GPT Image Generation API Guide: Create AI Images with gpt-image-1 in 2026

curl -X POST "https://api.openai.com/v1/images/generations" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-image-1",
    "prompt": "A modern tech startup office with floor-to-ceiling windows overlooking a city skyline at sunset. Developers working at standing desks with multiple monitors showing code. Photorealistic style.",
    "n": 1,
    "size": "1536x1024",
    "quality": "high"
  }'

from openai import OpenAI
import base64

# Using Crazyrouter for cost savings
client = OpenAI(
    api_key="YOUR_CRAZYROUTER_KEY",
    base_url="https://crazyrouter.com/v1"
)

# Generate an image
response = client.images.generate(
    model="gpt-image-1",
    prompt="A futuristic AI chip glowing with blue neural pathways, "
           "placed on a circuit board. Macro photography, shallow depth of field.",
    n=1,
    size="1024x1024",
    quality="high",
    response_format="b64_json"
)

# Save the image
image_data = base64.b64decode(response.data[0].b64_json)
with open("ai_chip.png", "wb") as f:
    f.write(image_data)

print(f"Image saved! Revised prompt: {response.data[0].revised_prompt}")

import OpenAI from 'openai';
import fs from 'fs';

const client = new OpenAI({
  apiKey: process.env.CRAZYROUTER_API_KEY,
  baseURL: 'https://crazyrouter.com/v1'
});

async function generateImage() {
  const response = await client.images.generate({
    model: 'gpt-image-1',
    prompt: 'A clean, minimal infographic showing the architecture of a modern AI API gateway. Include labeled boxes for "Client", "API Gateway", "Load Balancer", and multiple "AI Provider" nodes. Professional tech diagram style.',
    n: 1,
    size: '1536x1024',
    quality: 'high'
  });

  console.log('Image URL:', response.data[0].url);
  
  // Download and save
  const imageResponse = await fetch(response.data[0].url);
  const buffer = Buffer.from(await imageResponse.arrayBuffer());
  fs.writeFileSync('architecture.png', buffer);
}

generateImage();

# Edit an existing image with gpt-image-1
response = client.images.edit(
    model="gpt-image-1",
    image=open("original.png", "rb"),
    mask=open("mask.png", "rb"),  # Optional: white areas = edit regions
    prompt="Replace the background with a tropical beach at sunset",
    n=1,
    size="1024x1024"
)

print(f"Edited image: {response.data[0].url}")

❌ "A cat"
✅ "A tabby cat sitting on a windowsill, golden hour lighting, 
   shot on Canon EOS R5, shallow depth of field, warm tones"

❌ "A website mockup"
✅ "A clean SaaS dashboard UI mockup showing analytics charts, 
   dark mode, modern design with purple accent colors, 
   16:9 aspect ratio, high fidelity wireframe"

"A professional headshot photo of a business executive.
 Do NOT include: watermarks, text overlays, artistic filters, 
 or unrealistic lighting."

"Split image: Left side shows a traditional office with paper files,
 right side shows a modern AI-powered workspace with holographic displays.
 Connected by a gradient transition in the middle."

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    api_key="YOUR_CRAZYROUTER_KEY",
    base_url="https://crazyrouter.com/v1"
)

async def generate_batch(prompts):
    tasks = [
        client.images.generate(
            model="gpt-image-1",
            prompt=prompt,
            n=1,
            size="1024x1024",
            quality="standard"
        )
        for prompt in prompts
    ]
    results = await asyncio.gather(*tasks)
    return [r.data[0].url for r in results]

prompts = [
    "A minimalist logo for an AI company, blue and white",
    "A developer typing code, cinematic lighting",
    "An abstract visualization of neural networks"
]

urls = asyncio.run(generate_batch(prompts))
for url in urls:
    print(url)