Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 4 Sonnet
50
GLM-4.7
68
Pick GLM-4.7 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
+2.1 difference
Claude 4 Sonnet
GLM-4.7
$3 / $15
$0 / $0
40 t/s
82 t/s
1.33s
1.10s
200K
200K
Pick GLM-4.7 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-4.7 is clearly ahead on the provisional aggregate, 68 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 $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 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-4.7 is ahead on BenchLM's provisional leaderboard, 68 to 50. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.7% and 73.8%.
Claude 4 Sonnet has the edge for coding in this comparison, averaging 72.7 versus 70.6. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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