GLM Coding Plan Integrations

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Goose is an open-source, on-machine AI agent designed to automate engineering tasks directly within your terminal or integrated development environment (IDE). Operating locally, it efficiently executes tasks such as code generation, debugging, and deployment, allowing developers to focus on higher-level problem-solving. Goose's extensible architecture enables customization with preferred large language models (LLMs) and integration with external APIs, enhancing its capabilities to suit diverse project requirements. By autonomously handling complex tasks, Goose streamlines the development process, increasing productivity and reducing manual effort. Developers have praised Goose for its ability to manage tasks like updating dependencies, running tests, and automating code migrations, highlighting its effectiveness in real-world applications.

Claude Code is an AI-powered coding assistant designed to work directly inside your existing development environment. It goes beyond simple autocomplete by understanding entire codebases and helping developers build, debug, refactor, and ship features faster. Developers can interact with Claude Code from the terminal, IDEs, Slack, or the web, making it easy to stay in flow without switching tools. By describing tasks in natural language, users can let Claude handle code exploration, modifications, and explanations. Claude Code can analyze project structure, dependencies, and architecture to onboard developers quickly. It integrates with common command-line tools, version control systems, and testing workflows. This makes it a powerful companion for both individual developers and teams working on complex software projects.

OpenCode is the AI coding agent purpose-built for the terminal. It delivers a responsive, themeable terminal UI that feels native while streamlining your workflow. With LSP auto-loading, it ensures the right language servers are always available for accurate, context-aware coding support. Developers can spin up multiple AI agents in parallel sessions on the same project, maximizing productivity. Shareable links make it easy to reference, debug, or collaborate across sessions. Supporting Claude Pro and 75+ LLM providers via Models.dev, OpenCode gives you full freedom to choose your coding companion.

Autonomous coding agent right in your IDE, capable of creating/editing files, running commands, using the browser, and more with your permission every step of the way. Cline can handle complex software development tasks step-by-step. With tools that let him create & edit files, explore large projects, use the browser, and execute terminal commands (after you grant permission), he can assist you in ways that go beyond code completion or tech support. While autonomous AI scripts traditionally run in sandboxed environments, this extension provides a human-in-the-loop GUI to approve every file change and terminal command, providing a safe and accessible way to explore the potential of agentic AI.

Crush is a glamorous AI coding agent that lives right in your terminal, seamlessly connecting your tools, code, and workflows with any Large Language Model (LLM) of your choice. It offers multi-model flexibility, letting you choose from a variety of LLMs or add your own using OpenAI or Anthropic-compatible APIs, and supports mid-session switching between them while preserving context. Crush is session-based, enabling multiple project-specific contexts to coexist. Powered by Language Server Protocol (LSP) enhancements, it incorporates coding-aware context just like a developer’s editor. It's highly extensible via Model Context Protocol (MCP) plugins using HTTP, stdio, or SSE for added capabilities. Crush runs anywhere, leveraging Charm’s sleek Bubble Tea-based TUI for a polished terminal user experience. Written in Go and MIT-licensed (with FSL-1.1 for trademarks), enabling developers to stay in their terminal while taking advantage of expressive AI coding assistance.

GLM-4.6 advances upon its predecessor with stronger reasoning, coding, and agentic capabilities: it demonstrates clear improvements in inferential performance, supports tool use during inference, and more effectively integrates into agent frameworks. In benchmark tests spanning reasoning, coding, and agents, GLM-4.6 outperforms GLM-4.5 and shows competitive strength against models such as DeepSeek-V3.2-Exp and Claude Sonnet 4, though it still trails Claude Sonnet 4.5 in pure coding performance. In real-world tests using an extended “CC-Bench” suite across front-end development, tool building, data analysis, and algorithmic tasks, GLM-4.6 beats GLM-4.5 and approaches parity with Claude Sonnet 4, winning ~48.6% of head-to-head comparisons, while also achieving ~15% better token efficiency. GLM-4.6 is available via the Z.ai API, and developers can integrate it as an LLM backend or agent core using the platform’s API.

GLM-4.7 is an advanced large language model designed to significantly elevate coding, reasoning, and agentic task performance. It delivers major improvements over GLM-4.6 in multilingual coding, terminal-based tasks, and real-world software engineering benchmarks such as SWE-bench and Terminal Bench. GLM-4.7 supports “thinking before acting,” enabling more stable, accurate, and controllable behavior in complex coding and agent workflows. The model also introduces strong gains in UI and frontend generation, producing cleaner webpages, better layouts, and more polished slides. Enhanced tool-using capabilities allow GLM-4.7 to perform more effectively in web browsing, automation, and agent benchmarks. Its reasoning and mathematical performance has improved substantially, showing strong results on advanced evaluation suites. GLM-4.7 is available via Z.ai, API platforms, coding agents, and local deployment for flexible adoption.

GLM-5 is Z.ai’s latest large language model built for complex systems engineering and long-horizon agentic tasks. It scales significantly beyond GLM-4.5, increasing total parameters and training data while integrating DeepSeek Sparse Attention to reduce deployment costs without sacrificing long-context capacity. The model combines enhanced pre-training with a new asynchronous reinforcement learning infrastructure called slime, improving training efficiency and post-training refinement. GLM-5 achieves best-in-class performance among open-source models across reasoning, coding, and agent benchmarks, narrowing the gap with leading frontier models. It ranks highly on evaluations such as Vending Bench 2, demonstrating strong long-term planning and operational capabilities. The model is open-sourced under the MIT License.

GLM-5-Turbo is a high-speed variant of Z.ai’s GLM-5 model, designed to deliver efficient and stable performance in agent-driven environments while maintaining strong reasoning and coding capabilities. It is optimized for high-throughput workloads, particularly long-chain agent tasks where multiple steps, tools, and decisions must be executed in sequence with reliability and low latency. It supports advanced agentic workflows, enabling systems to perform multi-step planning, tool calling, and task execution with improved responsiveness compared to larger flagship models. GLM-5-Turbo inherits core capabilities from the GLM-5 family, including strong reasoning, coding performance, and support for long-context processing, while focusing on optimization of core requirements such as speed, efficiency, and stability in production environments. It is designed to integrate with agent frameworks like OpenClaw, where it can coordinate actions, process inputs, and execute tasks.