Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
Qwen3.6-35B-A3B
66
Verified leaderboard positions: GLM-4.7 unranked · Qwen3.6-35B-A3B #23
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if agentic is the priority or you need the larger 262K context window.
Agentic
+6.2 difference
Coding
+3.7 difference
Knowledge
+0.1 difference
GLM-4.7
Qwen3.6-35B-A3B
$0 / $0
N/A
82 t/s
N/A
1.10s
N/A
200K
262K
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.6-35B-A3B only becomes the better choice if agentic is the priority or you need the larger 262K context window.
GLM-4.7 has the cleaner provisional overall profile here, landing at 69 versus 66. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 66.9. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 51.5%. Qwen3.6-35B-A3B does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.6-35B-A3B gives you the larger context window at 262K, compared with 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 69 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 51.5%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 60.5. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 66.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Qwen3.6-35B-A3B has the edge for agentic tasks in this comparison, averaging 51.5 versus 45.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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