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Zhipu AI Releases GLM-4.6: Achieving Enhancements in Real-World Coding, Long-Context Processing, Reasoning, Searching and Agentic AI

Zhipu AI Releases GLM-4.6: Achieving Enhancements in Real-World Coding, Long-Context Processing, Reasoning, Searching and Agentic AI

Zhipu AI has announced the release of GLM-4.6, a significant update to its GLM series. This version focuses on agentic workflows, long-context reasoning, and practical coding tasks, aiming to enhance the user experience in real-world applications.

Key Features of GLM-4.6

  • Context + Output Limits: GLM-4.6 supports a 200K input context and a maximum output of 128K tokens.
  • Real-World Coding Results: On the extended CC-Bench, GLM-4.6 demonstrates near parity with Claude Sonnet 4, achieving a 48.6% win rate while using approximately 15% fewer tokens compared to GLM-4.5.
  • Benchmark Positioning: Zhipu AI reports clear gains over GLM-4.5 across eight public benchmarks while noting that GLM-4.6 still lags behind Claude Sonnet 4.5 on coding tasks.
  • Ecosystem Availability: The model is accessible via Z.ai API and OpenRouter, with integrations into popular coding agents such as Claude Code, Cline, Roo Code, and Kilo Code. Existing Coding Plan users can upgrade simply by switching the model name to glm-4.6.
  • Open Weights + License: The Hugging Face model card indicates the model is licensed under MIT, with a size of 357B parameters (MoE) using BF16/F32 tensors.
  • Local Inference: GLM-4.6 supports local serving through vLLM and SGLang, with weights available on Hugging Face and ModelScope.

Summary

GLM-4.6 represents a material advancement with a 200K context window, a reduction of approximately 15% in token usage on CC-Bench compared to GLM-4.5, and near parity with Claude Sonnet 4 in task completion rates. Immediate availability is ensured through Z.ai, OpenRouter, and open-weight artifacts for local deployment.

FAQs

1. What are the context and output token limits?

GLM-4.6 supports a 200K input context and a maximum output of 128K tokens.

2. Are open weights available and under what license?

Yes. The Hugging Face model card lists open weights under the MIT license and indicates a 357B-parameter MoE configuration using BF16/F32 tensors.

3. How does GLM-4.6 compare to GLM-4.5 and Claude Sonnet 4 on applied tasks?

On the extended CC-Bench, GLM-4.6 shows approximately 15% fewer tokens used compared to GLM-4.5 and achieves near parity with Claude Sonnet 4 (48.6% win-rate).

4. Can I run GLM-4.6 locally?

Yes. Zhipu provides weights on Hugging Face and ModelScope, and local inference is documented with vLLM and SGLang. Community quantizations are emerging for workstation-class hardware.

For more information, please visit the official Zhipu AI blog.