OpenAI
GPT Image 2.5 Sunburst
OpenAI GPT Image 2.5 Sunburst for high-precision image editing and complex instructions
Model TypeHigh-precision image generation and editing
Input / OutputText and image input, image output
API EndpointsSupports Image API generations and edits
Pricing & Specs
💰 Pricing
Input (Text)$5 / M tokens
Input (Image)$8 / M tokens
Price$30 / M image output tokens
⚙️ Specs
Model TypeHigh-precision image generation and editing
Input / OutputText and image input, image output
API EndpointsSupports Image API generations and edits
SizesSupports 1024x1024, 1024x1536, 2048x2048, 3840x2160, and more; max side 3840px, total pixels up to 8,294,400
Qualitylow / medium / high / xhigh / max / auto
Output FormatPNG by default, JPEG / WebP optional; Image API returns base64 images
BackgroundTransparent PNG/WebP output with background=transparent
Standard PricingText input $5 / 1M tokens ($1.25 cached), image input $8 / 1M tokens ($2 cached), image output $30 / 1M tokens
Model NotesPrioritizes editing precision for complex instructions, local changes, and reference-image consistency
Doc StatusUpdated from OpenAI's official image generation guide and pricing page
API Examples
Python
from openai import OpenAI
import base64
client = OpenAI(
base_url="https://api.yairouter.com/v1",
api_key="your-api-key"
)
result = client.images.generate(
model="gpt-image-2.5-sunburst",
prompt="A vertical poster for a modern tea brand, glass cup, grapefruit slices, fresh white and green background",
size="1024x1536",
quality="high",
output_format="png",
)
image_base64 = result.data[0].b64_json
with open("poster.png", "wb") as f:
f.write(base64.b64decode(image_base64))cURL
curl -s https://api.yairouter.com/v1/images/generations -H "Content-Type: application/json" -H "Authorization: Bearer YOUR_API_KEY" -d '{
"model": "gpt-image-2.5-sunburst",
"prompt": "A vertical poster for a modern tea brand, glass cup, grapefruit slices, fresh white and green background",
"size": "1024x1536",
"quality": "high",
"output_format": "png"
}' | jq -r '.data[0].b64_json' | base64 --decode > poster.png