zai-org/GLM-5.3
GLM-5.3 is a 753.3 billion parameter language model developed by zai-org, building upon the GLM-5.2 base. It demonstrates significant advancements in complex coding and long-horizon tasks, achieving open-source state-of-the-art performance on various coding benchmarks. Notably, GLM-5.3 exhibits emergent cyber capabilities, excelling in vulnerability discovery and exploitation tasks. This model is primarily optimized for advanced software engineering and cybersecurity applications.
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GLM-5.3 Overview
GLM-5.3 is a 753.3 billion parameter model from zai-org, representing a significant post-training enhancement over its predecessor, GLM-5.2. This iteration focuses on boosting performance in complex coding and long-horizon tasks, showcasing substantial improvements across various benchmarks.
Key Capabilities & Differentiators
- Stronger Coding Performance: Achieves a 50% improvement over GLM-5.2 on zai-org's in-house Z.ai Code Bench and sets new open-source state-of-the-art (SOTA) records on public benchmarks like Terminal Bench 3.0 and Agents' Last Exam.
- Emergent Cyber Capability: Demonstrates advanced capabilities in cybersecurity, achieving SOTA on CyberGym for vulnerability discovery and more than doubling GLM-5.2's performance on exploitation benchmarks.
- Benchmark Leadership: Outperforms many comparable models on critical coding and agentic benchmarks, including Terminal Bench 3.0, CyberGym, ExploitGym, and AutomationBench.
- Configurable Reasoning: Supports a
reasoning_effortparameter withlow,high, andmaxlevels, allowing users to control computational intensity, defaulting tomaxfor benchmark reproduction.
Ideal Use Cases
GLM-5.3 is particularly well-suited for applications requiring:
- Advanced Software Engineering: Tasks involving complex code generation, debugging, and automated software development.
- Cybersecurity Research & Development: Vulnerability discovery, exploit generation, and other cyber-agentic operations.
- Agentic Workflows: Scenarios demanding long-horizon planning and execution, especially in technical domains.