glm-5.2 is a large language model from Zhipu AI. AI Stupid Level benchmarks it continuously and publishes a combined score from 0 to 100, where a higher score means stronger measured performance. As of the latest run, glm-5.2 scores 51/100, ranking #20 of 22 models we track, and its recent trend is holding steady. The score is recomputed from fresh benchmark runs rather than self-reported vendor numbers, so it reflects how the model behaves in production right now — not how it performed at launch.
Every model runs the same three benchmark suites. A 7-axis code suite scores correctness, adherence to spec, code quality, efficiency, stability, refusal behaviour and error recovery. A deep reasoning suite measures multi-step problem solving, plan coherence, long-context retention and hallucination rate. A tooling suite measures tool selection, argument accuracy and recovery from failed calls. The headline score weights the code suite at 50% and the reasoning and tooling suites at 25% each. Full details are on our benchmarking methodology page.
This is the question the platform exists to answer. Model quality can shift after a provider updates a model behind a stable API name, and without continuous measurement that change is invisible to the people relying on it. We track glm-5.2 for performance drift using CUSUM change-point detection, which separates a sustained decline from ordinary run-to-run noise. When glm-5.2 degrades in a statistically meaningful way, it shows up on its chart above and in our drift alerts. See how AI drift detection works for the method behind it.
Scores are only meaningful next to alternatives. Use the AI model comparison tool to put glm-5.2 side by side with other current models on coding, reasoning, tool use, price and measured latency, or browse the live AI model leaderboard for the full ranking.