# GLM-Image Usage, Cost & Rank | OpenCode Data

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.

- Page: https://opencode.ai/data/zhipuai/glm-image
- JSON: https://opencode.ai/data/zhipuai/glm-image.json
- Model ID: zhipuai/glm-image
- Lab: Zhipu

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 10K |
| Max output | 0 |
| Knowledge cutoff | - |
| Release date | 2026-01-19 |
| Input modalities | text, image |
| Output modalities | image |
| Reasoning | No |
| Tool calling | No |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/zai-org/GLM-Image) |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.