Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 4 Sonnet
50
GLM-5.2
94
Verified leaderboard positions: Claude 4 Sonnet unranked · GLM-5.2 #9
Pick GLM-5.2 if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+10.6 difference
Claude 4 Sonnet
GLM-5.2
$3 / $15
$1.4 / $4.4
40 t/s
N/A
1.33s
N/A
200K
1M
Pick GLM-5.2 if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude 4 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 3.4x on output cost alone. GLM-5.2 is the reasoning model in the pair, while Claude 4 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-5.2 gives you the larger context window at 1M, compared with 200K for Claude 4 Sonnet.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 50.
Claude 4 Sonnet has the edge for coding in this comparison, averaging 72.7 versus 62.1. GLM-5.2 stays close enough that the answer can still flip depending on your workload.
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