Image Generation

OpenRouter supports image generation via the Chat Completions and Responses endpoints. You can find the supported models, their capabilities, and pricing by filtering our model list by image output.

Model Discovery

You can find image generation models in several ways:

Via the API

Use the output_modalities query parameter on the Models API to programmatically discover image generation models:

$
# List only image generation models
$
curl "https://openrouter.ai/api/v1/models?output_modalities=image"
$
$
# List models that support both text and image output
$
curl "https://openrouter.ai/api/v1/models?output_modalities=text,image"

See Models - Query Parameters for the full list of supported modality values.

On the Models Page

Visit the Models page and filter by output modalities to find models capable of image generation. Look for models that list "image" in their output modalities.

In the Chatroom

When using the Chatroom, click the Image button to automatically filter and select models with image generation capabilities. If no image-capable model is active, you’ll be prompted to add one.

API Usage

To generate images, send a request to the /api/v1/chat/completions endpoint with the modalities parameter. The value depends on the model’s capabilities:

• Models that output both text and images (e.g., Gemini): Use modalities: ["image", "text"]
• Models that only output images (e.g., Sourceful, Flux): Use modalities: ["image"]

Basic Image Generation

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import { OpenRouter } from '@openrouter/sdk';
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const openRouter = new OpenRouter({
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  apiKey: '{{API_KEY_REF}}',
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});
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const result = await openRouter.chat.send({
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  model: '{{MODEL}}',
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  messages: [
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    {
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      role: 'user',
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      content: 'Generate a beautiful sunset over mountains',
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    },
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  ],
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  modalities: ['image', 'text'],
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  stream: false,
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});
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// The generated image will be in the assistant message
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if (result.choices) {
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  const message = result.choices[0].message;
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  if (message.images) {
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    message.images.forEach((image, index) => {
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      const imageUrl = image.imageUrl.url; // Base64 data URL
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      console.log(`Generated image ${index + 1}: ${imageUrl.substring(0, 50)}...`);
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    });
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  }
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}

Image Configuration Options

Some image generation models support additional configuration through the image_config parameter.

Aspect Ratio

Set image_config.aspect_ratio to request specific aspect ratios for generated images.

Supported aspect ratios:

• 1:1 → 1024×1024 (default)
• 2:3 → 832×1248
• 3:2 → 1248×832
• 3:4 → 864×1184
• 4:3 → 1184×864
• 4:5 → 896×1152
• 5:4 → 1152×896
• 9:16 → 768×1344
• 16:9 → 1344×768
• 21:9 → 1536×672

Azure MAI Image aspect ratios (supported by microsoft/mai-image-2.5):

• 1:1 → 1024×1024 (default)
• 4:3 → 1024×768
• 3:4 → 768×1024
• 16:9 → 1365×768
• 9:16 → 768×1365
• 3:2 → 1152×768
• 2:3 → 768×1152

Extended aspect ratios (supported by google/gemini-3.1-flash-image-preview only):

• 1:4 → Tall, narrow format ideal for scrolling carousels and vertical UI elements
• 4:1 → Wide, short format for hero banners and horizontal layouts
• 1:8 → Extra-tall format for notification headers and narrow vertical spaces
• 8:1 → Extra-wide format for wide-format banners and panoramic layouts

Image Size

Set image_config.image_size to control the resolution of generated images.

Supported sizes:

• 1K → Standard resolution (default)
• 2K → Higher resolution
• 4K → Highest resolution
• 0.5K → Lower resolution, optimized for efficiency (supported by google/gemini-3.1-flash-image-preview only)

You can combine both aspect_ratio and image_size in the same request:

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import requests
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import json
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url = "https://openrouter.ai/api/v1/chat/completions"
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headers = {
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    "Authorization": f"Bearer {API_KEY_REF}",
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    "Content-Type": "application/json"
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}
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payload = {
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    "model": "{{MODEL}}",
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    "messages": [
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        {
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            "role": "user",
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            "content": "Create a picture of a nano banana dish in a fancy restaurant with a Gemini theme"
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        }
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    ],
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    "modalities": ["image", "text"],
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    "image_config": {
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        "aspect_ratio": "16:9",
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        "image_size": "4K"
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    }
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}
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response = requests.post(url, headers=headers, json=payload)
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result = response.json()
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if result.get("choices"):
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    message = result["choices"][0]["message"]
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    if message.get("images"):
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        for image in message["images"]:
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            image_url = image["image_url"]["url"]
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            print(f"Generated image: {image_url[:50]}...")

Strength (Recraft only)

Set image_config.strength to control how much the output image differs from the input image during image-to-image generation. This parameter only applies when input images are provided in messages. It is only supported by Recraft models.

• Range: 0.0 to 1.0
• Default: 0.2
• Lower values produce outputs closer to the input image; higher values allow more creative deviation.

