GLM-5 is an advanced LLM developed to support complex reasoning, long-context understanding, and multilingual text generation. It combines deep semantic comprehension with strong instruction following, enabling developers to build reliable AI assistants, automation workflows, coding copilots, and knowledge systems. The model is optimized for both conversational AI and structured outputs, making it suitable for chat interfaces, data extraction pipelines, agentic systems, and enterprise integrations.
GLM-5 is optimized for stable inference and predictable behavior in production environments. It demonstrates strong instruction adherence, reduced hallucination patterns compared to earlier-generation models, and improved contextual retention in extended conversations.
GLM-5 is built to handle analytical tasks, multi-step reasoning, and structured data generation. It can reliably produce JSON outputs, formatted reports, and schema-aligned responses, making it ideal for backend integrations and automation tools.
GLM-5 supports extended context windows, allowing it to process large documents, maintain conversation continuity, and reason over complex multi-paragraph inputs. This makes it well-suited for research assistants, legal document analysis, and enterprise knowledge management systems.
GLM-5 performs effectively in code completion, debugging assistance, and technical documentation generation. It supports modern programming workflows and can be integrated into developer tools, CI/CD pipelines, and AI-powered IDE assistants.
Create high-quality blog posts, technical documentation, marketing copy, and multilingual content with strong consistency and structure.
Leverage structured output capabilities for data extraction, document parsing, and transformation into machine-readable formats.
Integrate GLM-5 into engineering workflows to accelerate development, improve code quality, and streamline debugging processes.
Use GLM-5 as the reasoning core for AI agents that perform multi-step tasks, decision-making operations, and contextual execution flows.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
GLM-5 is documented as suited to complex systems engineering and long-horizon agentic tasks. It is also described as strong for real-world coding and end-to-end software development tasks.
The model is documented for building agentic applications that use native tool calling, function execution, and long-horizon task planning. It is also suited to office workflows that require stable completion of complex, multi-step tasks across stages.
GLM-5 is documented for roleplay, with support for maintaining character settings consistently over time. It is also documented for text data extraction from complex documents such as contracts, announcements, and financial reports.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "zhipu/glm-5", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "zhipu/glm-5", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"zhipu/glm-5","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Benchmark | Score | What it measures | Source | Retrieved |
|---|---|---|---|---|
| Intelligence | 27.9 | Composite score across standardised reasoning, knowledge and problem-solving evaluations, measured independently by Artificial Analysis | Source | September 12, 2026 |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
GLM-5 This page | Coding + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
GLM-5 has a 200,000 tokens context window and can return up to 128,000 tokens.
GLM-5 takes text as input and returns text.
Use zhipu/glm-5 as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
GLM-5 became available on February 12, 2026.
GLM-5 is priced at input $1.3 / 1M tokens, output $4.16 / 1M tokens, cached input $0.26 / 1M tokens.
Yes, GLM-5 can stream responses as they are generated.
GLM-5 was built by Zhipu AI.
GLM-5 is a 745B parameter mixture-of-experts (MoE) model.
