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Model details

GLM 4.6

GLM 4.6 is positioned as a flagship foundation model from Zhipu AI, purpose-built for agentic workflows, multi-step reasoning, and code generation. The model draws from a large-scale architecture with hundreds of billions of parameters, using a technique that activates a smaller subset during inference to balance capability with efficiency. This design enables strong performance across coding tasks, research-oriented question answering, and complex agentic scenarios where the model must plan, use tools, and maintain context over extended interactions.

The development lineage reflects a series of incremental improvements, with GLM 4.6 delivering roughly 15% better token efficiency compared to its predecessor. It has earned recognition as the top-performing domestic model in China, positioning it as a competitive alternative to major Western frontier models in enterprise and developer contexts. The architecture and training approach are optimized for generating long-form outputs in a single pass, making it well-suited for tasks like writing entire code modules, producing detailed documentation, or analyzing large documents without chunking. For developers building autonomous agents or pipelines that require reliable function calling and extended reasoning chains, GLM 4.6 offers a proven foundation backed by real-world benchmark validation.

IO.NETzai-org/GLM-4.6glm

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Provider
IO.NET
Model key
zai-org/GLM-4.6
Release date
Nov 15, 2024
Last updated
Nov 15, 2024
Knowledge cutoff
2024-10
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.40
Output token cost
$1.75

Limits

Output tokens
4,096 tokens
Context window
200,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about GLM 4.6

IO.NET

CoverageAnalysis

Cirra AI published a detailed technical analysis of GLM-4.6's tool calling and MCP integration capabilities. The article describes GLM-4.6 as Zhipu AI's flagship mixture-of-experts language model with a 200K-token context window, reasoning-capable "thinking mode," and native support for structured function/tool calls. The analysis highlights GLM-4.6's architectural emphasis on chain-of-thought planning and reliability in function calls, including double-checking arguments and rejecting unknown tools. GLM-4.6 is designed to autonomously decide when to invoke external tools such as web search, calculators, or code execution during inf

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