GLM 5, released as open source in Singapore, is presented as a signal that large language models are evolving from simple code and interface generation to building complete systems and executing complex, end-to-end tasks. This transition is framed as a move away from informal “vibe coding” toward agentic engineering, where models are expected to plan, make decisions, and manage multi step workflows more like human engineers. GLM 5 is positioned as one of the strongest open source models for coding and autonomous task execution, particularly in real world programming contexts that demand complex system design and sustained planning.
The model is built on a new architecture designed to scale both capability and efficiency. Its parameter count has expanded from 355bn to 744bn, with active parameters rising from 32bn to 40bn, while pre training data has grown to 28.5trn tokens. These scaling moves are paired with changes in training strategy: a framework called Slime enables asynchronous reinforcement learning at a larger scale, so the model can learn continuously from extended interactions and improve post training efficiency. GLM 5 also introduces DeepSeek Sparse Attention, a technique that is described as maintaining long context performance while cutting deployment costs and improving token efficiency, which is critical for long horizon reasoning and planning.
Benchmark results are used to illustrate how GLM 5 performs on tasks that resemble engineering and operations work. On SWE-bench-Verified and Terminal Bench 2.0, GLM-5 scores 77.8 and 56.2, respectively, which are reported as the highest results for open source models and as surpassing Gemini 3 Pro in several software engineering tasks. On Vending Bench 2, which simulates running a vending machine business over a year, it finishes with a balance of $4,432, leading other open source models in operational and economic management. These outcomes are highlighted as evidence that GLM 5 can maintain goals over long time spans, manage resources, and coordinate multi step processes, reinforcing the view that the frontier of artificial intelligence is shifting from writing code to delivering functioning, integrated systems.
