Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
66
Qwen3.6 Plus
65
Verified leaderboard positions: GLM-5 #17 · Qwen3.6 Plus #16
Pick GLM-5 if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Agentic
+5.4 difference
Coding
+1.6 difference
Reasoning
+1.2 difference
Knowledge
+4.7 difference
Multilingual
+1.6 difference
Inst. Following
+4.8 difference
GLM-5
Qwen3.6 Plus
$1 / $3.2
$null / $null
74 t/s
N/A
1.64s
N/A
200K
1M
Pick GLM-5 if you want the stronger benchmark profile. Qwen3.6 Plus only becomes the better choice if agentic is the priority or you need the larger 1M context window.
GLM-5 finishes one point ahead on BenchLM's provisional leaderboard, 66 to 65. 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.
GLM-5's sharpest advantage is in instruction following, where it averages 92.6 against 87.8. The single biggest benchmark swing on the page is HLE, 50.4% to 28.8%. Qwen3.6 Plus does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
Qwen3.6 Plus is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.6 Plus gives you the larger context window at 1M, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 66 to 65. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 28.8%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 66. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for coding in this comparison, averaging 64.8 versus 63.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for reasoning in this comparison, averaging 62 versus 60.8. Inside this category, AA-LCR is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 61.6 versus 56.2. Inside this category, DeepPlanning is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 87.8. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for multilingual tasks in this comparison, averaging 84.7 versus 83.1. Inside this category, NOVA-63 is the benchmark that creates the most daylight between them.
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