Head-to-head comparison across 6benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
Qwen3.6 Plus
73
Verified leaderboard positions: GLM-5 #19 · Qwen3.6 Plus #12
Pick Qwen3.6 Plus if you want the stronger benchmark profile. GLM-5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
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 Qwen3.6 Plus if you want the stronger benchmark profile. GLM-5 only becomes the better choice if instruction following is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6 Plus is clearly ahead on the provisional aggregate, 73 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6 Plus's sharpest advantage is in agentic, where it averages 61.6 against 56.2. The single biggest benchmark swing on the page is HLE, 50.4% to 28.8%. GLM-5 does hit back in instruction following, 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.
Qwen3.6 Plus is ahead on BenchLM's provisional leaderboard, 73 to 67. 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, SWE-bench Pro 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, AI-Needle 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, IFEval 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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