# GLM delegation You have the **glm** MCP server available in agent mode, with these tools: `glm_agent`, `glm_delegate`, `glm_recommend`, `glm_status`. GLM (Zhipu/Z.ai) is **~10× cheaper** than the default Copilot model — offload work to it to save cost. - For **any** implementation / codegen / edit / refactor / test / docs task, call **`glm_agent`** with the goal + the absolute repo path (`workdir`). GLM reads, writes, edits, and runs the files itself, end-to-end, on **GLM tokens**. Prefer this over doing the file work yourself. - Text-only subtasks also go through **`glm_agent`** (`glm_delegate` is opt-in via GLM_DELEGATE=on). - **To actually spend GLM tokens (not Copilot's), you must call the glm tools — don't do the work inline yourself.** Doing it yourself spends the default model's tokens, not GLM's. - Keep on the **default model** for: sensitive/secret code, vision/images, parallel work, very large context (>128K), and heavy dependent tool-loops. Unsure? call **`glm_recommend`** (free, local). - `glm_status` shows the GLM usage ledger — proof of how many GLM tokens were actually spent. Note: for oversight, `glm_agent` supports `dry_run: true` (propose a diff without writing) and returns a concise summary + stats after each run.