--- title: "RunwayML - Video Generation" url: "/docs/providers/runwayml/videos" canonical_url: "https://docs.litellm.ai/docs/providers/runwayml/videos" type: "docs" last_updated: "2026-10-02" summary: "LiteLLM supports RunwayML's Gen-4 video generation API, allowing you to generate videos from text prompts and images." related: - "/docs/providers/runwayml/images" - "/docs/providers/s3_vectors" --- # RunwayML - Video Generation > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt LiteLLM supports RunwayML's Gen-4 video generation API, allowing you to generate videos from text prompts and images. ## Quick Start ```python showLineNumbers title="Basic Video Generation" from litellm import video_generation import os os.environ["RUNWAYML_API_KEY"] = "your-api-key" # Generate video from text and image response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) print(f"Video ID: {response.id}") print(f"Status: {response.status}") ``` ## Authentication Set your RunwayML API key: ```python showLineNumbers title="Set API Key" import os os.environ["RUNWAYML_API_KEY"] = "your-api-key" ``` ## Supported Parameters | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `model` | string | Yes | Model to use (e.g., `runwayml/gen4_turbo`) | | `prompt` | string | Yes | Text description for the video | | `input_reference` | string/file | Yes | URL or file path to reference image | | `seconds` | int | No | Video duration (5 or 10 seconds) | | `size` | string | No | Video dimensions (`1280x720` or `720x1280`). Can also use `ratio` format (`1280:720`) | ## Complete Workflow ```python showLineNumbers title="Complete Video Generation Workflow" from litellm import video_generation, video_status, video_content import os import time os.environ["RUNWAYML_API_KEY"] = "your-api-key" # 1. Generate video response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) video_id = response.id print(f"Video generation started: {video_id}") # 2. Check status until completed while True: status_response = video_status(video_id=video_id) print(f"Status: {status_response.status}") if status_response.status == "completed": print("Video generation completed!") break elif status_response.status == "failed": print("Video generation failed") break time.sleep(10) # Wait 10 seconds before checking again # 3. Download video content video_bytes = video_content(video_id=video_id) # 4. Save to file with open("generated_video.mp4", "wb") as f: f.write(video_bytes) print("Video saved successfully!") ``` ## Async Usage ```python showLineNumbers title="Async Video Generation" from litellm import avideo_generation, avideo_status, avideo_content import os import asyncio os.environ["RUNWAYML_API_KEY"] = "your-api-key" async def generate_video(): # Generate video response = await avideo_generation( model="runwayml/gen4_turbo", prompt="A serene lake with mountains in the background", input_reference="https://example.com/lake.jpg", seconds=5, size="1280x720" ) video_id = response.id print(f"Video generation started: {video_id}") # Poll for completion while True: status_response = await avideo_status(video_id=video_id) print(f"Status: {status_response.status}") if status_response.status == "completed": break elif status_response.status == "failed": print("Video generation failed") return await asyncio.sleep(10) # Download video video_bytes = await avideo_content(video_id=video_id) # Save to file with open("generated_video.mp4", "wb") as f: f.write(video_bytes) print("Video saved successfully!") asyncio.run(generate_video()) ``` ## LiteLLM Proxy Usage Add RunwayML to your proxy configuration: ```yaml showLineNumbers title="config.yaml" model_list: - model_name: gen4-turbo litellm_params: model: runwayml/gen4_turbo api_key: os.environ/RUNWAYML_API_KEY ``` Start the proxy: ```bash litellm --config /path/to/config.yaml ``` Generate videos through the proxy: ```bash showLineNumbers title="Proxy Request" curl --location 'http://localhost:4000/v1/videos' \ --header 'Content-Type: application/json' \ --header 'x-litellm-api-key: sk-' \ --data '{ "model": "runwayml/gen4_turbo", "prompt": "A high quality demo video of litellm ai gateway", "input_reference": "https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", "ratio": "1280:720" }' ``` Check video status: ```bash showLineNumbers title="Check Status" curl --location 'http://localhost:4000/v1/videos/{video_id}' \ --header 'x-litellm-api-key: sk-' ``` Download video content: ```bash showLineNumbers title="Download Video" curl --location 'http://localhost:4000/v1/videos/{video_id}/content' \ --header 'x-litellm-api-key: sk-' \ --output video.mp4 ``` ## Supported Models | Model | Description | Duration | Aspect Ratios | |-------|-------------|----------|---------------| | `runwayml/gen4_turbo` | Fast video generation | 5-10s | 1280x720, 720x1280 | ## Error Handling ```python showLineNumbers title="Error Handling" from litellm import video_generation, video_status import time try: response = video_generation( model="runwayml/gen4_turbo", prompt="A scenic mountain view", input_reference="https://example.com/mountain.jpg", seconds=5 ) # Poll for completion max_attempts = 60 # 10 minutes max attempts = 0 while attempts < max_attempts: status_response = video_status(video_id=response.id) if status_response.status == "completed": print("Video generation completed!") break elif status_response.status == "failed": error = status_response.error or {} print(f"Video generation failed: {error.get('message', 'Unknown error')}") break time.sleep(10) attempts += 1 if attempts >= max_attempts: print("Video generation timed out") except Exception as e: print(f"Error: {str(e)}") ``` ## Cost Tracking LiteLLM automatically tracks RunwayML video generation costs: ```python showLineNumbers title="Cost Tracking" from litellm import video_generation, completion_cost response = video_generation( model="runwayml/gen4_turbo", prompt="A high quality demo video of litellm ai gateway", input_reference="https://media.licdn.com/dms/image/v2/D4D0BAQFqOrIAJEgtLw/company-logo_200_200/company-logo_200_200/0/1714076049190/berri_ai_logo?e=2147483647&v=beta&t=7tG_KRZZ4MPGc7Iin79PcFcrpvf5Hu6rBM4ptHGU1DY", seconds=5, size="1280x720" ) # Calculate cost cost = completion_cost(completion_response=response) print(f"Video generation cost: ${cost}") ``` ## API Reference For complete API details, see the [OpenAI Video Generation API specification](https://platform.openai.com/docs/guides/video-generation) which LiteLLM follows. ## Supported Features | Feature | Supported | |---------|-----------| | Video Generation | ✅ | | Image-to-Video | ✅ | | Status Checking | ✅ | | Content Download | ✅ | | Cost Tracking | ✅ | | Logging | ✅ | | Fallbacks | ✅ | | Load Balancing | ✅ | ## Related pages - [RunwayML - Image Generation](https://docs.litellm.ai/docs/providers/runwayml/images.md) - [AWS S3 Vectors](https://docs.litellm.ai/docs/providers/s3_vectors.md)