GLM-4.7 vs GLM-5

Pick GLM-4.7 if you want the stronger benchmark profile. GLM-5 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

GLM-4.7 finishes one point ahead on BenchLM's provisional leaderboard, 68 to 67. 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-4.7's sharpest advantage is in coding, where it averages 70.6 against 63.2. The single biggest benchmark swing on the page is HLE, 24.8% to 50.4%. GLM-5 does hit back in agentic, 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 GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 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.