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OpenRouter [Video] Video to Text

openrouter/router/video
Run any VLM (Video Language Model) with fal, powered by OpenRouter.
Inference
Commercial use
Streaming
Schema
LLMs
PlaygroundAPI

About

Run any video-capable LLM with fal, powered by OpenRouter.

Process video files (analysis, summarization, etc.) using models that support video input. Video files can be provided as URLs, YouTube links, or data URIs. Supported formats: mp4, mpeg, mov, webm.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("openrouter/router/video", {
  input: {
    prompt: "Please transcribe the videos respectively.",
    model: "google/gemini-2.5-flash"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

Streaming#

This model supports streaming requests. You can stream data directly to the model and get the result in real-time.

import { fal } from "@fal-ai/client";

const stream = await fal.stream("openrouter/router/video", {
  input: {
    prompt: "Please transcribe the videos respectively.",
    model: "google/gemini-2.5-flash"
  }
});

for await (const event of stream) {
  console.log(event);
}

const result = await stream.done();

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("openrouter/router/video", {
  input: {
    prompt: "Please transcribe the videos respectively.",
    model: "google/gemini-2.5-flash"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("openrouter/router/video", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("openrouter/router/video", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Read more about file handling in our file upload guide.

5. Schema#

Input#

video_urls list<string>

List of URLs or data URIs of video files to process. Supported formats: mp4, mpeg, mov, webm. For Google Gemini on AI Studio, YouTube links are also supported. Mutually exclusive with video_url.

prompt string* required

Prompt to be used for the video processing

system_prompt string

System prompt to provide context or instructions to the model

model string* required

Name of the model to use. Charged based on actual token usage.

reasoning boolean

Should reasoning be the part of the final answer.

temperature float

This setting influences the variety in the model's responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input. Default value: 1

max_tokens integer

This sets the upper limit for the number of tokens the model can generate in response. It won't produce more than this limit. The maximum value is the context length minus the prompt length.

{
  "video_urls": [
    "https://v3b.fal.media/files/b/0a8b3081/t4Jsy53x-Q8iQqg78_Vj__vid01.mp4",
    "https://v3b.fal.media/files/b/0a8b3085/xWtbpb6pf4i-BSvR2oWbi_vid06.mp4"
  ],
  "prompt": "Please transcribe the videos respectively.",
  "system_prompt": "Please look at the videos in order and answer the question.",
  "model": "google/gemini-2.5-flash",
  "temperature": 1
}

Output#

output string* required

Generated output from video processing

usage UsageInfo

Token usage information

{
  "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,
    "total_tokens": 1100,
    "completion_tokens": 100,
    "cost": 0.0005
  }
}

Other types#

UsageInfo#

prompt_tokens integer
completion_tokens integer
total_tokens integer
cost float* required

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