Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
Qwen3.6-27B
73
Verified leaderboard positions: GLM-4.7 unranked · Qwen3.6-27B #18
Pick Qwen3.6-27B if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Agentic
+14.0 difference
Coding
Knowledge
+1.6 difference
GLM-4.7
Qwen3.6-27B
$0 / $0
$0 / $0
82 t/s
N/A
1.10s
N/A
200K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 73 to 68. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in agentic, where it averages 59.3 against 45.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 59.3%.
Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for GLM-4.7.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 73 to 68. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 59.3%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 60.6. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GLM-4.7 and Qwen3.6-27B are effectively tied for coding here, both landing at 70.6 on average.
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 45.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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