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URL: https://docs.litellm.ai/docs/providers/openrouter

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

👁 Open In Colab

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 NameFunction Call
openrouter/openai/gpt-3.5-turbocompletion('openrouter/openai/gpt-3.5-turbo', messages)
openrouter/openai/gpt-3.5-turbo-16kcompletion('openrouter/openai/gpt-3.5-turbo-16k', messages)
openrouter/openai/gpt-4completion('openrouter/openai/gpt-4', messages)
openrouter/openai/gpt-4-32kcompletion('openrouter/openai/gpt-4-32k', messages)
openrouter/anthropic/claude-2completion('openrouter/anthropic/claude-2', messages)
openrouter/anthropic/claude-instant-v1completion('openrouter/anthropic/claude-instant-v1', messages)
openrouter/google/palm-2-chat-bisoncompletion('openrouter/google/palm-2-chat-bison', messages)
openrouter/google/palm-2-codechat-bisoncompletion('openrouter/google/palm-2-codechat-bison', messages)
openrouter/meta-llama/llama-2-13b-chatcompletion('openrouter/meta-llama/llama-2-13b-chat', messages)
openrouter/meta-llama/llama-2-70b-chatcompletion('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​

ModelDescription
openrouter/google/gemini-2.5-flash-imageGemini 2.5 Flash with image editing

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

Supported Parameters​

ParameterOpenRouter MappingNotes
sizeimage_config.aspect_ratio1024x1024 → 1:1, 1536x1024 → 3:2, 1024x1536 → 2:3, 1792x1024 → 16:9, 1024x1792 → 9:16
qualityimage_config.image_sizelow/standard → 1K, medium → 2K, high/hd → 4K
nnNumber 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
withopen("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)