Zhipu AI has released GLM-4.6, a major update to its GLM series focused on agentic workflows, long-context reasoning, and practical coding tasks. The model raises the input window to 200K tokens with a 128K max output, targets lower token consumption in applied tasks, and ships with open weights for local deployment.
So, what’s exactly is new?
- Context + output limits: 200K input context and 128K maximum output tokens.
- Real-world coding results: On the extended CC-Bench (multi-turn tasks run by human evaluators in isolated Docker environments), GLM-4.6 is reported near parity with Claude Sonnet 4 (48.6% win rate) and uses ~15% fewer tokens vs. GLM-4.5 to finish tasks. Task prompts and agent trajectories are published for inspection.
- Benchmark positioning: Zhipu summarizes “clear gains” over GLM-4.5 across eight public benchmarks and states parity with Claude Sonnet 4/4.6 on several; it also notes GLM-4.6 still lags Sonnet 4.5 on coding—a useful caveat for model selection.
- Ecosystem availability: GLM-4.6 is available via Z.ai API and OpenRouter; it integrates with popular coding agents (Claude Code, Cline, Roo Code, Kilo Code), and existing Coding Plan users can upgrade by switching the model name to
glm-4.6. - Open weights + license: Hugging Face model card lists License: MIT and Model size: 355B params (MoE) with BF16/F32 tensors. (MoE “total parameters” are not equal to active parameters per token; no active-params figure is stated for 4.6 on the card.)
- Local inference: vLLM and SGLang are supported for local serving; weights are on Hugging Face and ModelScope.
Summary
GLM-4.6 is an incremental but material step: a 200K context window, ~15% token reduction on CC-Bench versus GLM-4.5, near-parity task win-rate with Claude Sonnet 4, and immediate availability via Z.ai, OpenRouter, and open-weight artifacts for local serving.
FAQs
1) What are the context and output token limits?
GLM-4.6 supports a 200K input context and 128K maximum output tokens.
2) Are open weights available and under what license?
Yes. The Hugging Face model card lists open weights with License: MIT and a 357B-parameter MoE configuration (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 reports ~15% fewer tokens vs. GLM-4.5 and 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/ModelScope and documents local inference with vLLM and SGLang; community quantizations are appearing for workstation-class hardware.
Check out the GitHub Page, Hugging Face Model Card and Technical details. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
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