GLM-5 vs Qwen3.6-27B

Pick Qwen3.6-27B if you want the stronger benchmark profile. GLM-5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

Qwen3.6-27B 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.

Qwen3.6-27B's sharpest advantage is in coding, where it averages 70.6 against 63.2. The single biggest benchmark swing on the page is HLE, 50.4% to 24%. GLM-5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for GLM-5.