GPT Image 1.5 is OpenAI's image generation and editing model. It can create new visuals from text and make targeted modifications to existing images. It is especially suited for tasks that require preserving a person's appearance, product features, brand logos, and original composition, while enhancing complex instruction following and dense text rendering to help marketing, e-commerce, and design teams continuously refine the same set of visual assets.
Clarify capacity, input and output, and calling methods before choosing a model.
Image creation and detail preservation are model capabilities; the number of reference images, file formats, and result formats are calling specifications for this platform endpoint.
Learn what gpt-image-1.5 can bring to your work.
GPT Image 1.5 excels at adding, removing, combining, or replacing elements in existing images while preserving lighting, composition, and a person's appearance as much as possible. When modifying clothing, backgrounds, or product colors, clearly state what must remain unchanged so the edit focuses on the objective rather than redesigning the entire image.
For creative requests involving multiple objects, explicit positional relationships, or segmented layouts, the model places greater emphasis on relationships between elements. Describe the subjects, spatial positions, visual hierarchy, and style separately for advertising concept images, composite visuals, and informational posters; complex tasks should still be checked to ensure object counts and positions are accurate.
The model improves the rendering of smaller, denser text and better preserves brand marks and key visuals during editing. It is suitable for developing headlines, product images, and layouts together, reducing drafts where text feels disconnected from the image. Before formal publication, proofread all text and check whether logo details have changed.
Start with specific tasks to find where the model can be effective.
Provide a product image and a description of the target scene, requesting that its shape, colors, and branding be preserved while changing only the environment, props, or display angle. You can create candidate images for product catalogs, holiday scenes, and presentation assets in different styles, then have the team select among them; compare product details against the actual item one by one to avoid treating generated changes as real specifications.
Provide the campaign theme, headline copy, brand images, and layout requirements, and have the model generate text-containing advertising visuals or modify the backgrounds and decorative elements of existing posters. Deliverables can serve as social media assets, campaign visual drafts, and design proposals, then be reviewed for text accuracy, information hierarchy, and brand consistency.
Using a portrait photo as a basis, describe changes to clothing, hairstyle, environment, or artistic style, and explicitly request that facial features and pose be preserved. It is suitable for creating styling proposals, character visual drafts, and candidate creative portraits. Keep the original image for comparison during successive edits to help identify shifts in appearance or local details.
Choose based on task complexity, input materials, and expected results.
If the task focuses on precise modifications to existing images, preserving brand elements, or visual creation containing substantial text, GPT Image 1.5 is more worth prioritizing for testing than GPT Image 1. Its improvements focus on instruction following, image preservation, and editing quality; if the existing workflow already meets your needs, compare results using the same set of materials before deciding whether to make adjustments.
If you do not yet have visual materials and want to explore the overall direction, choose text-to-image generation; if the product, person, or composition has already been determined and only local content needs to be changed, choose image editing. For detail-sensitive tasks, try the high input fidelity setting, and write the items to modify and the items to preserve separately to avoid interpreting high fidelity as pixel-level immutability.
From a small-scale task to full integration.
Clarify the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to review the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Understand output quality and capability boundaries before formal use.
Answers to common questions about using gpt-image-1.5.
Submit model=gpt-image-1.5 and prompt to /openai/images/generations. The prompt should describe the subject, background, lighting, layout, and any text that needs to appear; when there are no source-image constraints, this approach is better suited for exploring the complete image and visual direction.
Submit image, prompt, and explicitly specify model=gpt-image-1.5 to /openai/images/edits. image can use a single image URL or an array of URLs; instructions should separately state the elements that need to change and the parts that must be preserved, such as people, products, and composition.
The editing endpoint allows up to 16 image URLs. You can provide references for people, products, or environments, and explain in the prompt the role of each image, which elements need to be combined, and which cannot be changed. When there are many reference images, avoid conflicting style and appearance requirements.
You can try creating posters with Chinese titles and copy. The model has improved dense and small-font text rendering, but multilingual performance still has limitations. Clearly provide the original text, text placement, and hierarchy, then check every character after generation; long body text or strict brand fonts should be typeset separately.
You can choose url to obtain an image address, or b64_json to obtain encoded image data, and select PNG, JPEG, or WebP according to your use case. Responses may include created/data or may return task_id; applications should handle completed images and task responses separately, and should not treat a task ID as an image result.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Start with a clear goal and evaluate whether it suits your work based on real results.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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