A user tested GLM 5.2 on a complex, multi-file computer vision implementation task to evaluate its production-grade capabilities beyond benchmark scores. The model successfully built a browser-based CV studio involving object detection, persistent tracking, and a FastAPI backend, demonstrating strong architectural planning and consistency.
- It generated a planning document that preemptively addressed canvas tainting bugs and designed a same-origin video proxy before writing code.
- JSON contracts between the frontend tracker, report panel, and backend system prompt remained consistent across multiple rounds of edits.
- The model performed self-verification by running production builds and checking backend routes after changes rather than declaring success immediately.
- It made reasonable technical tradeoffs without prompting, such as choosing Mobilenet_v2 for CPU accuracy and WASM for portability.
The test highlights GLM 5.2's strength in coding and tool use, though it remains text-only and trails frontier models in pure math and non-English tasks.