Example:

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{
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  "image_config": {
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    "strength": 0.7
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  }
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}

Text Layout (Recraft V3 only)

Use image_config.text_layout to place text at specific positions on the generated image. Each entry specifies the text to render and a bounding box defined by four corner points in normalized coordinates (0 to 1). This parameter is only supported by Recraft V3 (recraft/recraft-v3) for both text-to-image and image-to-image requests. Recraft V4 and V4 Pro do not support text_layout.

Each text layout entry is an object with:

• text (required): The text string to render
• bbox (required): Array of 4 [x, y] coordinate pairs defining the bounding box corners (top-left, top-right, bottom-right, bottom-left), with values from 0 to 1

Example:

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{
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  "image_config": {
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    "text_layout": [
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      {
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        "text": "Hello",
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        "bbox": [[0.3, 0.45], [0.6, 0.45], [0.6, 0.55], [0.3, 0.55]]
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      },
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      {
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        "text": "World",
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        "bbox": [[0.35, 0.6], [0.65, 0.6], [0.65, 0.7], [0.35, 0.7]]
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      }
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    ]
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  }
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}

Style (Recraft V3 only)

Use image_config.style to apply a specific artistic style to the generated image. This parameter is only supported by Recraft V3 (recraft/recraft-v3). Recraft V4 and V4 Pro do not support styles.

See the full list of available styles in Recraft’s documentation. Note that vector styles are not supported.

Example:

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{
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  "image_config": {
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    "style": "Photorealism"
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  }
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}

RGB Colors (Recraft only)

Use image_config.rgb_colors to specify a color palette that influences the generated image. Each color is a [r, g, b] array of three integers (0 to 255). This parameter is supported by Recraft models for both text-to-image and image-to-image requests.

Example:

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{
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  "image_config": {
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    "rgb_colors": [
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      [255, 0, 0],
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      [0, 128, 0]
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    ]
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  }
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}

Background RGB Color (Recraft only)

Use image_config.background_rgb_color to set a specific background color for the generated image. The value is a [r, g, b] array of three integers (0 to 255). This parameter is supported by Recraft models for both text-to-image and image-to-image requests.

Example:

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{
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  "image_config": {
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    "background_rgb_color": [0, 0, 255]
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  }
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}

You can combine rgb_colors and background_rgb_color in the same request:

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{
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  "image_config": {
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    "rgb_colors": [[255, 0, 0]],
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    "background_rgb_color": [255, 255, 255]
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  }
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}

Font Inputs (Sourceful only)

Use image_config.font_inputs to render custom text with specific fonts in generated images. The text you want to render must also be included in your prompt for best results. This parameter is only supported by Sourceful models (sourceful/riverflow-v2-fast and sourceful/riverflow-v2-pro).

Each font input is an object with:

• font_url (required): URL to the font file
• text (required): Text to render with the font

Limits:

• Maximum 2 font inputs per request
• Additional cost: $0.03 per font input

Example:

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{
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  "image_config": {
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    "font_inputs": [
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      {
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        "font_url": "https://example.com/fonts/custom-font.ttf",
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        "text": "Hello World"
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      }
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    ]
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  }
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}

Tips for best results:

• Include the text in your prompt along with details about font name, color, size, and position
• The text parameter should match exactly what’s in your prompt - avoid extra wording or quotation marks
• Use line breaks or double spaces to separate headlines and sub-headers when using the same font
• Works best with short, clear headlines and sub-headlines

Super Resolution References (Sourceful V2 only)

Use image_config.super_resolution_references to enhance low-quality elements in your input image using high-quality reference images. The output image will match the size of your input image, so use larger input images for better results. This parameter is only supported by Sourceful models (sourceful/riverflow-v2-fast and sourceful/riverflow-v2-pro) when using image-to-image generation (i.e., when input images are provided in messages).

Limits:

• Maximum 4 reference URLs per request
• Only works with image-to-image requests (ignored when there are no images in messages)
• Additional cost: $0.20 per reference

Example:

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{
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  "image_config": {
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    "super_resolution_references": [
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      "https://example.com/reference1.jpg",
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      "https://example.com/reference2.jpg"
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    ]
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  }
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}

Tips for best results:

• Supply an input image where the elements to enhance are present but low quality
• Use larger input images for better output quality (output matches input size)
• Use high-quality reference images that show what you want the enhanced elements to look like

Scoring Prompt (Sourceful V2.5 only)

Use image_config.scoring_prompt to give the model free-form guidance it uses to evaluate and refine its own output during generation. This parameter is only supported by Sourceful V2.5 models (sourceful/riverflow-v2.5-fast and sourceful/riverflow-v2.5-pro).

Example:

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{
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  "image_config": {
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    "scoring_prompt": "Prefer realistic materials, crisp product edges, and even studio lighting."
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  }
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}

Scoring Rubric (Sourceful V2.5 only)

Use image_config.scoring_rubric to provide a structured set of weighted dimensions the model scores its output against. This is the structured complement to scoring_prompt — the two are independent and can be provided together in the same request (rubric for weighted dimension scoring, prompt for additional free-form guidance). This parameter is only supported by Sourceful V2.5 models (sourceful/riverflow-v2.5-fast and sourceful/riverflow-v2.5-pro).

