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Creates images from text prompts using image generation models. Internally routes to chat completion models with image generation capabilities.

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Endpoint

POST https://api.llmgateway.io/v1/images/generations

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Authentication

Requires authentication using Bearer token or x-api-key header. See Authentication.

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Request Body

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prompt
string
required
A text description of the desired image(s).Example: "A white siamese cat"
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model
string
default:"auto"
The model to use for image generation.Options:
  • "auto" - Automatically selects gemini-3-pro-image-preview
  • "gemini-2.5-flash-image"
  • "gemini-3-pro-image-preview"
  • Other image generation models
Example: "gemini-2.5-flash-image"
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n
number
default:1
The number of images to generate. Must be between 1 and 10.Example: 1
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size
string
The size of the generated images. Supported sizes depend on the model.Common sizes:
  • "1024x1024" (square)
  • "1792x1024" (landscape)
  • "1024x1792" (portrait)
  • "1536x1024"
  • "1024x1536"
Example: "1024x1024"
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aspect_ratio
string
The aspect ratio of the generated images. Takes precedence over size if both are provided.Common ratios:
  • "1:1" (square)
  • "16:9" (landscape)
  • "9:16" (portrait)
  • "4:3"
  • "3:2"
  • "7:4"
Example: "16:9"
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quality
string
The quality of the generated image.Options: "standard", "hd", "low", "medium", "high"Example: "standard"
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style
string
The style of the generated images.Options:
  • "vivid" - Hyper-real, dramatic lighting and colors
  • "natural" - Realistic, organic look
Example: "vivid"
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response_format
string
default:"b64_json"
The format in which the generated images are returned. Only b64_json is supported.Example: "b64_json"

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Response

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created
number
Unix timestamp of when the images were created.
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data
array
Array of generated images.Each image contains:
  • b64_json (string): Base64-encoded image data
  • revised_prompt (string, optional): The actual prompt used (may differ from input)

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Examples

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Generate Single Image

curl https://api.llmgateway.io/v1/images/generations \
  -H "Authorization: Bearer $LLMGATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "A serene mountain landscape at sunset",
    "model": "gemini-2.5-flash-image",
    "size": "1024x1024",
    "quality": "standard"
  }'

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Generate Multiple Images

response = client.images.generate(
    prompt="A futuristic city skyline",
    model="gemini-2.5-flash-image",
    n=3,
    size="1024x1024"
)

# Save all images
for i, image in enumerate(response.data):
    image_data = base64.b64decode(image.b64_json)
    with open(f"image_{i}.png", "wb") as f:
        f.write(image_data)

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Custom Aspect Ratio

response = client.images.generate(
    prompt="A wide panoramic view of the ocean",
    model="gemini-2.5-flash-image",
    aspect_ratio="16:9",  # Wide format
    style="natural"
)

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High Quality with Vivid Style

response = client.images.generate(
    prompt="A detailed dragon in a fantasy setting",
    model="gemini-2.5-flash-image",
    quality="hd",
    style="vivid",
    size="1792x1024"
)

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Response Example

{
  "created": 1677858242,
  "data": [
    {
      "b64_json": "iVBORw0KGgoAAAANSUhEUgAA...",
      "revised_prompt": "A serene mountain landscape at sunset with dramatic orange and pink skies"
    }
  ]
}

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Image Edits Endpoint

The gateway also supports editing existing images: 
POST https://api.llmgateway.io/v1/images/edits

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Request Body (JSON)

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images
array
required
Array of input image references to edit. Each image must have:
  • image_url (string): HTTPS URL or base64 data URL
Example:
[
  {"image_url": "https://example.com/image.png"},
  {"image_url": "data:image/png;base64,..."}
]
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prompt
string
required
A text description of the desired image edit.Example: "Add a watercolor effect to this image"
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model
string
The model to use for image editing.Example: "gemini-3-pro-image-preview"
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n
number
The number of edited images to generate (1-10).Example: 1
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background
string
Background behavior for generated image output.Options: "transparent", "opaque", "auto"Example: "transparent"
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input_fidelity
string
Controls fidelity to the original input image(s).Options: "high", "low"Example: "high"
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quality
string
Output quality for image models.Options: "low", "medium", "high", "auto"Example: "high"
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size
string
Requested output image size.Options: "auto", "1024x1024", "1536x1024", "1024x1536"Example: "1024x1024"
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output_format
string
Output image format.Options: "png", "jpeg", "webp"Example: "png"
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output_compression
number
Compression level for JPEG or WebP output (0-100).Example: 100

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Multipart Form Data

The edits endpoint also accepts multipart/form-data for OpenAI compatibility: 
curl https://api.llmgateway.io/v1/images/edits \
  -H "Authorization: Bearer $LLMGATEWAY_API_KEY" \
  -F [email protected] \
  -F prompt="Add a sunset background" \
  -F model="gemini-3-pro-image-preview" \
  -F size="1024x1024"
Supported field names for the image file:
  • image
  • image[] (used by some clients)
  • file
Optional mask field:
  • mask - Mask image to specify areas to edit

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Edit Image Example

response = client.images.edit(
    image=open("source.png", "rb"),
    prompt="Add a watercolor painting effect",
    model="gemini-3-pro-image-preview",
    size="1024x1024",
    input_fidelity="high",
    quality="high"
)

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

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Error Responses

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Invalid Request

{
  "error": true,
  "status": 400,
  "message": "Invalid request parameters: prompt: Required"
}

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No Images Generated

{
  "error": true,
  "status": 500,
  "message": "The model did not generate any images. Try a different model with image generation capabilities."
}

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Invalid Image URL

{
  "error": true,
  "status": 400,
  "message": "images[0].image_url must be an https URL or a base64 data URL"
}

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Notes

  • Images are returned as base64-encoded data in the response
  • The gateway internally uses chat completion models with image generation capabilities
  • Different models support different aspect ratios and sizes
  • For best results, provide detailed and specific prompts
  • The revised_prompt in the response shows how the model interpreted your prompt

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