GPT Image 2.5
GPT Image 2.5
Product guide

What to expect from a new GPT image model

A practical guide to planning prompts, image references, editing workflows, and responsible launch copy for GPT Image 2.5.

GPT Image 2.5 product teamApr 25, 20266 min read
1

Start with the workflow, not the model name

Strong image products are built around jobs users already understand: product photography, social posts, portraits, background cleanup, restoration, and canvas expansion. GPT Image 2.5 presents those jobs as focused tools instead of asking users to learn provider details first.

Give each high-intent job a dedicated entry point instead of hiding everything behind one generic prompt box.
Keep tool names literal so users can scan the page and understand the outcome before starting.
Use gallery examples and prompt cards to show what a successful workflow produces.
2

Use references to keep direction consistent

Reference images, reusable prompts, and clear style notes help teams keep a campaign coherent across many outputs. The marketing pages now make that workflow visible through tool cards, prompt examples, gallery items, and a studio-style homepage panel.

Write prompts with subject, lighting, composition, output style, and constraints in separate phrases.
Route reusable examples back to the prompt library so teams can start from known-good language.
Make before-and-after states visible when the tool changes an existing image.
3

Keep launch claims precise

Public copy should explain what the front end supports today and what will be connected later. Uploads, generation, credits, refunds, and provider execution should be documented before real processing is enabled.

Avoid implying provider endorsement unless there is explicit authorization.
Separate visible frontend affordances from backend capabilities that still need integration.
Keep pricing and credit language aligned with the actual charge and refund lifecycle.
4

Design for safe iteration

The front-end structure is ready for real APIs, but it keeps unfinished execution states disabled or clearly labeled. That gives the product room to add storage, auth, credit charging, timeout handling, and download flows without changing the public information architecture.

Use shared libraries for provider behavior before adding app-specific API logic.
Document upload handling, retention, and deletion paths before asking for sensitive files.
Add E2E coverage after the real DOM exists so selectors match the shipped UI.
Launch readiness checklist
Tool pages explain the user job and route into the image workspace.
Prompt examples are reusable and tied to visible output categories.
Credits, failures, and refunds have clear product language.
Privacy, uploads, and commercial-use boundaries are linked from support surfaces.

Turn the guide into a working image flow

Use the current resource center to move from planning notes into prompts, examples, and the image workspace.

Back to resourcesOpen image workspace
What to expect from a new GPT image model