glm-4.7-flashGLM-4.7-Flash is a state-of-the-art 30B-class model designed to strike a strong balance between performance and efficiency. It is specifically optimized for agentic coding scenarios, with enhanced capabilities in code generation, long-horizon task planning, and tool-based collaboration. Among open-source models of comparable size, GLM-4.7-Flash has achieved leading results on multiple public benchmark leaderboards, establishing itself as a competitive and practical choice for advanced developer and agent workflows.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="glm-4.7-flash", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="glm-4.7-flash",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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GLM-5.2 is Z.AI's flagship model for long-horizon task execution, designed to handle complex, project-scale workflows with high reliability. Featuring a 1M-token context window, it can maintain and reason over extensive engineering context, enabling consistent execution across large, multi-stage tasks. Optimized for end-to-end software development, GLM-5.2 follows engineering standards reliably and can manage the full workflow from requirements analysis and implementation to testing and multi-platform deployment, making it well suited for advanced coding agents and large-scale autonomous engineering projects.
GLM-5.1 delivers a major advancement in coding capability, with significant improvements in handling long-horizon tasks. It is designed to operate beyond short interactions, enabling continuous, autonomous execution over extended periods. The model can work independently on a single task for 8+ hours, performing planning, execution, and iterative self-improvement to produce complete, engineering-grade results, making it well suited for complex development workflows and autonomous agent systems.
GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems.
GLM-5 Turbo is a high-performance model from Z.ai optimized for fast inference and agent-driven workflows. Designed for real-world environments such as OpenClaw scenarios, it delivers strong performance across long execution chains and complex task pipelines. The model features improved instruction decomposition, tool integration, scheduled and persistent execution, and enhanced stability for extended multi-step tasks, making it well suited for autonomous agents and production automation workflows.
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GLM-5.2 is Z.AI's flagship model for long-horizon task execution, designed to handle complex, project-scale workflows with high reliability. Featuring a 1M-token context window, it can maintain and reason over extensive engineering context, enabling consistent execution across large, multi-stage tasks. Optimized for end-to-end software development, GLM-5.2 follows engineering standards reliably and can manage the full workflow from requirements analysis and implementation to testing and multi-platform deployment, making it well suited for advanced coding agents and large-scale autonomous engineering projects.
GLM-4.6 improves on GLM-4.5 with a larger 200K context window, stronger coding performance (including better real-world agent tools like Claude Code and Cline), and clearer gains in reasoning with built-in tool use. It delivers more capable agent behavior, integrates better into agent frameworks, and produces more natural, readable writing — especially in role-playing scenarios.
GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems.
The GLM-4.5 series is built for agent-style AI, combining reasoning, coding, and tool use. GLM-4.5 has 355B total parameters (32B active), while GLM-4.5-Air is a lighter 106B/12B version. Both support hybrid modes — a “thinking” mode for complex reasoning and tools, and a fast non-thinking mode for quick replies. The models are open-sourced (including FP8 versions) under the MIT license for commercial use, and they rank highly on benchmarks: GLM-4.5 scores 63.2 (3rd overall), while GLM-4.5-Air achieves 59.8 with better efficiency.
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