How to Use GPT Image 2: ChatGPT, API, Online Generator, and Prompts

Using GPT Image 2 well is less about finding a magic prompt and more about choosing the right workflow. A developer building an API integration, a marketer creating product ads, and a designer testing reference-image edits all need different starting points.

This guide shows the main practical paths for using GPT Image 2:

• Use OpenAI's API when you need a production integration.
• Use the Responses API when you need a multi-turn image workflow.
• Use ChatGPT-style image generation when you want a product experience and do not need direct API control.
• Use an online generator when you want to test prompts, compare visual directions, and avoid writing code too early.

Last verified: June 8, 2026.

Official references:

• OpenAI GPT Image 2 model page
• OpenAI image generation guide
• OpenAI Images API reference
• OpenAI API pricing

Quick answer

Use GPT Image 2 like this:

If you only need one image from one prompt, keep the workflow simple. If you need an editable product experience, saved context, or a multi-step assistant, plan the workflow before you generate anything.

Step 1: Choose where to use GPT Image 2

The first decision is not the prompt. It is the access path.

Option A: Use the OpenAI Image API

Choose this when you want to generate or edit images inside your own product, backend job, internal tool, or automation.

OpenAI's image generation guide says the Image API provides generation and edit endpoints for GPT Image models, including gpt-image-2. With this path, you choose the GPT Image model directly.

Use it for:

• Automated product image creation
• Batch ad variants
• Internal design tools
• User-facing image generation features
• Reference-image editing workflows
• Controlled experiments where you need repeatable settings

Basic JavaScript example:

import OpenAI from "openai";
import fs from "node:fs";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

const result = await openai.images.generate({
  model: "gpt-image-2",
  prompt:
    "Create a square ecommerce product photo of a matte black insulated bottle on a pure white background, front-facing, crisp edges, soft studio light, no props, no text.",
  size: "1024x1024",
  quality: "medium",
});

const imageBase64 = result.data?.[0]?.b64_json;

if (!imageBase64) {
  throw new Error("No image returned");
}

fs.writeFileSync("product-photo.png", Buffer.from(imageBase64, "base64"));

This is intentionally small. Before adding queues, retries, databases, or user accounts, prove that your prompt produces useful images.

Option B: Use the Responses API for multi-turn image workflows

Choose this when you want a conversational image product: the user generates an image, asks for a revision, keeps context, adds references, and continues refining.

OpenAI's image guide explains that the Responses API supports image generation as a built-in tool and is better suited for multi-turn editing and workflows that accept image File IDs as inputs.

Use it for:

• Chat-based image editors
• Iterative design assistants
• Image workflows with saved conversation context
• Multi-step product creation flows
• Experiences where the user says "keep this, change that"

The tradeoff is complexity. Responses API requests can include mainline model token usage in addition to image generation costs, so measure the whole interaction, not only the final image.

Option C: Use ChatGPT image generation

Choose this when you want to create images inside ChatGPT's product interface.

The key detail: GPT Image 2 is the API model name. ChatGPT image generation is a product surface, and the UI may not expose the exact underlying API model ID to you. If your goal is casual image creation, that may be fine. If your goal is production integration, use the API docs instead.

Use ChatGPT-style image generation for:

• Brainstorming visuals
• One-off creative exploration
• Rough moodboards
• Early prompt ideas
• Non-technical image iteration

Do not use it as your source of truth for API model availability, pricing, rate limits, or production behavior.

Option D: Use an online GPT Image 2 generator

Choose this when you want to test prompts before writing code.

The GPT Image 2 generator is useful for answering a practical question:

Does this prompt produce the kind of image I can actually use?

This is the fastest path for marketers, ecommerce operators, founders, and designers who want to compare product images, posters, UI concepts, and reference edits before committing to an API integration.

Step 2: Define the image job

Most weak GPT Image 2 prompts fail because the user asks for a vibe instead of a job.

Weak:

Make a premium image for my product.

