gpt-image-2:official is the image generation and editing model for OpenAI GPT Image 2, suitable for turning text ideas, product photos, and character references into deliverable visual assets. It focuses on text in images, layout composition, and reference image-guided creation. It can generate posters and infographics from scratch, as well as modify colors, backgrounds, or local objects in existing images, making it suitable for design workflows that require repeated reviews.
Clarify capacity, inputs and outputs, and invocation methods before selecting a model.
GPT Image 2 provides image creation and editing capabilities. The quantities, dimensions, formats, and upload rules above correspond to how this model is invoked on this platform.
Learn what gpt-image-2:official can bring to your work.
Suitable for generating headlines, slogans, descriptive labels, and the main image together, rather than merely drawing a text-free background. When creating, you can specify the main headline position, font-size hierarchy, whitespace, and color scheme, guiding a poster or infographic toward the intended visual structure; the final copy, numbers, and reading order should still be checked character by character.
When editing, you can provide product, character, or style references at the same time, and explain the role of each image, such as preserving a product's appearance, drawing from a background color palette, or adjusting the shooting atmosphere. This is suitable for developing different creative directions around existing assets, but reference images are creative constraints and do not mean trademarks, faces, and fine structures will remain unchanged pixel for pixel.
Mask editing can focus changes on a specified area, such as replacing an object on a tabletop, adjusting part of an outfit, or adding background elements. Prompts should describe both the complete target image and the content that needs to be preserved; clear Alpha boundaries help express intent, but edge blending still needs to be checked after the image is generated.
Start with specific tasks to find where the model can be effective.
Enter the event title, verified information, brand color palette, and layout requirements to generate promotional posters or infographics for proposals. It is recommended to distinguish between text that must be presented accurately and decorative content that can be freely created; when delivering, focus on checking dates, prices, labels, and hierarchy, then complete layout proofreading before formal publication.
Provide a clear product image and specify the packaging, outline, and viewing angle to preserve, then try different backgrounds, colors, or lifestyle scenes. Suitable for e-commerce key visuals, advertising proposals, and packaging mockups; use masks when only local changes are needed, and compare the generated image with the original afterward to check brand marks and product details.
Enter character references, clothing details, expression requirements, and image layout to create character design sheets or creative candidates around the same theme. Use multiple generations when multiple directions are needed, making it easy to compare composition and style; for ongoing projects, retain selected reference images and check the consistency of faces, clothing, and props one by one.
Choose based on task complexity, input materials, and expected results.
If a task requires both generating a draft from text and continuing to refine a selected image, GPT Image 2 is suitable as a creative tool for the same workflow. First use low or medium quality to validate the composition, then increase the quality to inspect details. Existing Image 1.5 projects can use the same copy and reference materials for comparison, focusing on text, layout, and editing results rather than deciding migration solely by version number.
GPT Image 2.5 Flare focuses on generation speed, while Sunburst focuses on high fidelity and fine control; GPT Image 2 is suitable for tasks such as text posters, product creatives, and reference image editing. Existing qualified templates can continue using this model; if delivery priorities shift to speed or more detailed editing control, compare the relevant variants with real materials without assuming a fixed degree of improvement.
From a small-scale task to full integration.
Define goals, 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.
Before formal use, understand the range of output quality and capabilities.
Answers to common questions about using gpt-image-2:official.
It is not another independent base model, but a public invocation ID variant of GPT Image 2. Both generation and editing requests should explicitly specify gpt-image-2:official; it shares the same core creative positioning as the ID without the suffix, but invocation and billing should be distinguished according to the selected model.
Use /openai/images/generations to create from text; when an existing image needs modification, use /openai/images/edits and pass a single URL, an array of URLs, or an uploaded file through image. For multi-image input, specify the role each image plays for the subject, style, or scene.
Use the editing endpoint and place the local original image and mask in the same multipart request. The mask must be an Alpha PNG of the same dimensions, with transparent areas indicating where modifications are allowed; the prompt should also clearly describe the intended changes and content to preserve, then check the region edges and surrounding details after generation.
You can use n to request 1–10 candidate images, making it easier to compare different compositions. response_format can be url or b64_json, but Base64 responses support only a single image. The output file format is separately controlled by output_format, with PNG, JPEG, or WebP available.
You can add callback_url so the request first returns task_id and you receive the final result when it is complete; synchronous mode returns image data directly. This model is billed based on actual Token usage, and the amount before a request is an estimate. Saving task IDs and deduplicating callbacks helps manage long-running tasks and avoid duplicate processing.
Model information · Updated: 2026-10-01. See the API and pricing sections for invocation parameters and billing rules.
Start with a clear objective and evaluate whether it suits your work based on real results.
