Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
69
Qwen3.5-27B
63
Verified leaderboard positions: GLM-4.7 unranked · Qwen3.5-27B #16
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
Agentic
+6.3 difference
Coding
+7.6 difference
Knowledge
+20.0 difference
GLM-4.7
Qwen3.5-27B
$0 / $0
$0 / $0
82 t/s
N/A
1.10s
N/A
200K
262K
Pick GLM-4.7 if you want the stronger benchmark profile. Qwen3.5-27B only becomes the better choice if knowledge is the priority or you need the larger 262K context window.
GLM-4.7 is clearly ahead on the provisional aggregate, 69 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 63. The single biggest benchmark swing on the page is BrowseComp, 52% to 61%. Qwen3.5-27B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-27B 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 63. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 61%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 60.6. Inside this category, MMLU-Pro 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 63. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 45.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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