Molmo2 Video Qa

Molmo2 Video QA is a powerful video understanding model that answers questions about video content. Simply upload a video and ask anything — the model analyzes visual scenes, actions, objects, and context to deliver accurate, natural-language responses.

Built for developers and creators who need intelligent video comprehension without building complex pipelines.

Why Choose This?

Natural language understanding Ask questions in plain English about what happens in your video — no need for timestamps or frame-by-frame annotation.
Scene and action recognition Understands objects, people, movements, environments, and temporal sequences across the video.
Flexible video input Accepts video uploads or public URLs for seamless integration into existing workflows.
Fast and accurate Optimized for quick turnaround while maintaining high comprehension accuracy.
Production-ready API Ready-to-use REST endpoint with predictable per-second pricing and no cold starts.

How to Use

Upload your video — drag and drop a file or paste a public video URL.
Write your question — describe what you want to know about the video content.
Submit — the model processes the video and returns a natural-language answer.
Iterate — ask follow-up questions or upload new videos as needed.

Pricing

Per-5-second billing with a 5-second minimum.

Video Duration	Cost
Up to 5s	$0.005
10s	$0.01
30s	$0.03
60s	$0.06
120s (max)	$0.12

Billing Rules

Minimum charge: 5 seconds ($0.005)
Rate: $0.001 per second ($0.005 per 5 seconds)
Maximum video length: 120 seconds (2 minutes)

Best Use Cases

Content moderation — Automatically review video uploads for policy compliance.
Video search and indexing — Extract semantic information for searchable video libraries.
Accessibility — Generate descriptions of video content for visually impaired users.
Education and training — Analyze instructional videos and answer learner questions.
Surveillance and monitoring — Summarize events or detect specific actions in footage.
Social media analytics — Understand trends and content themes across video posts.

Notes

If using a URL, ensure it is publicly accessible. A preview thumbnail in the interface confirms successful access.
For videos longer than 2 minutes, split into segments and process separately.
Clear, well-lit footage with minimal background noise yields the best results.
Be specific in your questions for more precise answers.

Molmo2 Video Qa API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-qa with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Molmo2 Video Qa below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-qa" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "video": "https://example.com/your-input.mp4"
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/molmo2/video-qa", {
        "video": "https://example.com/your-input.mp4"
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/molmo2/video-qa",
    {
    "video": "https://example.com/your-input.mp4"
}
)

print(output["outputs"][0])  # → URL of the generated output

Molmo2 Video Qa API — Frequently asked questions

What is the Molmo2 Video Qa API?

Molmo2 Video Qa is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Molmo2-4B Video QA: Answer questions about video content with temporal understanding. Open-source vision-language model. Ready-to-use REST API, no cold starts, duration-based pricing. You can call it programmatically or try it from the playground above.

How do I call the Molmo2 Video Qa API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/molmo2-video-qa.

How much does Molmo2 Video Qa cost per run?

Molmo2 Video Qa starts at $0.005 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Molmo2 Video Qa accept?

Key inputs: `video`, `text`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/molmo2-video-qa.

How long does Molmo2 Video Qa take to generate?

Average end-to-end generation time on WaveSpeedAI is around 10 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Molmo2 Video Qa outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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Molmo2 Video QA