GPT Image 2

Overview

GPT Image 2 is a general-purpose image model for high-quality generation and practical creative workflows. It is especially useful when you want strong prompt adherence, polished output, and better handling of readable text inside images.

What it is good at

Marketing and brand visuals
Product imagery and packaging concepts
UI mockups and interface scenes
Posters, cards, menus, and other text-heavy visuals
Transparent-background cutouts and compositing assets

Recommended prompt structure

Write prompts in this order when possible:

Subject
Scene or environment
Composition and camera framing
Lighting and material cues
Required on-image text, if any

Example:

A premium product photo of a matte black coffee bag on a stone countertop, front-facing composition, soft morning light, subtle steam in the background. The label reads 'HOUSE BLEND'.

Text rendering tips

Put required text in quotes.
Keep each text region short.
Ask for a concrete layout context such as poster, menu board, price card, or UI screen.
Review outputs manually before publication when the text is business-critical.

Output strategy

Use low for fast ideation.
Use medium for most prompt iteration.
Use high for the final render that will actually be exported or reviewed.

Size and billing

The public size parameter uses aspect ratios rather than pixel dimensions. Common values include:

1:1
3:4
4:3
4:5
5:4
16:9
9:16
2:3
3:2
21:9
9:21
1:2
2:1

Credit usage is determined by quality:

low: 2 credits per image
medium: 6 credits per image
high: 12 credits per image

For most teams, the practical pattern is low for drafts, medium for production iteration, and high only for final-review assets.

Common limitations

Dense paragraphs inside the image are still less reliable than short labels or headlines.
Highly constrained layouts may require multiple iterations.
Sensitive prompts may be blocked or transformed by safety rules.

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