Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
Qwen3 235B 2507
35
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+15.6 difference
GLM-4.7
Qwen3 235B 2507
$0 / $0
$0 / $0
82 t/s
N/A
1.10s
N/A
200K
128K
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge 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, 71 to 35. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7 is the reasoning model in the pair, while Qwen3 235B 2507 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 128K for Qwen3 235B 2507.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 71 to 35. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 77.5%.
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 76.2 versus 60.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.