GPT Image 2.5 Sunburst
GPT Image 2.5 Sunburst — OpenAI's most capable image model, built for premium creative and editing workflows with tighter control across edits. Text-to-image, reference-image editing, mask inpainting, transparent backgrounds, quality up to max.
api reference
about
gpt image 2.5 sunburst — openai's most capable image model, built for premium creative and editing workflows with tighter control across edits. text-to-image, reference-image editing, mask inpainting, transparent backgrounds, quality up to max.
1. calling the api
install the client
the client provides a convenient way to interact with the api.
1pip install inferenceshsetup your api key
set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.
1export INFERENCE_API_KEY="inf_your_key"run and get result
submit a request and wait for the final result. best for batch processing or when you don't need progress updates.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8 "app": "openai/gpt-image-2-5-sunburst",9 "input": {10 "prompt": "A cat wearing a tiny top hat, oil painting style"11 }12 })1314print(result["output"])stream live updates
get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9 "app": "openai/gpt-image-2-5-sunburst",10 "input": {11 "prompt": "A cat wearing a tiny top hat, oil painting style"12 }13 }, stream=True):14 if update.get("progress"):15 print(f"progress: {update['progress']}%")16 if update.get("output"):17 print(f"output: {update['output']}")2. authentication
the api uses api keys for authentication. see the authentication docs for detailed setup instructions.
3. files
file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.
automatic upload
the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.
1# local file paths are automatically uploaded2result = client.run({3 "app": "openai/gpt-image-2-5-sunburst",4 "input": {5 "image": "/path/to/local/image.png", # detected & uploaded6 "audio": "https://example.com/audio.mp3", # url passed through7 }8})4. webhooks
get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.
1result = client.run({2 "app": "openai/gpt-image-2-5-sunburst",3 "input": {4 "prompt": "A cat wearing a tiny top hat, oil painting style"5 },6 "webhook": "https://your-server.com/webhook"7}, wait=False)webhook payload
your endpoint receives a JSON POST with the task result:
1{2 "event": "task.completed",3 "timestamp": "2024-01-15T10:30:05Z",4 "data": {5 "id": "task_abc123",6 "short_id": "abc123",7 "status": 10,8 "status_text": "completed",9 "output": { ... },10 "created_at": "2024-01-15T10:30:00Z",11 "updated_at": "2024-01-15T10:30:05Z"12 }13}5. schema
input
text prompt describing the desired image.
optional reference image(s) for editing. when a mask is provided, it applies to the first image.
optional mask image indicating areas to edit (requires input images). transparent areas in the mask indicate where the image should be edited. applied to the first image.
output image width in pixels. must be a multiple of 16.
output image height in pixels. must be a multiple of 16.
rendering quality. 'low' for fast drafts, 'high' for final assets, 'xhigh' and 'max' for maximum detail at higher cost.
number of images to generate (1-10).
output file format.
compression level for jpeg/webp (0-100). ignored for png.
content moderation strictness. 'auto' applies standard filtering; 'low' is less restrictive.
background transparency. 'transparent' produces an alpha-channel image (requires png or webp output; prompt for an isolated subject, not a scene). 'opaque' forces a solid background. 'auto' lets the model decide.
Gemini Nano Banana 2.1
google/gemini-nano-banana-2-1
$1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search
Grok Imagine Image 2.0
xai/grok-imagine-image-2-0
$0.04/img low 1k, $0.06/img low 2k, $0.06/img med 1k, $0.08/img med 2k; $0.01/source img (edits)
GPT Image 2.5 Flare
openai/gpt-image-2-5-flare
$0.006-$0.21/image at 1024x1024: low $0.0059, medium $0.0132, high $0.0527, xhigh $0.0937, max $0.2107. Larger sizes cost more; edits add ~$0.0083 per reference image.
Image Resize & Crop
eval/resize
from $0.00000277/sec
X Post Card Renderer
x/post-render
from $0.00000277/sec
new models
VACE
pruna/vace
video$0.40/video
P-Video-Animate
pruna/p-video-animate
video$0.03/sec 720p, $0.06/sec 1080p
P-Video-Upscale
pruna/p-video-upscale
videoStd: $0.01/sec 720p, $0.02 1080p, $0.04 2k, $0.08 4k; high fps 2x
P-Video-2-Pro-Avatar
pruna/p-video-2-pro-avatar
video$0.02/sec 480p, $0.04/sec 768p, $0.06/sec 1080p, $0.08/sec 2k, $0.12/sec 4k
P-Video-2-Pro-Reference
pruna/p-video-2-pro-reference
videoOutput: cost $0.015-$0.105/sec, speed $0.02-$0.115/sec. Ref video: $0.03-$0.06/sec. Ref img: $0.005/img. Ref audio: $0.0015-$0.0025/sec
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