GLM-5 is positioned as a next-generation foundation model aimed at moving software development from casual "vibe coding" toward full agentic engineering. Built on the agentic, reasoning, and coding strengths of its predecessor, it adopts DeepSeek Sparse Attention (DSA) to meaningfully cut training and inference cost while still holding long context together, an important property when a model has to keep hours of work in mind. The architecture is a 744B-parameter mixture-of-experts design that activates roughly 40B parameters per pass, balancing scale with efficiency so the system can be served at production cost while tackling large codebases and multi-step engineering jobs. Multiple thinking modes let developers trade depth for latency, and the model supports streaming, function calling, context caching, and structured output, making it well suited to integration inside modern agent frameworks and IDE-style coding tools.
On the post-training side, the team rebuilt the reinforcement learning stack around an asynchronous infrastructure that decouples generation from training, dramatically improving throughput, and paired it with new asynchronous agent RL algorithms that let the model learn from long, complex interactions rather than short, isolated prompts. These choices, combined with continued training in agentic, reasoning, and coding (ARC) skills, translate directly into the strengths highlighted by the team and ecosystem partners: 77.8% on SWE-Bench Verified, a leading open-source showing on Vending Bench 2 for long-horizon planning, and a coding experience that the developers describe as approaching Claude Opus 4.5 in real programming scenarios. For practitioners, GLM-5 fits best where a single model has to plan, debug, refactor, and run end-to-end across extended sessions, such as autonomous backend construction, deep debugging with iterative self-correction, and architect-level decomposition of system requirements, while remaining an open-weight option that teams can self-host, inspect, and fine-tune.
