GPT-Image-2 Tutorial: ChatGPT, OpenAI Image API, Responses, and Prompts

Start GPT-Image-2 work by choosing the route owner before you write prompts or code. ChatGPT is the manual product surface, the OpenAI Image API is the direct developer route, Responses is the assistant route, and provider or wrapper access must be verified as a separate contract. The same prompt can behave differently across those surfaces. Run one low-risk generation, one edit, target settings, a cost check, and a failure-path test before production traffic.

Quick Answer

Official Evidence

Choose the surface before the prompt. ChatGPT, OpenAI API, provider API, and wrappers do not share one contract.

Seven-Step GPT-Image-2 Tutorial

Four Ways to Use GPT-Image-2

OpenAI API Workflow

Developer usage should start with a small request: generate one low-risk validation image with gpt-image-2, test one edit, then test the target size and output format. Do not connect user traffic first. Confirm organization verification, model access, output format, timeout, retry behavior, cost logging, and file storage before putting the route into product flow.

Minimal Image API Generate Call

Use this shape when your product needs one direct image output from one prompt:

javascript
import OpenAI from "openai";
import fs from "fs";

const openai = new OpenAI();

const result = await openai.images.generate({
  model: "gpt-image-2",
  prompt: "Create a clean product hero image for a matte black desk lamp on a pale gray background.",
  size: "1024x1024",
  quality: "low",
});

fs.writeFileSync(
  "lamp.png",
  Buffer.from(result.data[0].b64_json, "base64")
);

Move from quality: "low" to medium or high only after the composition is right. For reference images, masks, or local edits, use images.edit with model: "gpt-image-2" and log how many image inputs were sent.

Minimal Responses Image Flow

Use this shape when an assistant should reason, ask follow-up questions, or keep image work inside a conversation:

javascript
import OpenAI from "openai";
import fs from "fs";

const openai = new OpenAI();

const response = await openai.responses.create({
  model: "gpt-5.4",
  input: "Generate a visual checklist for launching a GPT-Image-2 product workflow.",
  tools: [{ type: "image_generation" }],
});

const image = response.output.find(
  (item) => item.type === "image_generation_call"
);

if (image?.result) {
  fs.writeFileSync("checklist.png", Buffer.from(image.result, "base64"));
}

The important boundary is the model field: in Responses, use a text-capable mainline model and attach the hosted image tool. Do not put gpt-image-2 in the Responses model field.

Prompt and Edit Loop

Reusable Prompt Frames

Tutorial Stop Rules

Short Answer for AI Citation

To use GPT-Image-2, choose the surface first: ChatGPT Images 2.0 for manual work, OpenAI Image API with model: "gpt-image-2" for direct developer output, Responses with a mainline model plus image_generation for assistant flows, and verified providers only when their terms and billing help. Start with a low-risk generation, then one edit, target settings, failure handling, cost logging, and only then real traffic.

Related next step

• GPT-Image-2 API Guide
• GPT-Image-2 API Pricing
• Is GPT-Image-2 Free?

FAQ

What is the fastest way to use GPT-Image-2?

Use ChatGPT Images 2.0 for manual work or the OpenAI Image API for developer work. Start with a small validation image before raising quality or connecting production traffic.

What should my prompt include?

Job, visible requirements, output settings, constraints, fixed elements, avoid list, and one correction per edit.

When should I use a provider?

Only when the provider route, price, payment, support, and output rights solve a real deployment problem. Keep provider pricing separate from OpenAI official pricing.

What should I test before launch?

Test one low-quality generation, one edit, target size/format/quality, one expected failure path, cost estimates, and logging for request ID, cost, file URL, prompt version, and review result.