GLM 5.3: Agentic Coding and Long-Horizon Model
GLM 5.3 is a Mixture-of-Experts model from Z.ai. It uses the same base model as GLM 5.2, and every gain comes from post-training. Compared with GLM 5.2 it is much stronger at complex coding and long-horizon agentic tasks, with native English and Chinese support.
Key Features
- Stronger Coding - Z.ai reports a 50% improvement over GLM 5.2 on its in-house Z.ai Code Bench
- Terminal and Agent Work - 88.2 on Terminal Bench 2.1 (up from 81.0) and 28.3 on Terminal Bench 3.0 (up from 4.6)
- Software Engineering - 66.9 on DeepSWE, up from 46.2 for GLM 5.2
- Security Research - 84.5 on CyberGym for vulnerability discovery
- Tool Use - 62.5 on Humanity's Last Exam with tools and 73.0 on Toolathlon Verified
- Adjustable Thinking - The
reasoning_effort setting accepts low, high, or max (the default)
Use Cases
- Repository-level software engineering and multi-file code changes
- Long-running agentic workflows in terminal and coding harnesses
- Vulnerability discovery and security analysis
- Multi-step tool calling and automation
- Long-context document and codebase analysis
Architecture and Design
GLM 5.3 keeps the GLM 5.2 architecture: a sparse-attention Mixture-of-Experts design in which a lightweight indexer picks the tokens each query attends to, keeping long-context inference affordable across its 1M-token window. The checkpoint ships in FP8 and includes a multi-token prediction layer.
Training and Inference
All improvements over GLM 5.2 come from post-training. Z.ai evaluates with a temperature of 1.0 and top-p of 0.95. In the chat template, clear_thinking defaults to false; for chat use, pass clear_thinking=true. Leave reasoning_effort at its default max to reproduce published benchmark results.
Deploy GLM 5.3 on Vast.ai for agentic coding and long-context work on flexible GPU infrastructure.
