and 85.7%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 59.4. Inside this category, GPQA 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 49. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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