Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
82
Qwen3.5 397B
63
Verified leaderboard positions: GLM-5.1 #25 · Qwen3.5 397B #19
Pick GLM-5.1 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+9.1 difference
Coding
+0.6 difference
Knowledge
+12.9 difference
GLM-5.1
Qwen3.5 397B
$1.4 / $4.4
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
203K
128K
Pick GLM-5.1 if you want the stronger benchmark profile. Qwen3.5 397B 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, 82 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 56.2. The single biggest benchmark swing on the page is HLE, 52.3% to 28.7%. Qwen3.5 397B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 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-5.1 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-5.1 gives you the larger context window at 203K, compared with 128K for Qwen3.5 397B.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 82 to 63. The biggest single separator in this matchup is HLE, where the scores are 52.3% and 28.7%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 versus 60.3. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 56.2. Inside this category, MCP Atlas is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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