GLM-5 vs ZAYA1-8B

Pick GLM-5 if you want the stronger benchmark profile. ZAYA1-8B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.

GLM-5 is clearly ahead on the provisional aggregate, 67 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5's sharpest advantage is in instruction following, where it averages 92.6 against 74. The single biggest benchmark swing on the page is GPQA, 86% to 71%. ZAYA1-8B 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 ZAYA1-8B. That is roughly Infinityx on output cost alone. ZAYA1-8B 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 gives you the larger context window at 200K, compared with 131K for ZAYA1-8B.