GLM 5.3 Text Model for Coding Agents

Overview

GLM 5.3 is Z.ai’s flagship open-weights text model from the GLM family. It uses a mixture-of-experts design and heavy post-training for complex coding and agent tasks. You provide chat-style instructions and long context, then it produces an assistant reply with code, plans, and analysis. Its 1M-token context and up to 128K output tokens help keep large repos and long tasks in one thread.

What you can build

• Multi-file refactor plans that stay consistent across large codebases
• Debug sessions from long logs and stack traces, with clear root-cause writeups
• Code review feedback that follows project conventions and style rules
• Defensive security review notes, plus patch ideas and verification steps
• Long-horizon agent playbooks that track goals, constraints, and next actions

Inputs

• A single task request written as plain text, such as a coding goal or bug report.
• A full chat transcript with roles like system, user, and assistant, for multi-turn work.
• System instructions on the first turn to set rules, tone, and output format.
• Image attachments in JPEG, PNG, or WEBP when your selected GLM endpoint supports vision.
• Video attachments with MP4 recommended; keep total video uploads under 40 MB, since larger clips may be re-encoded and sampled.
• A user or session identifier when you want the platform to group chat history across turns.
• Reasoning controls where you select how hard the model thinks (low, high, or max).
• Sampling controls that trade off creativity versus repeatability, including nucleus sampling and a sampling temperature.
• Repetition controls that reduce loops and repeated phrases in long generations.
• A maximum output length, up to 128K tokens, to cap long answers.

Outputs

• A chat-style assistant message that contains the main answer as text.
• Optional separate reasoning text when the connected provider exposes it.
• Optional tool-call objects that include a function name and JSON arguments, when tool calling is enabled.
• Optional metadata such as token usage totals and cached token counts, when provided by the backend.

Recommended settings

• For hard coding tasks and long multi-step plans, set reasoning effort to max.
• For faster iterations and quick questions, use low reasoning effort.
• For repeatable code generation and translation, disable sampling so outputs are more deterministic.
• For general chat and brainstorming, keep sampling enabled and use a temperature near 1 with a nucleus cutoff near 0.95.

Limitations

• GLM 5.3 is documented as text-only input. Use GLM 5.3 Flash or FlashX for images, video, and files.
• Reasoning can’t be turned off for GLM 5.3. Requests that try to disable it may fail.
• Output length tops out at 128K tokens, even if your prompt is much longer.
• Very long prompts can still lose details. Put key constraints and acceptance checks near the end.
• Low-quality inputs can mislead the model. This includes truncated logs, partial diffs, inconsistent file context, or OCR artifacts.
• Some gateways ignore custom tool definitions. In those deployments, the model won’t call your tools.

Safety & compliance

• GLM 5.3 is released under the GLM-5.3 License, which grants broad rights to use and modify the weights.
• Keep the copyright notice and license text in redistributed copies.
• Your use must follow applicable laws and regulations.
• If you run a model-as-a-service business above the license’s revenue threshold, you must pass a Z.ai security review for commercial use.
• The model and its outputs come with no warranty.