Developers working with large codebases often hit the limits of their AI coding assistants. Small context windows force constant summarizing, breaking the flow when trying to refactor many files, trace dependencies, or run long autonomous agent loops. Switching models or re‑fetching context wastes time and introduces errors.
GLM‑5.2 solves these pain points by offering a usable one‑million‑token context window. This lets an agent hold an entire mid‑sized repository—source, tests, configs, and conversation history—in a single view. The model can return up to 131 072 tokens per response, so detailed edits or explanations stay intact.
Two thinking‑effort levels, High and Max, give control over reasoning depth. For complex, multi‑step coding work, selecting Max effort (via the /effort command) lets the model plan, execute, test, and fix loops without frequent breaks.
Setup is straightforward. For Claude Code users, edit the settings file to point the Sonnet and Opus slots to glm‑5.2[1m] and raise the auto‑compact window to one million tokens. Alternatively, export environment variables that swap the Anthropic‑compatible base URL to https://api.z.ai/api/anthropic and set the default models to glm‑5.2[1m]. Then run /effort max and verify with /status.
The same approach works for Cline, OpenClaw, and other agentic tools that support an OpenAI‑compatible endpoint: point the base URL to https://api.z.ai/api/coding/paas/v4, specify the model glm‑5.2, and set the context size to one million.
With these steps, teams can drop‑in GLM‑5.2 as a replacement for existing assistants, keep their current workflows, and gain the ability to handle whole‑repository refactors, long‑horizon agent runs, and large document analysis without the usual context‑window headaches.
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