GLM 5.2 may keep near-max coding quality with far fewer tokens

GLM 5.2 appears to use far more reasoning tokens than GLM 5.1, with one comparison rising from about 16,700 tokens to 36,700 tokens. Because GLM 5.2 is set to max effort by default, local runs can become very slow and expensive in practice. On an older Xeon machine, one local test became unusable: a math question still had no answer after 12 hours.

A graph in Z.ai’s technical report suggests that high effort can use less than half the tokens of max effort while reaching about 98% of max-level intelligence on coding tasks. That makes high effort worth testing for both local users and API users who care about cost and speed. In follow-up tests, a 4-bit quantized local run at high effort produced an answer after about 6,000 tokens, but the answer was only partly right; Z.ai chat results at high and max effort looked broadly similar, with small differences in how edge cases were handled.

Related hardware reports show that GLM 5.2 can run with long context on very large personal machines, including high-memory Mac Studio setups and multi-GPU desktops, but ordinary local hardware may still face serious memory and waiting-time limits.