Image Generation

LLMGateway supports image generation models through the standard chat completions API. These models can create images based on text prompts and return them as base64-encoded data URLs.

Available Models

You can find all available image generation models on our models page.

Making Requests

Image generation works through the same /v1/chat/completions endpoint as text models. Simply use an image generation model and provide a text prompt describing the image you want to create.

curl -X POST "https://api.llmgateway.io/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash-image-preview",
    "messages": [
      {
        "role": "user",
        "content": "Generate an image of a cute golden retriever puppy playing in a sunny meadow"
      }
    ]
  }'

Response Format

Image generation models return responses in the standard chat completions format, with generated images included in the images array within the assistant message:

{
	"id": "chatcmpl-1756234109285",
	"object": "chat.completion",
	"created": 1756234109,
	"model": "gemini-2.5-flash-image-preview",
	"choices": [
		{
			"index": 0,
			"message": {
				"role": "assistant",
				"content": "Here's an image of a cute dog for you: ",
				"images": [
					{
						"type": "image_url",
						"image_url": {
							"url": "data:image/png;base64,<base64_encoded_image_data>"
						}
					}
				]
			},
			"finish_reason": "stop"
		}
	],
	"usage": {
		"prompt_tokens": 8,
		"completion_tokens": 1303,
		"total_tokens": 1311
	}
}

Vision support

You can edit or modify images by combining image generation with vision models by including the image in the messages array.

Response Structure

Images Array

The images array contains one or more generated images with the following structure:

• type: Always "image_url" for generated images
• image_url.url: A data URL containing the base64-encoded image data (format: data:image/png;base64,<data>)

Content Field

The content field may contain descriptive text about the generated image, depending on the model's behavior.

Usage Notes

Best Practices

Prompt Engineering

• Be specific and descriptive in your prompts for better results
• Include details about style, composition, lighting, and mood
• Experiment with different models to find the best fit for your use case

Handling Responses

• Parse the images array to extract generated images
• The base64 data can be directly used in HTML <img> tags or saved to files
• Consider implementing proper error handling for failed generation requests

Cost Optimization

• Monitor token usage as image generation can be more expensive than text generation
• Use auto routing to automatically select cost-effective image generation models
• Cache generated images when appropriate to avoid repeated generation costs