The model’s efficiency and versatility make it ideal for developers and enterprises aiming to deploy advanced AI applications with economic and performance benefits.
GLM-4.6 represents the cutting edge in large language models from Zhipu AI, balancing expansive context capabilities, efficient token use, and strong reasoning performance.
GLM-4.6 API Overview
GLM-4.6 is an advanced large language model developed by Zhipu AI (now Z.ai), featuring a state-of-the-art 355 billion parameter Mixture of Experts (MoE) architecture. It is optimized for a broad range of tasks including complex reasoning, coding, writing, and multi-turn dialogue with an extended context window of 200,000 tokens. GLM-4.6 demonstrates industry-leading performance, especially in programming and agentic tasks, making it a top choice for developers and enterprises seeking efficiency and versatility.
Technical Specifications
Model Architecture: 355B parameter Mixture of Experts (MoE)
Input Modality: Text
Output Modality: Text
Context Window Size: 200,000 tokens (expanded from 128,000 in GLM-4.5)
Maximum Output Tokens: 128,000 tokens
Efficiency: Approximately 30% more efficient token consumption than previous versions
GLM-4.6 has been rigorously evaluated across authoritative benchmarks demonstrating competitive or superior results against leading models:
Real-world Coding Tests: Outperforms similar domestic models in 74 coding scenarios, showing better code correctness and performance.
Comparative Efficiency: Consumes approximately 30% fewer tokens for equivalent output, reducing costs and resource needs.
Benchmark Results: Comparable to Claude Sonnet 4 and 4.6 on multi-domain NLP benchmarks like AIME, GPQA, LCB v6, and SWE-Bench Verified.
Reasoning and Agent Tasks: Strong performance in decision-making and tool-assisted tasks, often matching or exceeding competitors in benchmark tests.
Contextual Understanding: Expanded context allows superior performance on tasks requiring deep document analysis and complex instructions.
Performance Benchmarks
Key Features and Capabilities
Extended Context Handling: With a massive 200K token window, GLM-4.6 can perform detailed long-form text comprehension, multi-step problem solving, and maintain coherent, prolonged dialogues.
Superior Coding Performance: Outperforms GLM-4.5 and many domestic competitors in 74 practical coding tests within the Claude Code environment. Excels in front-end development, code organization, and autonomous planning.
Advanced Reasoning and Decision Making: Enhanced tool usage capabilities during inference enable better autonomous agent frameworks and search-based task execution.
Natural Language Generation: Produces text with improved alignment to human stylistic preferences, excelling in role-playing, content creation (novels, scripts, ads), and multi-turn conversations.
Key Features and Capabilities
GLM-4.6 API Pricing
Input: $0.63
Output: $2.31
Cached: $0.1155
Use Cases
Long-context document analysis and summarization
Complex multi-step reasoning and problem solving
Real-world programming and code generation in multiple languages
Natural language content creation including creative writing and scripts
Chatbots with sustained, coherent multi-turn conversations
Agentic systems with tool use and autonomous decision making
Vs. GLM-4.5: GLM-4.6 offers noticeable improvements in code generation accuracy and maintains a consistent edge in handling ultra-long context inputs, while retaining strong agentic task performance close to GLM-4.5.
Vs. OpenAI GPT-4.5: GLM-4.6 narrows the gap in reasoning and multi-step task accuracy, leveraging its much larger context window; however, GPT-4.5 still leads in raw task precision on some standardized benchmarks.
Vs. Claude 4 Sonnet: While Claude 4 Sonnet excels in coding and multi-agent efficiency, GLM-4.6 matches or surpasses it in agentic reasoning and long-document comprehension, making it stronger for extended-context applications.
Vs. Gemini 2.5 Pro: GLM-4.6 balances advanced reasoning and coding capabilities with enhanced long-form document understanding, whereas Gemini 2.5 Pro is more focused on optimizing individual coding and reasoning benchmarks.
.png){kind=small}
.png){kind=small}
-p-130x130q80.png)
