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Generate and edit images through Eden AI’s OpenAI-compatible image endpoints. Point any OpenAI client at https://api.edenai.run/v3 and the images API works as a drop-in replacement.

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Overview

Image models are served by dedicated image endpoints — not the chat completions endpoint:
MethodPathPurpose
POST/v3/images/generationsGenerate one or more images from a text prompt
POST/v3/images/editsEdit an existing image with a prompt (optional mask)
GET/v3/images/modelsList the image models available in your region
Both endpoints return the standard OpenAI image shape — data: [{ url \| b64_json }] — plus Eden’s cost (USD) and provider fields.
Image models are only available on /v3/images/*. Sending one to /v3/chat/completions returns 400 — Model(s) do not support /chat/completions.
This is different from Expert Model image generation, which wraps Eden’s feature API for providers like DALL-E and Stable Diffusion. Use the /v3/images endpoints when you want an OpenAI-compatible, drop-in image surface.

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Models

The image surface spans many providers. A few common model strings:
ProviderModel string
Googlegoogle/gemini-2.5-flash-image, google/gemini-3-pro-image-preview, google/imagen-4.0-generate-001
OpenAIopenai/gpt-image-1, openai/gpt-image-1-mini
Stabilitystabilityai/sd3.5-large
Amazonamazon/amazon.nova-canvas-v1:0
Call GET /v3/images/models for the full, region-aware list (image models also appear in GET /v3/models, so the OpenAI SDK’s client.models.list() returns them too).

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Generating images

import requests

url = "https://api.edenai.run/v3/images/generations"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
}

payload = {
    "model": "google/gemini-2.5-flash-image",
    "prompt": "A nano banana dish in a fancy restaurant, cinematic lighting",
    "size": "1024x1024",
    "n": 1,
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

# Each item carries either a hosted `url` or an inline base64 `b64_json`
image = result["data"][0]
print(image["url"] or image["b64_json"][:60])
Because the surface is OpenAI-compatible, the official OpenAI SDK works unchanged — just set base_url: 
from openai import OpenAI

client = OpenAI(base_url="https://api.edenai.run/v3", api_key="YOUR_API_KEY")

result = client.images.generate(
    model="google/gemini-2.5-flash-image",
    prompt="A nano banana dish in a fancy restaurant, cinematic lighting",
    size="1024x1024",
    n=1,
)
print(result.data[0].url or result.data[0].b64_json[:60])

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Request parameters

FieldRequiredDescription
modelyesprovider/model, e.g. google/gemini-2.5-flash-image
promptyesText description of the image to generate (1–32,000 chars)
nnoNumber of images to generate (1–10)
sizenoOutput resolution, e.g. 1024x1024, 1536x1024, auto. Allowed values depend on the model
qualitynostandard, hd, auto — accepted values depend on the model
response_formatnourl or b64_json, forwarded to providers that honor it
Unknown fields are forwarded to the provider unchanged.

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Response format

{
  "created": 1779792103,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAAANSUhEUg...",
      "url": null,
      "revised_prompt": null
    }
  ],
  "output_format": "png",
  "size": "1024x1024",
  "usage": {
    "total_tokens": 291,
    "input_tokens": 19,
    "output_tokens": 272
  },
  "cost": 0.010975,
  "provider": "google"
}

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Saving the image to disk

import base64

# Assuming `result` is the response from POST /v3/images/generations above
image = result["data"][0]

if image["b64_json"]:
    with open("output.png", "wb") as f:
        f.write(base64.b64decode(image["b64_json"]))
else:
    # The provider returned a hosted URL instead of inline base64
    import requests
    with open("output.png", "wb") as f:
        f.write(requests.get(image["url"]).content)
Some providers return a hosted url instead of inline b64_json. Download the image if you want to keep it, since those URLs can expire.

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Editing images

POST /v3/images/edits takes the same fields as generations plus an images array. Each entry sets exactly one of file_id (returned by Eden’s /v3/upload/... endpoints) or image_url (an https:// URL or a data:image/...;base64,... URL). An optional mask (same shape) marks the regions to edit. The endpoint dispatches on Content-Type: send application/json for the images[] form below, or multipart/form-data for the classic OpenAI SDK file-upload form. 
import requests

url = "https://api.edenai.run/v3/images/edits"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
}

payload = {
    "model": "google/gemini-2.5-flash-image",
    "prompt": "Add a small red hat",
    "images": [
        {"image_url": "https://example.com/photo.png"}
    ],
    "size": "1024x1024",
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()
print(result["data"][0]["b64_json"][:60])
image_url with an https:// URL works for providers that accept remote URLs (e.g. Gemini). For openai/gpt-image-*, pass a data: URL or upload the file via /v3/upload/... and reference it by file_id instead — those models do not accept remote URLs.

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Listing image models

cURL
curl https://api.edenai.run/v3/images/models \
  -H "Authorization: Bearer YOUR_API_KEY"

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Limitations

  • No streaming. stream: true is forwarded to the provider but the partial-image pipeline is not wired up.
  • No @edenai router or fallbacks. Specify a single explicit provider/model.
  • /v3/images/variations is not implemented.

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Next Steps

Expert Image Generation

Use Eden’s feature API for DALL-E, Stable Diffusion, and more

Chat Completions

Standard text chat completions reference