Run long-term coding tasks with thousands of tool calls using an open-weight model
You can run a model that decomposes complex problems, experiments, reads results, and adjusts its strategy over hundreds of rounds. The GLM-5 series is a large language model designed for agent tasks that run for extended periods without stopping, going beyond simple code generation. The latest version, GLM-5.3, significantly improved coding capabilities and cybersecurity-related skills through post-training.
Post-training results for GLM-5.3
GLM-5.3 uses the same base model as GLM-5.2 but achieved significant performance improvements through the post-training process. It showed a 50% performance improvement over GLM-5.2 on the internal evaluation benchmark Z.ai Code Bench, and recorded top-tier scores among open-source models on public benchmarks such as Terminal Bench 3.0 and Agents' Last Exam. It also demonstrated clear progress in cybersecurity, performing more than twice as well as GLM-5.2 in vulnerability discovery and exploitation stages.
Architecture improvements for long-term tasks
GLM-5.3-Flash introduces a hybrid architecture combining sparse attention and linear attention, significantly reducing long-context processing costs while maintaining precision. This model uses Manifold-Constrained Hyper-Connections (mHC) to enhance scaling efficiency and provides higher intelligence with fewer compute resources based on a 30-trillion-token multimodal pre-training corpus.
Model deployment and fine-tuning methods
The model is available for download on Hugging Face and ModelScope at a scale of 744B-A40B (40B active out of 744B parameters) and supports FP8 and BF16 precision. Various frameworks such as SGLang, vLLM, Transformers, KTransformers, and Unsloth can be used for local deployment, while the Ascend NPU platform supports vLLM-Ascend, xLLM, and SGLang. GLM-5.2 allows only high and max for the reasoning_effort parameter, whereas GLM-5.3 and GLM-5.3-Flash support three levels: low, high, and max. Fine-tuning supports SFT, PPO, and GRPO methods through the Slime (v0.3.0+) and ms-swift (v4.4.0+) frameworks.
Options suitable for long-term agent tasks
This model is suitable for developers who need to perform complex system engineering and long-term agent tasks. GLM-5.1 demonstrates the ability to judge ambiguous problems, adjust strategies over hundreds of rounds, and continue optimization. It achieved the top rank among open-source models by operating a virtual vending machine business for one year on Vending Bench 2, ending with a final balance of $4,432. However, due to its 744B parameter scale, local execution requires substantial hardware resources, and performance and latency vary depending on the reasoning_effort setting, so parameters should be adjusted according to the intended use.
