Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
Qwen3.5 397B
62
Verified leaderboard positions: GLM-5.2 #9 · Qwen3.5 397B #22
Pick GLM-5.2 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+24.8 difference
Coding
+1.8 difference
Knowledge
+2.0 difference
GLM-5.2
Qwen3.5 397B
$1.4 / $4.4
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
1M
128K
Pick GLM-5.2 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if you want the cheaper token bill 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 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in agentic, where it averages 81 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 52.5%.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.60 input / $3.60 output per 1M tokens for Qwen3.5 397B. GLM-5.2 is the reasoning model in the pair, while Qwen3.5 397B 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 128K for Qwen3.5 397B.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 62. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 52.5%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 65.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 60.3. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
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