GLM-5 is a next-generation foundation model designed to shift the paradigm from vibe coding to agentic engineering. Published as an arXiv technical report (arXiv:2602.15763v1) by the GLM-5 Team with affiliations at Zhipu AI and Tsinghua University, the model builds upon the agentic, reasoning, and coding ARC capabilities of its predecessor. To advance autonomy and long-horizon problem solving, the team introduced a new asynchronous reinforcement learning infrastructure that decouples generation from training, drastically improving post-training efficiency, along with novel asynchronous agent RL algorithms that help the model learn from complex, long-horizon interactions. The work also adopts DSA to significantly reduce training and inference costs while maintaining long-context fidelity.

The combination of these innovations is reported to yield state-of-the-art performance on major open benchmarks, with particularly strong capability on real-world coding tasks and end-to-end software engineering challenges. According to the published abstract, GLM-5 surpasses previous baselines in handling full-stack engineering workflows, positioning it as a practical foundation for coding agents and autonomous development tools. Code, models, and additional information are made available through the project's GitHub repository, reflecting an open research approach to advancing agentic coding systems.