GLM-5 (EmberCloud)

GLM-5 is a next-generation foundation model developed by the GLM-5 Team, with affiliation listed as Zhipu AI and Tsinghua University. It builds upon the agentic, reasoning, and coding capabilities of its predecessor, expanding the lineage toward more autonomous software engineering workflows. The model's technical positioning is captured in its report title, "GLM-5: from Vibe Coding to Agentic Engineering," signaling a shift from interactive coding assistance toward systems that can handle end-to-end development challenges with greater independence.

Architecturally, GLM-5 introduces a sparse-attention mechanism referred to as DSA, designed to lower training and inference cost while preserving fidelity over long contexts. To improve alignment and autonomy, the team implemented an asynchronous reinforcement learning infrastructure that decouples generation from training, enabling more effective learning from complex, long-horizon agent interactions. The report describes GLM-5 as achieving state-of-the-art results on major open benchmarks and surpassing prior baselines on real-world coding tasks. As an open-weight model with weights and code released publicly, GLM-5 is well suited for developers and researchers exploring agentic coding systems, tool-augmented workflows, and reasoning-intensive applications that benefit from cost-efficient long-context processing.