This is a simplified guide to an AI model called GLM-4.7-Flash maintained by zai-org. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called GLM-4.7-Flash GLM-4.7-Flash maintained by zai-org zai-org . If you like these kinds of analysis, join AIModels.fyi AIModels.fyi or follow us on Twitter Twitter . Model overview GLM-4.7-Flash is a 30-billion parameter mixture-of-experts model that delivers strong performance in the lightweight deployment category. Maintained by zai-org, this model represents a balance between computational efficiency and reasoning capability. Compared to GLM-4.7, which offers broader capabilities across coding and reasoning tasks, GLM-4.7-Flash optimizes for faster inference while maintaining competitive benchmark performance. For teams requiring multimodal understanding, GLM-4.6V-Flash provides vision capabilities in a similarly lightweight form factor. GLM-4.7-Flash zai-org GLM-4.7 GLM-4.7-Flash GLM-4.6V-Flash Model inputs and outputs GLM-4.7-Flash accepts text-based prompts and conversations, processing them through a chat interface compatible with standard transformer formats. The model generates coherent text responses suitable for reasoning, coding tasks, and tool usage scenarios. GLM-4.7-Flash Inputs Text prompts and conversations formatted as messages with user and assistant rolesFlexible context supporting extended reasoning tasks and multi-turn dialogue Text prompts and conversations formatted as messages with user and assistant roles Text prompts and conversations Flexible context supporting extended reasoning tasks and multi-turn dialogue Flexible context Outputs Generated text responses with support for function calling and tool interactionStructured reasoning output capable of handling complex problem-solving scenarios Generated text responses with support for function calling and tool interaction Generated text responses Structured reasoning output capable of handling complex problem-solving scenarios Structured reasoning output Capabilities This model excels at mathematical problem-solving, achieving 91.6% on the AIME 2025 benchmark. It demonstrates strength in specialized knowledge tasks with 75.2% performance on GPQA, and handles software engineering challenges effectively, scoring 59.2% on SWE-bench Verified. The model supports native tool calling and reasoning modes, enabling it to break down complex tasks and interact with external systems. What can I use it for? Organizations can deploy GLM-4.7-Flash locally for rapid-response applications where latency matters. Educational platforms might use it for coding tutoring and mathematical problem explanation. Development teams can integrate it via vLLM or SGLang for production systems requiring both performance and efficiency. The model's tool-calling capabilities make it suitable for building autonomous agents that perform web browsing, code execution, and complex multi-step workflows. Companies seeking cost-effective inference can leverage this model's mixture-of-experts architecture to reduce computational overhead while maintaining quality output. GLM-4.7-Flash Things to try Experiment with the model's thinking mode to observe how it approaches complex mathematical problems step-by-step before producing answers. Test its software engineering abilities by asking it to solve repository-level issues or generate production-quality code. Try using it as a reasoning engine within agent frameworks where it must select appropriate tools and execute them in sequence. Compare its performance on your specific use cases against the broader GLM-4.7 or GLM-4.5 models to determine the efficiency gains in your infrastructure. GLM-4.7 GLM-4.5

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