aiqbench analysis
This article details an asynchronous API architecture that unifies access to multiple video generation models (Seedance, Veo, Wan) under a single endpoint, simplifying integration for developers. By decoupling the request submission from the long-running generation process via job IDs and polling, it addresses critical infrastructure challenges like serverless timeouts and session management in AI video workflows.
OpenRouter Video Generation API: A Code-First Guide

Every video provider has its own endpoint, job statuses, polling logic, and output format. Our async video API puts Seedance, Veo, Wan, and more behind one submit, poll, and download loop. Here's the full lifecycle in Python and TypeScript.
Adding video generation to an application is straightforward when you’re testing one model. The complexity shows up when you want to try another. Each provider can have its own endpoint, request parameters, job statuses, polling logic, and output format. That turns a simple model change into another integration to build and maintain. We put that workflow behind one asynchronous video API. You submit a prompt to POST /api/v1/videos, receive a job ID, poll until generation completes, and then download the finished video. In this guide, we’ll build that flow from start to finish. We’ll submit a job with Seedance, poll it safely, save the MP4, and then run the same integration with Veo and Wan. One endpoint, multiple video models. Generate with Seedance, Veo, Wan, and other supported models through POST /api/v1/videos. The workflow is asynchronous. Submit the job, poll its status, then download the completed video. Switch models by changing the model identifier. Some model-specific settings, such as duration and aspect ratio, still need adjusting per model, more on that in Step 4, but the endpoint, auth, polling loop, and download logic never change. Video generation takes longer than a typical API response. A model has to generate and…
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