Runway API

The Runway API allows you to embed Gen-4 Turbo, Gen-4 Image and Image References, or Gen-3 Alpha Turbo within your product inside a safe and reliable environment.

Model output specifications

The API exposes four models:

gen4_turbo Gen-4 Turbo image-to-video model
gen4_image Gen-4 Image and Image References text-to-image and image references model
gen3a_turbo Gen-3 Alpha Turbo image-to-video model
upscale_v1 Upscale

Enterprise Scale

The Runway API has been used by the world’s largest consumer technology companies to reliably generate millions of videos for their users.

To request higher usage than the self-serve tiers, you can submit an exception request on the usage page of your developer portal.

Enterprise Benefits

In addition to higher rate limits, enterprise customers receive:

Faster support via Slack (or email) channels
Earliest access to new features
Direct access to provide product feedback and request features
Implementation and usage tips
Custom payment terms

Price

API video generations match the price of our web application:

Generation Type	Price
720p image	$0.05
1080p image	$0.08
5s video	$0.25
10s video	$0.50
Upscale (per second)	$0.02

Quickstart

Get started by following our API guide or jump right into the example of using the Gen-4 Image References model below:

Node
Python

import RunwayML from '@runwayml/sdk';

const client = new RunwayML();

async function main() {
  // Create a new image generation task using the "gen4_image" model
  let task = await client.textToImage.create({
    model: 'gen4_image',
    ratio: '1920:1080',
    promptText: '@EiffelTower painted in the style of @StarryNight',
    referenceImages: [{
      uri: 'https://upload.wikimedia.org/wikipedia/commons/8/85/Tour_Eiffel_Wikimedia_Commons_(cropped).jpg',
      tag: 'EiffelTower'
    },
    {
      uri: 'https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1513px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg',
      tag: 'StarryNight'
    }]
  });

  do {
    // Wait for 1 second before polling
    await new Promise(resolve => setTimeout(resolve, 1000));

    task = await client.tasks.retrieve(task.id);
  } while (!['SUCCEEDED', 'FAILED'].includes(task.status));

  console.log('Task complete:', task);
  console.log('Image URL:', task.output[0]);
}

main();

import time
from runwayml import RunwayML

client = RunwayML()

task = client.text_to_image.create(
  model='gen4_image',
  ratio='1920:1080',
  prompt_text='@EiffelTower painted in the style of @StarryNight',
  reference_images=[{
    'uri': 'https://upload.wikimedia.org/wikipedia/commons/8/85/Tour_Eiffel_Wikimedia_Commons_(cropped).jpg',
    'tag': 'EiffelTower',
  },
  {
    'uri': 'https://upload.wikimedia.org/wikipedia/commons/thumb/e/ea/Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg/1513px-Van_Gogh_-_Starry_Night_-_Google_Art_Project.jpg',
    'tag': 'StarryNight',
  }],
)
task_id = task.id

# Poll the task until it's complete
time.sleep(1)  # Wait for a second before polling
task = client.tasks.retrieve(task_id)
while task.status not in ['SUCCEEDED', 'FAILED']:
  time.sleep(1)  # Wait for a second before polling
  task = client.tasks.retrieve(task_id)

print('Task complete:', task)
print('Image URL', task.output[0])

Quick references

Using the API Create your first generation with Runway's API

API Reference Read about how to interact with the API

Go-live checklist Prepare your integration for launch

Gen-4 Help Center How to get the best video results