Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
Qwen3 235B 2507
33
Verified leaderboard positions: GLM-5.1 #21 · Qwen3 235B 2507 unranked
Pick GLM-5.1 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+23.9 difference
GLM-5.1
Qwen3 235B 2507
$1.4 / $4.4
$0 / $0
N/A
N/A
N/A
N/A
203K
128K
Pick GLM-5.1 if you want the stronger benchmark profile. Qwen3 235B 2507 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3 235B 2507. That is roughly Infinityx on output cost alone. GLM-5.1 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-5.1 gives you the larger context window at 203K, compared with 128K for Qwen3 235B 2507.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 33.
Qwen3 235B 2507 has the edge for knowledge tasks in this comparison, averaging 76.2 versus 52.3. GLM-5.1 stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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