Today, we are releasing the latest version of our flagship model: GLM-4.6. Compared with GLM-4.5, this generation brings several key improvements:

• Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
• Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
• Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
• More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
• Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

We evaluated GLM-4.6 across eight public benchmarks covering agents, reasoning, and coding. Results show clear gains over GLM-4.5, with GLM-4.6 also holding competitive advantages over leading domestic and international models such as DeepSeek-V3.2-Exp and Claude Sonnet 4, but still lags behind Claude Sonnet 4.5 in coding ability.

Real-world experience matters more than leaderboards. We extended CC-Bench from GLM-4.5 with more challenging tasks, where human evaluators worked with models inside isolated Docker containers and completed multi-turn real-world tasks across front-end development, tool building, data analysis, testing, and algorithm. GLM-4.6 improves over GLM-4.5 and reaches near parity with Claude Sonnet 4 (48.6% win rate), while clearly outperforming other open-source baselines. From a token-efficiency perspective, GLM-4.6 finishes tasks with about 15% fewer tokens than GLM-4.5, showing improvements in both capability and efficiency. All evaluation details and trajectory data have been made publicly available for further community research: https://huggingface.co/datasets/zai-org/CC-Bench-trajectories

Getting started with GLM-4.6

Call GLM-4.6 API on Z.ai API platform

The Z.ai API platform offers both GLM-4.6 models. For comprehensive API documentation and integration guidelines, please refer to https://docs.z.ai/guides/llm/glm-4.6. Alternatively, developers are welcome to access both models through OpenRouter.

Use GLM-4.6 with Coding Agents

GLM-4.6 is now available to use within coding agents (Claude Code, Kilo Code, Roo Code, Cline and more).

For GLM Coding Plan subscribers: You'll be automatically upgraded to GLM-4.6. If you've previously customized the app configs (like ⁠~/.claude/settings.json in Claude Code), simply update the model name to ⁠"glm-4.6" to complete the upgrade.

For New users: The GLM Coding Plan offers Claude-level performance at a fraction of the cost — just 1/7th the price with 3x the usage quota. Start building today: https://z.ai/subscribe.

Chat with GLM-4.6 on Z.ai

GLM-4.6 is accessible through Z.ai by selecting the GLM-4.6 model option.

Serve GLM-4.6 Locally

Model weights of GLM-4.6 will soon be available at HuggingFace and ModelScope. For local deployment, GLM-4.6 supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official GitHub repository.