GLM-5 (Vertex AI (OpenAI-compatible))

GLM-5 is a next-generation foundation model from Zhipu AI and Tsinghua University, designed to shift coding assistance from informal "vibe coding" toward more autonomous, agent-driven software engineering. It builds directly on the agentic, reasoning, and coding capabilities established by its predecessor, framing those strengths under a unified ARC lens that the team uses to describe complex, tool-using behavior in real-world development tasks.

The architecture adopts DSA, a sparse attention design intended to bring down training and inference costs while preserving fidelity over very long inputs, making the model better suited to repository-scale context than dense-attention baselines. On top of this, the team introduced an asynchronous reinforcement learning pipeline that decouples generation from training to improve post-training efficiency, paired with new asynchronous agent RL algorithms that allow GLM-5 to learn from long-horizon, multi-step interactions. According to the technical report, these choices translate into state-of-the-art results on major open benchmarks and a noticeable lift on end-to-end software engineering challenges, positioning GLM-5 as a strong fit for teams working on autonomous coding agents, complex reasoning pipelines, and long-context development workflows.