GLM 5 is Zhipu AI’s latest flagship open weight model built around the idea of “Agentic Engineering”, so it is meant not just to write snippets of code but to design, debug and maintain whole software systems over long multi step workflows. It uses a Mixture of Experts (MoE) setup with roughly 744 to 745 billion total parameters, while only about 40 to 44 billion are active per token, which keeps inference relatively efficient compared to a dense model of the same scale. Compared with GLM 4.5, the parameter count and active parameters both increased and the pretraining data grew from about 23 trillion to roughly 28.5 trillion tokens, which is meant to boost reasoning and robustness in harder tasks. The model is positioned as “Opus class” in terms of code logic and systems engineering ability, aiming to sit close to closed models like Claude Opus in practical coding while still offering open weights and flexible deployment.
On coding benchmarks, GLM 5 reaches leading scores among open models: it reports around 77.8 on SWE bench Verified and about 56.2 on Terminal Bench 2.0, surpassing at least some proprietary competitors such as Gemini 3.0 Pro in aggregate coding and agent evaluations. In agent style benchmarks that test long horizon planning and tool use, like BrowseComp, MCP Atlas and τ² Bench, it ranks near the top of open weight systems and is highlighted for low hallucination rates on the AA Omniscience benchmark, which is important if you want agents that can run for hours without drifting off task. The model is marketed especially for backend heavy work such as architecture design, complex algorithms, log analysis and deep debugging rather than flashy front end content, which lines up with its focus on system level reasoning.
Overall, GLM 5 sits as one of the strongest open weight options in early 2026 for large scale programming and multi tool agents, although its large MoE size and “expert level” positioning also mean it is mainly targeted at more advanced developer and infrastructure setups, not just lightweight consumer use.