Each rubric entry is an object with:

• key (required): unique machine-readable identifier for the dimension
• label (required): human-readable name
• description (required): what this dimension evaluates
• weight (required): relative importance as a positive number
• passing_score (optional): minimum acceptable score
• score_guidance (optional): array of { "score": number, "description": string } anchors

Limits:

• 1 to 8 dimensions per request

Example:

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{
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  "image_config": {
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    "scoring_rubric": [
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      {
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        "key": "lighting",
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        "label": "Lighting quality",
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        "description": "Even, professional studio lighting with no blown highlights.",
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        "weight": 2,
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        "passing_score": 7,
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        "score_guidance": [
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          { "score": 10, "description": "Flawless, even studio lighting." },
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          { "score": 5, "description": "Acceptable but uneven lighting." }
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        ]
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      },
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      {
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        "key": "edges",
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        "label": "Edge sharpness",
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        "description": "Crisp, clean product edges without halos.",
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        "weight": 1
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      }
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    ]
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  }
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}

Background Mode and Color (Sourceful V2.5 only)

Use image_config.background_mode to control how the generated image’s background is handled, and image_config.background_hex_color to set the fill color for solid backgrounds. These parameters are only supported by Sourceful V2.5 models (sourceful/riverflow-v2.5-fast and sourceful/riverflow-v2.5-pro).

• background_mode: one of original (default — keep the generated background), transparent (remove the background), or solid (composite onto a flat color).
• background_hex_color: a #RRGGBB hex string. Required when background_mode is solid.

Behavior:

• Passing only background_hex_color (without background_mode) is treated as solid with that color.
• background_hex_color is validated but then discarded for original and transparent modes — a malformed hex string always returns a 400 regardless of mode.

Example (solid color):

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{
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  "image_config": {
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    "background_mode": "solid",
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    "background_hex_color": "#f6f1e8"
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  }
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}

Example (transparent):

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{
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  "image_config": {
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    "background_mode": "transparent"
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  }
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}

Streaming Image Generation

Image generation also works with streaming responses:

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import requests
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import json
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url = "https://openrouter.ai/api/v1/chat/completions"
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headers = {
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    "Authorization": f"Bearer {API_KEY_REF}",
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    "Content-Type": "application/json"
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}
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payload = {
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    "model": "{{MODEL}}",
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    "messages": [
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        {
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            "role": "user",
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            "content": "Create an image of a futuristic city"
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        }
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    ],
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    "modalities": ["image", "text"],
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    "stream": True
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}
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response = requests.post(url, headers=headers, json=payload, stream=True)
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for line in response.iter_lines():
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    if line:
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        line = line.decode('utf-8')
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        if line.startswith('data: '):
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            data = line[6:]
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            if data != '[DONE]':
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                try:
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                    chunk = json.loads(data)
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                    if chunk.get("choices"):
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                        delta = chunk["choices"][0].get("delta", {})
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                        if delta.get("images"):
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                            for image in delta["images"]:
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                                print(f"Generated image: {image['image_url']['url'][:50]}...")
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                except json.JSONDecodeError:
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                    continue

Response Format

When generating images, the assistant message includes an images field containing the generated images:

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{
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  "choices": [
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    {
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      "message": {
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        "role": "assistant",
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        "content": "I've generated a beautiful sunset image for you.",
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        "images": [
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          {
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            "type": "image_url",
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            "image_url": {
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              "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
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            }
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          }
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        ]
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      }
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    }
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  ]
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}

Image Format

• Format: Images are returned as base64-encoded data URLs
• Types: Typically PNG format (data:image/png;base64,)
• Multiple Images: Some models can generate multiple images in a single response
• Size: Image dimensions vary by model capabilities

Model Compatibility

Not all models support image generation. To use this feature:

1. Check Output Modalities: Ensure the model has "image" in its output_modalities
2. Set Modalities Parameter: Use ["image", "text"] for models that output both, or ["image"] for image-only models
3. Use Compatible Models: Examples include:
   • google/gemini-3.1-flash-image-preview (supports extended aspect ratios and 0.5K resolution)
   • google/gemini-2.5-flash-image
   • black-forest-labs/flux.2-pro
   • black-forest-labs/flux.2-flex
   • sourceful/riverflow-v2-standard-preview
   • Other models with image generation capabilities

Best Practices

• Clear Prompts: Provide detailed descriptions for better image quality
• Model Selection: Choose models specifically designed for image generation
• Error Handling: Check for the images field in responses before processing
• Rate Limits: Image generation may have different rate limits than text generation
• Storage: Consider how you’ll handle and store the base64 image data

Troubleshooting

No images in response?

• Verify the model supports image generation (output_modalities includes "image")
• Ensure you’ve set the modalities parameter correctly: ["image", "text"] for models that output both, or ["image"] for image-only models
• Check that your prompt is requesting image generation

Model not found?

• Use the Models page to find available image generation models
• Filter by output modalities to see compatible models