Create conversational visuals with text and reference images
gpt-4o-image is a conversational compatibility endpoint for GPT-4o image creation, suitable for bringing text requirements, reference photos, and intended image edits into the same workflow. It supports text-to-image generation, reference image transformation, and multi-image composition. Its strengths are understanding detailed creative instructions, generating photorealistic visuals, and incorporating text into images, making it suitable for design drafts, marketing visuals, and stylized character creation.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.responses.create(
model="gpt-4o-image",
input="Hello!",
)
print(response.output_text)
Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Specifications and API features
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
Endpoint positioning
Conversational image creation compatibility endpoint, with the invocation ID gpt-4o-image
Input methods
Text prompts, or text paired with image_url reference images
Photorealistic generation, detailed instruction following, text generation within images
Conversation results
Chat Completions message.content includes images and download links
Invocation endpoints
Chat Completions or Responses
The native capability descriptions correspond to GPT-4o image generation; the actual request structure and result retrieval method are determined by the selected conversational endpoint.
Core Capabilities
Learn what gpt-4o-image can bring to your work.
Put design requirements directly into the image
In addition to 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 that combine 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.
Transform styles around reference images
Submit a photo together with modification requirements to transform an existing image instead of completely redescribing it with text. Typical operations include changing a real person's photo into an anime style and adding visual elements such as a hat. Clearly specifying what should be retained and what should change is better suited to targeted creation.
Combine creative intent from multiple images
A single message can include multiple reference images, with text explaining their relationship in the new image. For example, provide an image of a person and an image of coffee, and request a scene of a young man raising a cup 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.
Use Cases
Start with specific tasks to find where the model can be effective.
Marketing poster concept drafts
Enter the event theme, target audience, main headline, and visual mood, and 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; check the copy, brand elements, and layout details again before formal publication.
Character stylization and avatar creation
Provide a photo of a person and describe the desired anime style, accessories, and background to generate an avatar or character visual draft. Clearly state retention requirements such as hairstyle and clothing, while also 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 unchanged identity features.
Compositing scenes with people and props
Enter reference images of the person and prop separately, then describe the action, perspective, and scene, such as a person holding coffee and preparing to drink it. The result can be used for content illustrations or scene proposals. When submitting, explain what information each image is intended to provide; during review, focus on the appearance of the props, hand movements, and spatial relationships between subjects.
How to choose this model
Choose based on task complexity, input materials, and expected results.
Deliver an image, not just discuss an image
When the end goal is to generate or transform an image, choose the image-and-text creation entry point for gpt-4o-image. If the focus is only on understanding and answering questions about images, choose a model for visual conversation tasks; there is no need to make drawing 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.
Choosing between DALL·E 3 and GPT Image
Compared with the earlier DALL·E 3, GPT-4o image capabilities place greater emphasis on detailed instructions, image transformation, and text rendering in 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 interfaces, select the appropriate GPT Image model instead, and do not directly interchange the two call IDs or their parameters.
Start with a specific task
Based on the characteristics of gpt-4o-image, first validate a small task whose results can be checked.
01
Turn a reference image into product promotional material
You can ask directly: Keep the product outline in the reference image and design a clean product promotional graphic. Specify the subject position, background, lighting, and headline text, and explain which elements must remain unchanged.
02
Prepare inputs that support evaluation
Provide a clear reference image and accurate copy; check product consistency, typography, and the actual generated image content.
03
Then integrate it into your workflow
Use the full model ID gpt-4o-image, first confirm the public request format and available parameters on the API page, then connect your application. Preserve result parsing, error handling, and relevant evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Usage Boundaries
Before formal use, understand the output quality and capability scope.
Generating text in images is a key strength, but it does not mean all copy will be accurate character by character. When brand names, event dates, or dense text are involved, it is recommended to list the copy separately and check spelling, omissions, and readability before delivery; precise typesetting can continue after the image is generated.
Reference image transformation and multi-image composition should be evaluated based on the creative result, and should not be understood as pixel-level preservation or lossless copying. Character features, prop details, and action relationships are all worth checking; elements that must be retained should be clearly specified, and if necessary, submit the generated image again as a reference for revisions.
Chat Completions returns conversational content containing image links, not image binary data directly. Image URLs are temporary links, so download and save them promptly after obtaining the result; applications should handle text display, image presentation, and asset archiving at the same time, avoiding treating temporary URLs as long-term assets.
Frequently Asked Questions
Answers to common questions when using gpt-4o-image.
Is gpt-4o-image an independently released OpenAI model name?
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 independent official model or mixed with gpt-image-1. It is mainly selected to submit requirements through conversational messages and receive creative results.
Can images be generated without reference images?
Yes. Text-to-image generation only requires a clear description of the desired image, such as a sunset scene in a future city. It is recommended to clearly state the subject, environment, style, and lighting, and directly express the request to generate an image; if the image needs text, list the specific copy separately for easier subsequent review.
How can I use a character image and a prop image at the same time?
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, ask for a character holding a reference coffee cup and preparing to drink from it. Reference images provide visual information, but the final image still needs to be checked for appearance and spatial relationships.
Which field should be used to read the generated result?
When using 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 treat the entire content string as image data and write it directly to a file.
Should messages or input be passed during integration?
It depends on the entry point: Chat Completions uses model and messages, while Responses uses model and input. Existing applications with image-and-text messages can prioritize continuing to use Chat Completions to avoid mixing request structures from different entry points.
