GLM-5 vs GLM-5.1

GLM-5 makes more sense if knowledge is the priority or you want the cheaper token bill, while GLM-5.1 is the cleaner fit if agentic is the priority or you need the larger 203K context window.

GLM-5 and GLM-5.1 sit in the same GLM-5 family. This page is less about two unrelated model lineages and more about how the siblings trade off on benchmark shape, token costs, and practical limits like context window.

GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 56.2% to 63.5%. GLM-5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. GLM-5.1 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. GLM-5.1 gives you the larger context window at 203K, compared with 200K for GLM-5.