GPT Image 2.6 Flare
gpt-image-2.6-flare is the public invocation ID for GPT Image 2.6 Flare. It focuses on fast generation and editing, supports text-to-image generation and reference image editing, and uses the GPT Image 2.5 image API.
Bring this model into your workflow
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
- API host
api.acedata.cloud- model
gpt-image-2.6-flare
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Specifications and API
- Creation Methods
- Text-to-image generation; reference image editing
- Output Formats
- PNG, JPEG, WebP, GIF
- Quality Options
- auto, low, medium, high, xhigh, max
Choose a Model
Flare focuses on fast generation and editing, while Sunburst focuses on detailed composition and editing. Explicitly specify gpt-image-2.6-flare; do not use gpt-image-2.6, which omits the variant name.
Text-to-Image Generation
Submit a prompt through the image generation API, describing the subject, scene, lighting, color palette, and aspect ratio. You can first generate one image to check the composition, then adjust the quality and dimensions as needed.
Edit Reference Images
Submit image and prompt through the image editing API. JSON accepts image URLs, while multipart accepts local files; up to 16 reference images can be provided. Describe the role of each image and what should be preserved and modified.
Billing and Results
Billing is based on the number of successfully generated images, with a base price of 0.22 Credits/image. Returned Token statistics do not change the per-image billing method. By default, read the image link in data; when b64_json is selected, only one image is returned. For long-running tasks, you can pass async or callback_url and use the original task_id to query the completed result.
Usage Limits
Use auto or WIDTHxHEIGHT for size. Width and height must be multiples of 16, the longest side must not exceed 3840, total pixels must be 655,360–8,294,400, and the aspect ratio must not exceed 3:1. n is 1–10, and b64_json requires n=1. Static PNG/JPEG/WebP supports 4K pixel presets. For native GIFs, 512x512, 9 frames, and 200ms per frame are recommended; actual dimensions may be adjusted by the model. GIFs cannot directly use static 4K specifications. SVG is not currently supported. Version 2.6 does not configure cross-model substitution.
Frequently Asked Questions
Do generation and editing use the same model?
Yes, both entry points use the full gpt-image-2.6-flare.
Are xhigh and max supported?
Yes, both generation and editing support them; higher quality may increase wait time and official usage.
How should the reverse suffix be understood?
It is an explicit alias for the corresponding regular version, billed by the number of successfully generated images.
How is official billed?
It is billed based on actual Token usage, with rates consistent with 2.5 official, not at a fixed price per image.
What should I do if the asynchronous wait times out?
Keep the original task_id and query the task status to avoid repeatedly submitting generation requests.
Advanced Parameters
Supports complexity=low/medium/high and static image markup annotation editing. GIF supports 2–16 frames, each frame 20–655350ms (rounded to 10ms increments), with defaults of 9 frames and 200ms. The annotation image must be a PNG of the same dimensions as the first reference image and contain transparent pixels; it cannot be used together with mask or GIF.
Use it in your favorite tools
Choose a development approach or application suited to this model and follow its setup guide.
Connect gpt-image-2.6-flare to Python SDK
Python SDK Integration Guide
SDK installation, authentication and examplesConnect gpt-image-2.6-flare to TypeScript SDK
TypeScript SDK Integration Guide
SDK installation, authentication and examplesOpenAI Images Generations API
API parameters and request examplesOpenAI Images Edits API
API parameters and request examplesQuickstart
This is only a basic call example. See the full docs for more parameters and advanced usage.
View Docs