GLM 5 is a fifth-generation frontier large language model developed by Tsinghua University's team. It features approximately 745 billion total parameters with a Mixture-of-Experts (MoE) architecture that activates around 44 billion parameters per inference, achieving state-of-the-art results in reasoning, coding, creative writing, and agentic AI tasks.

What context length does GLM 5 support?

GLM 5 supports a 128K token context window, allowing it to process lengthy documents, maintain long conversations, and manage complex agent workflows without losing earlier context. This capacity enables handling entire codebases or research papers in a single input.

Can GLM 5 be used as an AI agent?

Yes, GLM 5 is designed for agentic AI applications, supporting tool use, function calling, multi-turn planning, and self-correction. These capabilities allow it to execute autonomous multi-step tasks such as data analysis, code debugging, and workflow automation.

Does GLM 5 support image generation?

Yes, the GLM 5 ecosystem incorporates SEEDREAM 5.0, a model for generating photorealistic 2K images from text prompts. This includes text-to-image generation, image editing, and multi-subject composition, accessible through the platform's image generation features.

Can I use GLM 5 for commercial projects?

Yes, GLM 5 permits commercial use of generated content across all paid subscription plans. The licensing terms are included with each plan, allowing businesses and creators to utilize outputs for products, services, and marketing materials without restriction.

How does the Mixture-of-Experts architecture in GLM 5 improve efficiency?

The MoE architecture activates only a subset of experts per layer—8 out of 256—during inference, with ~44B active parameters out of 745B total. This sparsity reduces computational costs and memory usage while maintaining high performance, making GLM 5 more efficient than dense models of similar scale.

What programming languages and coding tasks is GLM 5 optimized for?

GLM 5 excels in code generation across over 50 programming languages, with top-tier performance on benchmarks like HumanEval and BigCodeBench. It handles tasks including code generation, debugging, refactoring, and infrastructure-as-code for tools like Terraform and Kubernetes, making it suitable for diverse development environments.

On which benchmarks does GLM 5 achieve state-of-the-art performance?

GLM 5 achieves SOTA results on multiple benchmarks: MMLU for multitask knowledge, BBH for complex reasoning, HumanEval for code generation, and AgentBench for agent capabilities. These scores demonstrate its competitive edge against proprietary models in reasoning and coding tasks.

What are the key differences between the Starter, Plus, and Enterprise pricing plans?

The plans differ in annual credit allocation: Starter offers 14,400 credits, Plus provides 24,000, and Enterprise includes 67,200. Higher tiers also feature lower cost per credit, priority or expert support, faster generation speeds, and all include commercial use licenses, catering to different user scales from hobbyists to teams.

What languages does GLM 5 support?

GLM 5 provides native support for English and Chinese, with additional coverage for over 15 other languages. Its multilingual capabilities are particularly strong in cross-lingual tasks, offering consistent performance across diverse linguistic contexts for global applications.

GLM 5 Core Features

Core Features of GLM 5

Advanced Reasoning and Code Generation

Performs multi-step logical reasoning, mathematical problem-solving, and generates, debugs, and refactors code across 50+ programming languages with state-of-the-art benchmark performance.

Autonomous Agent Workflows

Supports deployment of autonomous agents capable of tool integration, function calling, multi-turn planning, and self-correction for complex real-world task automation.

Photorealistic Image Synthesis

Generates high-quality 2K images from text prompts using Seedream 5.0, enabling text-to-image creation, editing, and complex multi-subject composition.

Long-Context Document Processing

Handles entire codebases, research papers, and legal documents in single prompts via 128K token context for comprehensive analysis and understanding.

Use Cases of GLM 5

Developers: Use GLM 5's state-of-the-art code generation and 128K context to audit entire codebases efficiently.
DevOps Engineers: Deploy GLM 5 agents for autonomous CI/CD pipeline debugging and root cause analysis.
Researchers: Process full research papers using the 128K token context window for comprehensive literature reviews.
Global Teams: Leverage GLM 5's multilingual capabilities for accurate Chinese-English cross-lingual document translation.
Game Developers: Generate consistent long-form game dialogue and NPC behavior scripts with agentic narrative control.

GLM 5 Core Features

Core Features of GLM 5

Advanced Reasoning and Code Generation

Autonomous Agent Workflows

Photorealistic Image Synthesis

Long-Context Document Processing

Use Cases of GLM 5

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