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GLM-4.5: Reasoning, Coding, and Agentic Abililties

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Today, we introduce two new GLM family members: GLM-4.5 and GLM-4.5-Air — our latest flagship models. GLM-4.5 is built with 355 billion total parameters and 32 billion active parameters, and GLM-4.5-Air with 106 billion total parameters and 12 billion active parameters. Both are designed to unify reasoning, coding, and agentic capabilities into a single model in order to satisfy more and more complicated requirements of fast rising agentic applications. Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models, offering: thinking mode for complex reasoning and tool using, and non-thinking mode for instant responses. They are available on Z.ai, Z.ai API and open-weights are avaiable at HuggingFace and ModelScope. Background: LLM always targets at achieving human-level cognitive capabilities across a wide range of domains, rather than designed for specific tasks. As a good LLM model, it is necessary to deal with general problem solving, generalization, commen sense reasoning, and self-imp

You are a user interacting with an agent.{instruction_display} # Rules: - Just generate one line at a time to simulate the user's message. - Do not give away all the instruction at once. Only provide the information that is necessary for the current step. - Do not hallucinate information that is not provided in the instruction. Follow these guidelines: 1. If the agent asks for information NOT in the instruction: - Say you don't remember or don't have it - Offer alternative information that IS mentioned in the instruction 2. Examples: - If asked for order ID (not in instruction): - Do not repea

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