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  • GLM-5: From Vibe Coding to Agentic Engineering - z. ai
    We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI)
  • GLM-5. 2 GLM-5. 1 GLM-5 - GitHub
    Compared to GLM-4 5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28 5T tokens GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity
  • GLM-5: from Vibe Coding to Agentic Engineering - arXiv. org
    Similar to GLM-4 5, the base model of GLM-5 goes through two stages: pre-training for general language and coding capacity, and mid-training for agentic and long-context capacity
  • zai-org GLM-5 | DeepWiki
    GLM-5 represents a significant scaling effort from GLM-4 5, targeting improved performance on reasoning, coding, and agentic tasks The model is purpose-built for real-world applications requiring extended planning horizons and complex system interactions
  • GLM-5 - Zhipu AIs Flagship Foundation Model
    GLM-5 by Zhipu AI: 745B parameter MoE model with frontier reasoning, coding, and agentic capabilities
  • [2602. 15763] GLM-5: from Vibe Coding to Agentic Engineering
    Through these innovations, GLM-5 achieves state-of-the-art performance on major open benchmarks Most critically, GLM-5 demonstrates unprecedented capability in real-world coding tasks, surpassing previous baselines in handling end-to-end software engineering challenges
  • zai-org GLM-5 · Hugging Face
    Instructions to use zai-org GLM-5 with libraries, inference providers, notebooks, and local apps Follow these links to get started
  • GLM-5 - Overview - Z. AI DEVELOPER DOCUMENT
    GLM-5 is Z AI’s new-generation foundation model, designed for Agentic Engineering, capable of providing reliable productivity in complex system engineering and long-range Agent tasks
  • GLM 5: Zhipu AI 745B LLM | 200K Context, API How to Use
    In a strategically significant move, GLM-5 has been trained entirely on Huawei Ascend chips using the MindSpore framework, achieving full independence from US-manufactured hardware — positioning GLM-5 as both a technical achievement and a milestone in self-reliant AI infrastructure
  • GLM-5 - AI Wiki
    GLM-5 is an open-weight flagship large language model released by Zhipu AI under its international brand Z ai on February 11, 2026 The model is a 744 billion parameter sparse Mixture of Experts (MoE) transformer with





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