200K
0.78
2.86
Chat
Active

GLM-4.6 | Zhipu AI

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 | Zhipu AITechflow Logo - Techflow X Webflow Template

GLM-4.6 | Zhipu AI

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
  • Supported Programming Languages: Python, JavaScript, Java (for coding tasks)

Performance Benchmarks

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

Code Sample

Comparison with Other Models

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.

API Integration

Accessible via AI/ML API. Documentation: available here.

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
  • Supported Programming Languages: Python, JavaScript, Java (for coding tasks)

Performance Benchmarks

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

Code Sample

Comparison with Other Models

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.

API Integration

Accessible via AI/ML API. Documentation: available here.

Try it now

400+ AI Models

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

The Best Growth Choice
for Enterprise

Get API Key
Testimonials

Our Clients' Voices