OpenRouter

LiteLLM supports all the text / chat / vision / embedding models from OpenRouter

Usage

import os
from litellm import completion

os.environ["OPENROUTER_API_KEY"] = ""
os.environ["OPENROUTER_API_BASE"] = "" # [OPTIONAL] defaults to https://openrouter.ai/api/v1
os.environ["OR_SITE_URL"] = "" # [OPTIONAL]
os.environ["OR_APP_NAME"] = "" # [OPTIONAL]

response = completion(
            model="openrouter/google/palm-2-chat-bison",
            messages=messages,
        )

Configuration with Environment Variables

For production environments, you can dynamically configure the base_url using environment variables:

import os
from litellm import completion

# Configure with environment variables
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
OPENROUTER_BASE_URL = os.getenv("OPENROUTER_API_BASE", "https://openrouter.ai/api/v1")

# Set environment for LiteLLM
os.environ["OPENROUTER_API_KEY"] = OPENROUTER_API_KEY
os.environ["OPENROUTER_API_BASE"] = OPENROUTER_BASE_URL

response = completion(
    model="openrouter/google/palm-2-chat-bison",
    messages=messages,
    base_url=OPENROUTER_BASE_URL  # Explicitly pass base_url for clarity
)

This approach provides better flexibility for managing configurations across different environments (dev, staging, production) and makes it easier to switch between self-hosted and cloud endpoints.

OpenRouter Completion Models

🚨 LiteLLM supports ALL OpenRouter models, send model=openrouter/<your-openrouter-model> to send it to open router. See all openrouter models here

Model Name	Function Call
openrouter/openai/gpt-3.5-turbo	`completion('openrouter/openai/gpt-3.5-turbo', messages)`
openrouter/openai/gpt-3.5-turbo-16k	`completion('openrouter/openai/gpt-3.5-turbo-16k', messages)`
openrouter/openai/gpt-4	`completion('openrouter/openai/gpt-4', messages)`
openrouter/openai/gpt-4-32k	`completion('openrouter/openai/gpt-4-32k', messages)`
openrouter/anthropic/claude-2	`completion('openrouter/anthropic/claude-2', messages)`
openrouter/anthropic/claude-instant-v1	`completion('openrouter/anthropic/claude-instant-v1', messages)`
openrouter/google/palm-2-chat-bison	`completion('openrouter/google/palm-2-chat-bison', messages)`
openrouter/google/palm-2-codechat-bison	`completion('openrouter/google/palm-2-codechat-bison', messages)`
openrouter/meta-llama/llama-2-13b-chat	`completion('openrouter/meta-llama/llama-2-13b-chat', messages)`
openrouter/meta-llama/llama-2-70b-chat	`completion('openrouter/meta-llama/llama-2-70b-chat', messages)`

Passing OpenRouter Params - transforms, models, route

Pass transforms, models, routeas arguments to litellm.completion()

import os
from litellm import completion

os.environ["OPENROUTER_API_KEY"] = ""

response = completion(
            model="openrouter/google/palm-2-chat-bison",
            messages=messages,
            transforms = [""],
            route= ""
        )

Embedding

from litellm import embedding
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

response = embedding(
    model="openrouter/openai/text-embedding-3-small",
    input=["good morning from litellm", "this is another item"],
)
print(response)

Image Generation

OpenRouter supports image generation through select models like Google Gemini image generation models. LiteLLM transforms standard image generation requests to OpenRouter's chat completion format.

Supported Parameters

size: Maps to OpenRouter's aspect_ratio format
- 1024x1024 → 1:1 (square)
- 1536x1024 → 3:2 (landscape)
- 1024x1536 → 2:3 (portrait)
- 1792x1024 → 16:9 (wide landscape)
- 1024x1792 → 9:16 (tall portrait)
quality: Maps to OpenRouter's image_size format (Gemini models)
- low or standard → 1K
- medium → 2K
- high or hd → 4K
n: Number of images to generate

Usage

from litellm import image_generation
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

# Basic image generation
response = image_generation(
    model="openrouter/google/gemini-2.5-flash-image",
    prompt="A beautiful sunset over a calm ocean",
)
print(response)

Advanced Usage with Parameters

from litellm import image_generation
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

# Generate high-quality landscape image
response = image_generation(
    model="openrouter/google/gemini-2.5-flash-image",
    prompt="A serene mountain landscape with a lake",
    size="1536x1024",  # Landscape format
    quality="high",     # High quality (4K)
)

