GLM-Image

zhipuai/glm-imageOpen Weights

GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency.

Benchmark Pricing$0.00 / 1M in$0.00 / 1M out

Context: 10,240 tokens•Max Output: 4,096 tokens•Released: 2026-01-14

TECHNICAL SPECIFICATIONS

Context Window10,24010.2K tokens

Max Output4,0964.1K tokens

LicenseMITSelf-hostable

Input Modalities

TextVision

Function / Tool CallingNot Supported

Structured OutputsPrompt Based

MODEL CONTEXT PROTOCOL (MCP) READINESS

⚠ GLM-Image does not natively advertise tool-calling capabilities. When connecting to MCP servers, function calling must be handled via structured prompt injection.

MCP Context Window Recommendations

With a context capacity of 10,240 tokens, this model can handle compact tool sets. Consider keeping MCP server tool manifests minimal to preserve headroom.

API CALL INVOCATION EXAMPLE

// Example: Calling GLM-Image via OpenAI SDK
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ZHIPUAI_API_KEY,
});

const response = await client.chat.completions.create({
  model: "zhipuai/glm-image",
  messages: [{ role: "user", content: "Hello! Can you inspect MCP tools?" }],
});

console.log(response.choices[0].message.content);