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Model details

Z-AI/GLM 4.6

GLM-4.6 is positioned as Z AI's flagship model, designed specifically to advance agentic and coding performance. It represents a generational step beyond GLM-4.5, with a substantially expanded context window that allows the model to hold far more information during a single conversation or task. The architecture has been tuned for reasoning depth, tool-use capabilities, and the kinds of complex, multi-step workflows that modern AI applications demand. Its design reflects a focus on practical utility—enabling developers and AI systems to reason across longer documents, manage more intricate coding tasks, and maintain coherence over extended interactions.

Training and evaluation evidence highlights measurable gains over its predecessor across reasoning, agent behavior, and coding benchmarks, reaching near parity with Claude Sonnet 4 in practical tasks while outperforming other open-source baselines. The model also demonstrates approximately 15% higher token efficiency compared to GLM-4.5, meaning it achieves more output per token consumed. GLM-4.6 is accessible across the Z.ai API platform, OpenRouter, and several prominent coding agents including Claude Code, Roo Code, Cline, and Kilo Code, with plans to release downloadable weights on HuggingFace and ModelScope. This broad availability positions it as a practical choice for teams building agentic pipelines, automated coding assistants, and long-context reasoning applications.

Qiniuz-ai/glm-4.6

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Provider
Qiniu
Model key
z-ai/glm-4.6
Release date
Oct 11, 2025
Last updated
Oct 11, 2025
Input modalities
Output modalities
Capabilities

Limits

Output tokens
200,000 tokens
Context window
200,000 tokens

Latest news about Z-AI/GLM 4.6

Qiniu

CoverageBenchmark

Z.ai's GLM 4.6 is documented in the Kilo Code coding-agent context as a text-input model released September 30, 2025, with a context window of 204,800 tokens and a maximum output of 131,072 tokens. Compared with GLM-4.5, the generation brings several key improvements: a longer context window expanded from 128K to 200K, Kilo Code's leaderboard surfaces independent coding-focused benchmark metrics for the model: an Artificial Analysis Coding Index of 45.8%, LiveCodeBench at 69.5%, TerminalBench Hard at 25.0%, long-context reasoning (LCR) at 54.0%, and IFBench instruction-following accuracy at 43.4%. Coding features include function cal

Videos about Z-AI/GLM 4.6