Better:

Create a square ecommerce listing image for a matte black insulated bottle. The bottle is centered, front-facing, fully visible, and shown on a pure white background with soft studio lighting and a natural shadow. Preserve the cylindrical shape and cap design. Do not add props, hands, logos, watermarks, or random text.

Before prompting, choose one job:

The more practical the job, the easier it is to judge the output.

Step 3: Use a prompt contract

A prompt contract tells GPT Image 2 what must happen and what must not change.

Use this structure:

Create a [format] image for [use case].
Main subject: [subject details that must stay accurate].
Reference rules: [what to preserve if input images are used].
Composition: [framing, camera angle, layout, aspect ratio or size].
Scene and lighting: [background, environment, light, mood].
Style: [realism level, visual style, material quality].
Text: [exact words in quotes, placement, no extra words].
Constraints: [what to avoid, what must stay unchanged].

You do not need every line every time. But if product accuracy, readable text, or reference preservation matters, do not skip those parts.

Step 4: Generate one small test first

Do not start with a 20-image batch. Start with one or a small set, then score the output.

Use this review checklist:

Only scale after the prompt passes the job-specific checks.

Step 5: Revise one variable at a time

When an output is close, do not rewrite the whole prompt. Change one variable.

Good revision prompt:

Keep the product, camera angle, and lighting from the previous image. Change only the background to a clean light-gray studio surface. Do not change the bottle shape, cap, label area, or product color.

This is stronger than:

Make it better and cleaner.

Prompt examples by use case

Product listing image

Create a square ecommerce product image of a matte black insulated water bottle. Show the bottle front-facing, centered, and fully visible on a pure white background. Use soft diffused studio lighting, crisp edges, and a subtle natural shadow. Preserve the cylindrical shape, cap design, matte finish, and clean label area. Do not add props, hands, fake logos, random text, signatures, or watermarks.

Why it works:

• The job is clear: ecommerce product image.
• The subject is inspectable.
• The background is controlled.
• The negative constraints remove common failure points.

For more commerce templates, use the GPT Image 2 product photo prompt guide.

Lifestyle hero image

Create a horizontal website hero image for a premium ceramic coffee mug. Place the mug on the left third of a warm wooden desk with a closed notebook and a folded linen napkin nearby. Use soft morning window light, natural shadows, realistic product photography, and a calm workspace mood. Leave clean negative space on the right for website copy. Do not add text, hands, logos, or clutter.

Why it works:

• It gives the image a page role.
• It reserves copy space.
• It keeps the product as the main subject.

Poster with readable text

Create a vertical product ad poster for wireless earbuds. Show the earbuds floating above a dark graphite surface with blue rim lighting and clean reflections. Add one large headline at the top that reads "SOUND WITHOUT LIMITS". Add no other text. Leave the lower third open for a call-to-action button. Do not add fake logos, watermarks, signatures, or unreadable small text.

Why it works:

• The text is short.
• The phrase is exact.
• The layout has one text area.
• Extra words are explicitly banned.

For text-heavy workflows, read How to make readable text in GPT Image 2 images.

Reference image edit

Edit the reference image while preserving the product shape, camera angle, logo placement, packaging proportions, and main color. Replace the background with a clean light-gray studio setting. Improve lighting, remove dust, and soften harsh reflections. Do not change the label text, dimensions, logo, cap design, or product color.

Why it works:

• It puts preservation first.
• It changes one major thing: the background and cleanup.
• It names what must not drift.

For a deeper method, use the reference image editing workflow.

UI mockup

Create a realistic mobile app screen mockup for an AI image generator. Aspect ratio 9:16. The screen has a top navigation bar, a large prompt input area, a generated image preview, model and aspect ratio controls, and a primary button labeled "Generate". Use clean modern SaaS styling, realistic spacing, high contrast, and readable labels. Do not add fake brand logos, dense placeholder text, or unreadable microcopy.

Why it works:

• It describes the screen like a product designer.
• It avoids vague "beautiful UI" language.
• It asks for realistic information density.

