Text-heavy marketing graphics
Generate posters, social cards, and promotional layouts that require multiple pieces of legible text.
Independent tool overview
GLM-Image at a glance
Z.AI's open-weight image-generation model, designed for dense text, knowledge-heavy graphics, high-resolution generation, and instruction-based image editing.
Visit the official GLM-Image site ↗
- Developer
- Z.AI
- Tasks
- Text-to-image and image-to-image
- Architecture
- 9B autoregressive model + 7B diffusion decoder
- Resolution
- 512–2048 px per side; multiples of 32
- Managed API
- $0.015 per image
- Languages emphasized
- English and Chinese text rendering
Overview
What GLM-Image is
GLM-Image is Z.AI's flagship open image model for text-to-image and image-to-image work. Its main differentiator is text-heavy composition: posters, presentation graphics, science explainers, multi-panel layouts, and social assets that need words embedded in the image.
The model combines a 9-billion-parameter autoregressive generator with a 7-billion-parameter diffusion decoder. The first component handles instructions and overall composition; the decoder restores image detail and uses a dedicated Glyph Encoder to improve rendered text.
Developers can self-host the published weights through Transformers and Diffusers, serve an OpenAI-style image endpoint with SGLang, or use Z.AI's managed API. Self-hosting gives more control, but the reference implementation remains computationally heavy.
Use cases
Who GLM-Image is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Explainers and diagrams
Create visually dense educational or scientific graphics with labels, steps, and structured information.
Image editing
Apply prompt-driven changes, style transfer, identity-preserving edits, and multi-subject composition from reference images.
Self-hosted image workflows
Run published weights inside a controlled environment when infrastructure, privacy, or model customization justifies the hardware cost.
Image-generation APIs
Add relatively inexpensive text-to-image generation through Z.AI's managed endpoint without operating the model locally.
Capabilities
Core GLM-Image features
Dense text rendering
A Glyph Encoder and targeted post-training focus on spelling, multi-line text, and multiple text regions inside one image.
Knowledge-intensive composition
Targets diagrams, posters, slide-like layouts, and other scenes where semantic structure matters as much as aesthetics.
Unified generation and editing
Supports text-to-image, image editing, style transfer, identity preservation, and multi-subject consistency in one model.
High-resolution output
Supports common aspect ratios and custom dimensions from 512 to 2048 pixels per side, with each dimension divisible by 32.
Open local pipeline
Official examples cover Transformers, Diffusers, and SGLang-based deployment.
Managed Z.AI API
Provides an image-generation endpoint that returns a URL for the generated asset.
Process
How the GLM-Image workflow works
Step 1
Choose managed or local
Use the Z.AI API for simple per-image billing, or download the model when deployment control outweighs infrastructure complexity.
Step 2
Write a structured prompt
Describe the layout, subject, style, hierarchy, and exact wording; the official guidance recommends putting intended rendered text in quotation marks.
Step 3
Set valid dimensions
Choose a supported aspect ratio or custom width and height between 512 and 2048 pixels, each divisible by 32.
Step 4
Generate and inspect
Review spelling, logos, layout, subject identity, and factual diagram content before using the image.
Step 5
Iterate or edit
Refine the prompt or supply one or more reference images for targeted changes and consistency.
Cost
GLM-Image pricing and free plan
The downloadable model does not carry a per-image fee, but self-hosting incurs GPU and engineering costs. Z.AI's official managed endpoint is billed per image.
Open weights
Free to download
Run GLM-Image on your own infrastructure under the applicable repository and model licenses.
- No vendor generation charge
- Compute, storage, bandwidth, and operations are your responsibility
- Review the license files for every distributed component
Z.AI API
$0.015/image
Managed image generation through Z.AI's API.
- Returns an image URL that must be downloaded
- API account and billing required
- Current documentation lists text input and image output
Third-party hosting
Varies
Inference providers may offer the model with their own hardware, queue, and billing terms.
- Not priced or operated by Z.AI
- Compare resolution, latency, retention, and commercial terms
Pricing checked . Check current pricing at the source ↗
Assessment
GLM-Image strengths and limitations
Where it stands out
- Purpose-built for images containing substantial English or Chinese text
- Handles generation and multiple image-editing tasks in one model
- Offers both downloadable weights and a low-cost managed API
- Supports custom dimensions up to 2048 pixels per side
- Official integrations include common open-model Python libraries and SGLang serving
What to consider
- Local inference is resource-intensive; Z.AI's reference H100 measurements show roughly 38 GB peak VRAM and about 64 seconds for a single 1024×1024 text-to-image result
- Even strong text-rendering models can misspell words or distort logos, so every asset needs visual review
- The vendor-reported benchmark results are useful context but are not a substitute for testing on your own prompts
- The official GitHub repository and Hugging Face model card display different top-level license labels, so legal teams should inspect the exact files and included components before production use
- API output is delivered by URL, requiring an additional download and storage step
- The managed API documentation currently describes text input; use the local model when image-to-image support is required and confirm current API capability first
Compare
GLM-Image alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Design
Qwen Image 2.0
Another open image family with strong text rendering, unified editing, and native high-resolution output.
Explore Qwen Image 2.0 →Content Creator
Z-Image
A competing open model family for teams comparing quality, speed, and local deployment requirements.
Explore Z-Image →Design
Ideogram
A polished hosted product for typography-focused design without running model infrastructure.
Explore Ideogram →Design
Ideogram 4.0
Consider this when evaluating newer open-weight image models with a strong design and text focus.
Explore Ideogram 4.0 →Questions
GLM-Image FAQs
What is GLM-Image?
GLM-Image is Z.AI's image-generation model for text-to-image and image-to-image tasks, with an emphasis on dense text, structured layouts, and knowledge-heavy graphics.
Is GLM-Image open source?
Z.AI publishes the model weights, implementation, and license files. The official GitHub repository labels the project Apache-2.0, while the Hugging Face model card labels the overall model MIT and notes Apache-2.0 components. Review the exact distribution and component licenses for your use case.
How much does the GLM-Image API cost?
Z.AI's current developer documentation lists the managed API at $0.015 per generated image.
Can GLM-Image edit existing images?
Yes in the downloadable model. Official examples cover prompt-based editing, and the model card describes style transfer, identity-preserving generation, and multi-subject consistency.
What resolutions does GLM-Image support?
Z.AI lists common 1:1, 3:4, 4:3, and 16:9 sizes. Custom width and height must each be between 512 and 2048 pixels and divisible by 32.
How much GPU memory does local GLM-Image need?
It is a heavy model. The repository reports about 37.8 GB peak VRAM for one 1024×1024 generation in its H100 Diffusers test, while the model card describes a slower CPU-offload path around 23 GB. Real requirements depend on resolution, batch size, software, and optimization.
Bottom line
Our GLM-Image verdict
GLM-Image is most compelling when accurate text and structured information inside an image are central requirements. The managed API removes its substantial local compute burden, while the published weights remain valuable for teams that need deployment control and can validate the licensing and hardware tradeoffs.
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