Ox Alpha video input: API Setup Guide for Multimodal Apps

Ox Alpha is an anonymous reasoning model focused on coding, sustained agentic work, production workloads, and workflows that combine text with visual context. Ox Alpha video input is therefore best understood as a visual-analysis capability rather than a video-generation feature.

OpenRouter lists the model as accepting text, images, and video while returning text. This makes it suitable for tasks such as reviewing a screen recording, identifying events in a demonstration, explaining a visual workflow, or connecting video observations with a software-engineering prompt.

Video Highlights:

• A 1 million-token context is listed for long-horizon tasks.
• Video support is presented alongside image and text input.
• The model is positioned for coding and agentic workflows.
• Audio handling should not be assumed from video support alone.
• Provider identity remains undisclosed during the preview period.

The practical distinction is important: a model can inspect visual frames without necessarily offering speech transcription or audio reasoning. Testing discussed by the community indicates that Ox Alpha rejects audio input, so users should treat a video file as a visual source unless the current API documentation explicitly confirms audio support.

OpenRouter provides an OpenAI-compatible API route for Ox Alpha. The model slug is stealth/ox-alpha, and the provider page shows one directly hosted provider during the preview. The setup pattern is familiar if your application already uses an OpenAI-style client.

The basic workflow has four parts:

A minimal TypeScript pattern can follow the OpenRouter SDK structure:

import { OpenRouter } from "@openrouter/sdk";

const openrouter = new OpenRouter({
  apiKey: process.env.OPENROUTER_API_KEY
});

const response = await openrouter.chat.send({
  model: "stealth/ox-alpha",
  messages: [
    {
      role: "user",
      content: [
        {
          type: "text",
          text: "Review this video and list the major visual events in chronological order."
        },
        {
          type: "video_url",
          video_url: {
            url: "YOUR_VIDEO_URL"
          }
        }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

The exact attachment syntax can vary by SDK implementation. Confirm the current request format in the Ox Alpha OpenRouter model page before deploying an integration.

Video analysis improves when the request defines what to inspect and how to present the result. A broad instruction such as “Explain this video” may produce a general summary, while a structured prompt can request event boundaries, visible objects, UI changes, or code-related observations.

Use the following patterns as starting points:

A reliable prompt usually includes five details:

• Scope: State whether the model should inspect the entire clip or only selected sections.
• Evidence: Ask it to separate visible facts from interpretation.
• Timing: Request approximate timestamps when event order matters.
• Output: Specify bullets, a table, JSON, or another format.
• Uncertainty: Require the model to mark unclear frames instead of presenting guesses as confirmed facts.

Long clips should be divided into logical segments when the task requires detailed frame-by-frame review. Segmenting also makes it easier to retry a failed request and compare the model’s interpretation across portions of the same recording.

OpenRouter lists Ox Alpha with a 1 million-token context and a free price at the time of the supplied 2026-08-22 reference. The same page reports a provider P50 latency of 5.30 seconds and throughput of 23 tokens per second in its displayed measurements. These values are operational snapshots, not guarantees for every location, request size, or workload.

The model is presented as suitable for long-horizon software engineering and production workloads, but video analysis still depends on media accessibility, visual clarity, prompt quality, and the provider’s current implementation.

The anonymous-provider status is central to interpreting Ox Alpha. OpenRouter routes requests to the model but states that it is not the developer, owner, or provider. Community analysis has suggested links to a GLM-family multimodal system based on tokenizer and video-encoder fingerprints, but that remains speculation until an official reveal.

The preview period also means availability and pricing may change. The safest workflow is to monitor the model page, keep a fallback provider or model available, and avoid designing a critical production pipeline around a temporary access condition.

Ox Alpha works best when video is treated as structured visual context for reasoning, coding support, and workflow analysis. Keep prompts specific, separate evidence from assumptions, and validate important conclusions before acting on them.