Storyboard panel set

Create a six-panel storyboard for a product launch video. Panel 1: close-up of a matte black smart speaker on a desk. Panel 2: a hand reaches toward the product. Panel 3: the top light ring turns on. Panel 4: sound waves are represented with subtle abstract motion lines. Panel 5: the product sits beside a phone app screen. Panel 6: final hero shot with clean negative space. Keep the product shape, color, and camera language consistent across all panels. Add no text.

Why it works:

• It defines shot order.
• It names continuity requirements.
• It avoids spending video credits before planning the sequence.

For the video planning layer, read the GPT Image 2 to AI video workflow.

API settings that matter

OpenAI's image guide documents customization options such as size, quality, format, compression, and background. For GPT Image 2, size can be flexible within documented constraints, and common sizes include square, landscape, portrait, 2K, and 4K options.

Use settings deliberately:

Avoid maxing everything out during exploration. A careful prompt plus medium settings can teach you more than an expensive batch with vague instructions.

How to use GPT Image 2 for ecommerce images

For ecommerce, judge output by buyer clarity, not artistic style.

Workflow:

1. Start with a white-background listing prompt.
2. Check product geometry, color, and edge quality.
3. Create a lifestyle hero only after the listing image is stable.
4. Create detail shots for material, texture, or packaging.
5. Create ad variants after you know the product stays recognizable.

Prompt set:

Listing: Create a square front-facing product photo on a pure white background...

Hero: Create a horizontal lifestyle image with the product on the left and clean copy space on the right...

Detail: Create a macro close-up of the material texture and product edge...

Ad: Create a vertical 4:5 product ad with one short headline and no extra text...

Internal next step: browse the product prompt category or the product photo prompt guide.

How to use GPT Image 2 with reference images

Reference images are useful when the uploaded image contains something you cannot afford to lose:

• Product shape
• Packaging proportions
• Logo placement
• Person identity
• Room layout
• Character design
• UI structure
• Brand style

Use this order:

Preserve: [source-of-truth details].
Change: [one major change].
Improve: [lighting, cleanup, crop, style].
Do not change: [details that must stay fixed].

Example:

Preserve the sneaker silhouette, sole shape, laces, side logo placement, and white-and-navy colorway from the reference image. Change only the background to a clean concrete studio floor. Improve lighting and shadows for a premium product photo. Do not change the shoe shape, color, logo placement, or materials.

If identity or product accuracy matters, reference rules should appear before style words.

Troubleshooting

The image looks good but is not useful

This usually means the prompt optimized for style instead of the job. Add the real use case:

Create a marketplace listing image...
Create a landing page hero image...
Create a source frame for image-to-video...
Create a product ad with one headline...

The model adds random text

Use:

Add no text.

or:

Add exactly one headline that reads "[PHRASE]". Do not add any other words, labels, logos, signatures, or watermarks.

The product changes shape

Use a reference image and stronger preservation language:

Preserve the exact product shape, proportions, color, logo placement, label area, camera angle, and packaging structure from the reference image.

The scene is too busy

Remove competing instructions:

Use one product, one surface, one background, and no props except [specific prop].

Costs rise too quickly

Stop batching. Test fewer outputs, lower exploration quality, and save only prompt patterns that pass your review checklist.

A simple repeatable workflow

Use this when you are unsure where to start:

1. Pick one use case: product, poster, UI, edit, storyboard, or video frame.
2. Write one prompt contract.
3. Generate one image.
4. Score the output with a checklist.
5. Revise one variable.
6. Save the winning prompt.
7. Scale only after the output is useful.

You can test that workflow in the GPT Image 2 generator, then compare examples in the GPT Image 2 prompt library.

Bottom line

The best way to use GPT Image 2 is to treat every prompt as a small production brief. Define the job, control the subject, choose the right access path, and evaluate the result by usefulness.

If you are building a product, use OpenAI's official model page, image generation guide, API reference, and pricing page as your source of truth. If you are still exploring prompts, use an online generator first, save the prompt structures that work, and only then turn them into repeatable API workflows.