# Access the generated image
image_data = response.data[0]
if image_data.b64_json:
    # Base64 encoded image
    print(f"Generated base64 image: {image_data.b64_json[:50]}...")
elif image_data.url:
    # Image URL
    print(f"Generated image URL: {image_data.url}")

Using OpenRouter-Specific Parameters

You can also pass OpenRouter-specific parameters directly using image_config:

from litellm import image_generation
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

response = image_generation(
    model="openrouter/google/gemini-2.5-flash-image",
    prompt="A futuristic cityscape at night",
    image_config={
        "aspect_ratio": "16:9",  # OpenRouter native format
        "image_size": "4K"       # OpenRouter native format
    }
)
print(response)

Response Format

The response follows the standard LiteLLM ImageResponse format:

{
    "created": 1703658209,
    "data": [{
        "b64_json": "iVBORw0KGgoAAAANSUhEUgAA...",  # Base64 encoded image
        "url": None,
        "revised_prompt": None
    }],
    "usage": {
        "input_tokens": 10,
        "output_tokens": 1290,
        "total_tokens": 1300
    }
}

Cost Tracking

OpenRouter provides cost information in the response, which LiteLLM automatically tracks:

response = image_generation(
    model="openrouter/google/gemini-2.5-flash-image",
    prompt="A cute baby sea otter",
)

# Cost is available in the response metadata
print(f"Request cost: ${response._hidden_params['additional_headers']['llm_provider-x-litellm-response-cost']}")

Image Edit

OpenRouter supports image editing through select models like Google Gemini image models. LiteLLM routes image edit requests to OpenRouter's chat completions endpoint with the source image sent as a base64 data URL and modalities: ["image", "text"].

Supported Models

Model	Description
`openrouter/google/gemini-2.5-flash-image`	Gemini 2.5 Flash with image editing

See all available image models on OpenRouter's model list.

Supported Parameters

Parameter	OpenRouter Mapping	Notes
`size`	`image_config.aspect_ratio`	`1024x1024` → `1:1`, `1536x1024` → `3:2`, `1024x1536` → `2:3`, `1792x1024` → `16:9`, `1024x1792` → `9:16`
`quality`	`image_config.image_size`	`low`/`standard` → `1K`, `medium` → `2K`, `high`/`hd` → `4K`
`n`	`n`	Number of images

note

quality=high (4K) is only supported by google/gemini-3-pro-image-preview and google/gemini-3.1-flash-image-preview. The google/gemini-2.5-flash-image model supports up to medium (2K).

Usage

from litellm import image_edit
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

# Basic image edit
response = image_edit(
    model="openrouter/google/gemini-2.5-flash-image",
    image=open("original_image.png", "rb"),
    prompt="Make the sky a vibrant purple sunset",
)

print(response)

Advanced Usage with Parameters

from litellm import image_edit
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

# Edit with size and quality parameters
response = image_edit(
    model="openrouter/google/gemini-2.5-flash-image",
    image=open("photo.png", "rb"),
    prompt="Add northern lights to the sky",
    size="1536x1024",   # Maps to aspect_ratio 3:2
    quality="high",      # Maps to image_size 4K
)

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

Multiple Images Edit

from litellm import image_edit
import os

os.environ["OPENROUTER_API_KEY"] = "your-api-key"

response = image_edit(
    model="openrouter/google/gemini-2.5-flash-image",
    image=[
        open("scene.png", "rb"),
        open("style_reference.png", "rb"),
    ],
    prompt="Blend the reference style into the scene",
)

print(response)

Usage​

Configuration with Environment Variables​

OpenRouter Completion Models​

Passing OpenRouter Params - transforms, models, route​

Embedding​

Image Generation​

Supported Parameters​

Usage​

Advanced Usage with Parameters​

Using OpenRouter-Specific Parameters​

Response Format​

Cost Tracking​

Image Edit​

Supported Models​

Supported Parameters​

Usage​

Advanced Usage with Parameters​

Multiple Images Edit​

Usage

Configuration with Environment Variables

OpenRouter Completion Models

Passing OpenRouter Params - transforms, models, route

Embedding

Image Generation

Supported Parameters

Usage

Advanced Usage with Parameters

Using OpenRouter-Specific Parameters

Response Format

Cost Tracking

Image Edit

Supported Models

Supported Parameters

Usage

Advanced Usage with Parameters

Multiple Images Edit