gpt-4o-image is a conversational compatibility endpoint for GPT-4o image creation, suitable for bringing text requirements, reference photos, and image modification intent into the same workflow. It supports text-to-image generation, reference image transformation, and multi-image composition. Its strengths include understanding detailed creative instructions, generating photorealistic images, and incorporating text into images, making it suitable for design drafts, marketing visuals, and stylized character creation.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Native capability descriptions correspond to GPT-4o image generation; actual request structures and result retrieval methods depend on the selected conversational endpoint.
Learn what gpt-4o-image can bring to your work.
Beyond describing the subject, you can give the model the composition, lighting, colors, and text that needs to appear. GPT-4o image capabilities are good at understanding detailed instructions, making them suitable for trying images composed of both titles and visual elements. When prompting, separate the copy that must appear from the parts open to creative interpretation, making it easier to check whether the generated image matches the design intent.
Submit a photo together with modification requirements to transform an existing image instead of relying entirely on text to describe it again. Typical operations include changing a real person's photo into an anime style and adding visual elements such as hats. Clearly specify what needs to be preserved and what needs to change for more targeted creation.
A single message can include multiple reference images, with text explaining their relationship in the new image. For example, provide a person image and a coffee image, then request an image of a young man holding up a cup and preparing to drink. It is suitable for combining different visual materials according to creative intent rather than simply stitching multiple images together; you still need to check details of the person, props, and actions.
Start with specific tasks to find where the model can be effective.
Enter the campaign theme, target audience, main headline, and visual mood, then request a visual concept draft with copy. You can first focus on exploring the subject and background, then add requirements for text placement and visual hierarchy. The deliverable is suitable as material for design discussions; before formal release, check the copy, brand elements, and layout details.
Provide a person's photo and describe the desired anime style, accessories, and background to generate an avatar or character visual draft. Clearly state preservation requirements for details such as hairstyle and clothing, while indicating which parts may change. This is suitable for tasks that need to create based on a reference appearance, but the result should not be treated as an exact copy with identity features completely unchanged.
Input separate reference images for the person and props, then describe the action, viewpoint, and scene, such as a person holding coffee and preparing to drink. The generated result can be used for content illustrations or scene proposals. When submitting, explain what information each image provides; during review, focus on the appearance of props, hand movements, and spatial relationships between subjects.
Choose based on task complexity, input materials, and expected results.
When the end goal is to generate or transform images, choose the image-and-text creation entry point for gpt-4o-image. If the focus is only on understanding and answering questions about images, select a model for visual conversation tasks instead; there is no need to make image generation the default workflow. Both can use similar message formats, but the request should clearly state the goal of generating an image to avoid unclear creative intent.
Compared with the earlier DALL·E 3, GPT-4o image capabilities place greater emphasis on detailed instructions, image transformation, and text rendering within images. gpt-4o-image is suitable for organizing image-and-text creation through conversational messages; if an application is already built around dedicated image generation or editing APIs, choose the corresponding GPT Image model instead, and do not directly interchange the two call IDs or their parameters.
From a small-scale task to production integration.
Define 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 full model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Understand output quality and capability scope before production use.
Answers to common questions about using gpt-4o-image.
Here, gpt-4o-image is a compatible entry point for conversational image creation. It is used to organize GPT-4o image generation workflows and should not be treated as another standalone official model or confused with gpt-image-1. It is mainly selected to submit requirements through conversational messages and receive creation results.
Yes. For text-to-image generation, simply clearly describe the image you want, such as a sunset scene in a futuristic city. It is recommended to specify the subject, environment, style, and lighting, and directly state that you want an image generated; if the image needs text, list the exact copy separately for easier subsequent review.
Combine text blocks and multiple image_url blocks in the same user message in Chat Completions, describing the character, props, and action relationship. For example, request that a character hold a reference coffee cup and prepare to drink from it. Reference images provide visual information; the final image still needs to be checked for appearance and spatial relationships.
When using /openai/chat/completions, read message.content from choices, which contains the image and download link. The client needs to identify and display the image link, then download and save the file; do not write the entire content string directly to a file as image data.
It depends on the entry point: Chat Completions uses model and messages, while Responses uses model and input. Hosted conversation entry points use methods such as question or structured message. Existing applications with image-and-text messages can prioritize continuing to use Chat Completions to avoid mixing request structures from different entry points.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Start with a clear goal and determine from real results whether it is suitable for your work.
