Run any VLM (Video Language Model) with fal, powered by OpenRouter.

openrouter/router/video/enterprise

Run any VLM (Video Language Model) with fal, powered by OpenRouter.

Inference

Commercial use

Streaming

Partner

Schema

LLMs

Playground API

Input

Video Urls

Hint: Drag and drop files from your computer, videos from web pages, paste from clipboard (Ctrl/Cmd+V), or provide a URL.

Video 1

Video 2

2 videos added

Prompt*

Type # to reference inputs.

System Prompt

Model*

Reasoning

Additional Settings

Customize your input with more control.

You will be charged based on the number of input and output tokens.

Streaming

Result

Idle

that's the way I look at it and I don't know what you would say. Sooner or later the child gets run over.
They seem to be too local, too provincial.

What would you like to do next?

{
  "output": "that's the way I look at it and I don't know what you would say. Sooner or later the child gets run over.\nThey seem to be too local, too provincial.",
  "usage": {
    "prompt_tokens": 1000,
    "cost": 0.0005,
    "total_tokens": 1100,
    "completion_tokens": 100
  }
}

You will be charged based on the number of input and output tokens.

🧠 Using OpenRouter API

The OpenRouter API provides unified access to a wide range of large language models — including GPT, Claude, Gemini, and many others through a single API interface. Billed through fal.

🚀 Usage with OpenAI Client

python
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Write a short story (under 200 words) about an AI that learns to dream. Use vivid sensory details and end with a surprising twist that makes the reader feel both awe and melancholy."},
    ],
)

print(response.choices[0].message.content)

🚿 Streaming Example

python
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

stream = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."},
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta:
        print(chunk.choices[0].delta.content, end="", flush=True)

📚 Documentation

For more details, visit the official docs:

🔗 OpenRouter API Docs
⚡ fal.ai API Docs

🧠 Using OpenRouter API

The OpenRouter API provides unified access to a wide range of large language models — including GPT, Claude, Gemini, and many others through a single API interface. Billed through fal.

🚀 Usage with OpenAI Client

python
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Write a short story (under 200 words) about an AI that learns to dream. Use vivid sensory details and end with a surprising twist that makes the reader feel both awe and melancholy."},
    ],
)

print(response.choices[0].message.content)

🚿 Streaming Example

python
from openai import OpenAI
import os

client = OpenAI(
    base_url="https://fal.run/openrouter/router/openai/v1",
    api_key="not-needed",
    default_headers={
        "Authorization": f"Key {os.environ['FAL_KEY']}",
    },
)

stream = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."},
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices and chunk.choices[0].delta:
        print(chunk.choices[0].delta.content, end="", flush=True)

📚 Documentation

For more details, visit the official docs:

🔗 OpenRouter API Docs
⚡ fal.ai API Docs

openrouter/router/video/enterprise, details and endpoints

openrouter/router/video/enterprise

Input

Result

What would you like to do next?

🧠 Using OpenRouter API

🚀 Usage with OpenAI Client

🚿 Streaming Example

📚 Documentation

🧠 Using OpenRouter API

🚀 Usage with OpenAI Client

🚿 Streaming Example

📚 Documentation