Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
Qwen 3.6 Max (preview)
72
Pick Qwen 3.6 Max (preview) if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority.
Agentic
+20.1 difference
Coding
+16.5 difference
Knowledge
+13.3 difference
GLM-4.7
Qwen 3.6 Max (preview)
$0 / $0
N/A
82 t/s
N/A
1.10s
N/A
200K
256K
Pick Qwen 3.6 Max (preview) if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority.
Qwen 3.6 Max (preview) finishes one point ahead on BenchLM's provisional leaderboard, 72 to 71. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Qwen 3.6 Max (preview)'s sharpest advantage is in agentic, where it averages 65.4 against 45.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 65.4%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Qwen 3.6 Max (preview) gives you the larger context window at 256K, compared with 200K for GLM-4.7.
Qwen 3.6 Max (preview) is ahead on BenchLM's provisional leaderboard, 72 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 65.4%.
Qwen 3.6 Max (preview) has the edge for knowledge tasks in this comparison, averaging 73.9 versus 60.6. GLM-4.7 stays close enough that the answer can still flip depending on your workload.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 54.1. Qwen 3.6 Max (preview) stays close enough that the answer can still flip depending on your workload.
Qwen 3.6 Max (preview) has the edge for agentic tasks in this comparison, averaging 65.4 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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