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.
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.
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.
Inputs
- Text prompts and conversations formatted as messages with user and assistant roles
- Flexible context supporting extended reasoning tasks and multi-turn dialogue
Outputs
- Generated text responses with support for function calling and tool interaction
- Structured reasoning output capable of handling complex problem-solving scenarios
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.
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.