Thinking Levels: Low, High and Max
GLM models offer three thinking levels — low, high and max — so you can spend compute only where it is needed: instant answers for simple questions, deep reasoning for complex engineering.
Thinking effort: low, high and max
GLM models come in more than one speed. Effort controls let you spend compute only when needed. GLM-5.3 always thinks (low / high / max, default max); per-tier efficiency figures are GLM-5.2 reported values, for reference.
Low
~35K average tokens
Scenarios where latency is more important than depth, such as quick editing, template code, and routine refactoring.
High
~43K average tokens
The default choice for most agent programming: approaching the highest quality with about half the token consumption.
Highest
~83K average tokens
Difficult long-term problems — allocate additional computing resources only when the task truly requires it.
The table below shows GLM-5.3 against comparison models, as officially published by Z.ai. GLM-5.3 shares the GLM-5.2 base — every gain comes from post-training. glmmodel.cn only reports these results.