What makes ChatGPT Image different
ChatGPT Image is built natively into GPT-4o’s architecture — not a separate model bolted on. This means it draws on GPT-4o’s full knowledge base when composing images: it can accurately render national flags, company logos, scientific diagrams, maps, and UI mockups that most models would get wrong. It’s also the best model for infographics and text-heavy layouts where both visual composition and textual accuracy matter.Capabilities
World-knowledge grounded
Leverages GPT-4o’s full knowledge base — accurately renders logos, flags, scientific diagrams, maps, and other knowledge-dependent visuals.
Best-in-class text rendering
Generates precise, readable text within images — signs, labels, infographic content, and multi-line layouts with correct spelling and placement.
Complex prompt fidelity
Significantly more precise than DALL-E 3. Follows multi-element, multi-constraint prompts with high accuracy.
Multi-reference compositing
Accepts up to 10 reference images for editing — combine subjects, backgrounds, products, and styles in a single generation.
Conversational editing
Refine images through natural chat context — maintains consistency and intent across multiple iterative edits.
Mask-based inpainting
Mask specific regions of an image for targeted edits while keeping the rest of the composition intact.
Specifications
| Feature | Details |
|---|---|
| Model API name | gpt-image-1 |
| Resolutions | 1024×1024 (1:1), 1536×1024 (3:2), 1024×1536 (2:3) |
| Quality tiers | Low, Medium, High |
| Output formats | PNG, JPEG, WebP |
| Transparent background | Yes (PNG and WebP) |
| Max reference images | 10 (for editing workflows) |
| Released | March 25, 2025 |
How to use
Write your prompt
Write a detailed, structured prompt. ChatGPT Image handles complex multi-element instructions well — be specific about all required components.
Prompting tips
- Describe text content precisely — Include exact wording, font style, and placement. Example: “A poster with the title ‘Sale Ends Friday’ in large bold red sans-serif text at the top.”
- Use it for knowledge-dependent visuals — Prompts referencing specific brands, flags, maps, or scientific concepts will produce more accurate results than other models.
- Multi-step editing — Generate a base image, then use follow-up instructions to modify specific elements: “Change the background to a sunset”, “Make the text white”.
- Be explicit with layout — For infographics: “Three-column layout, icons on the left, text on the right of each icon”.
Example prompts
A clean infographic showing the water cycle: evaporation, condensation, precipitation, and collection. Labeled with arrows, minimal design, blue and white color palette.
A product label for “Alpine Spring Water” with mountain imagery, clean typography, and a blue gradient background. Professional, minimal design.
A social media post graphic for a coffee shop: warm brown tones, a latte art photo, text reading “Good Morning, Seattle” in serif font, minimal modern layout.
Compare models
| Model | Text rendering | World knowledge | References | Best for |
|---|---|---|---|---|
| ChatGPT Image | Best-in-class | Yes (GPT-4o) | Up to 10 | Infographics, text-heavy layouts, knowledge-grounded visuals |
| Ideogram v3 | Excellent | No | Up to 3 (style) | Typography, posters, brand design |
| Nano Banana | Strong | No | Up to 4 | E-commerce, product compositing |
| Seedream 4.0 | Strong (multilingual) | No | Up to 6 | Commercial campaigns, multilingual markets |
ChatGPT Image uses GPT-4o’s architecture to ground image generation in world knowledge, making it particularly effective for prompts that reference specific real-world objects, brands, or concepts that other models typically misrepresent.