Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
Qwen3.6-27B
72
Verified leaderboard positions: GLM-4.7 unranked · Qwen3.6-27B #10
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 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.
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, 72 to 71. 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, GPQA 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, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.