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Molmo2-4B Video Understanding: Analyze videos with specialized tasks (general, summary, analysis, counting, scene description). Open-source vision-language model with temporal understanding. Ready-to-use REST API, no cold starts, duration-based pricing.

video-to-text
Input

Idle

Counting the <points coords="0.0 1 296 419 2 411 479 3 458 469 4 484 469 5 666 469 6 711 499 7 740 419 8 855 479 9 966 549">people in the video</points> shows a total of 9.

$0.005per run·~200 / $1

ExamplesView all

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README

Molmo2 Video Understanding

Analyze and understand video content with Molmo2 Video Understanding. This intelligent video analysis model performs various tasks including summarization, scene description, object counting, and detailed analysis — perfect for video cataloging, content moderation, and automated video workflows.

Why It Works Great

  • Multiple task modes: Summary, analysis, counting, scene description, and general Q&A.
  • Custom instructions: Add specific focus areas or questions.
  • Extended video support: Analyze videos up to 2 minutes long.
  • Structured output: Get organized, task-specific results.
  • Affordable: Starting at just $0.005 per video.
  • Versatile analysis: From quick summaries to detailed breakdowns.

Parameters

ParameterRequiredDescription
videoYesVideo to analyze (upload or public URL).
taskNoAnalysis type: general, summary, analysis, counting, or scene_description. Default: general.
textNoAdditional instructions or focus areas for the analysis.

How to Use

  1. Upload your video — drag and drop or paste a public URL.
  2. Select task — choose the type of analysis you need.
  3. Add instructions (optional) — specify focus areas or custom questions.
  4. Run — click the button to analyze.
  5. Review results — get structured analysis output.

Pricing

Per 5-second billing with minimum charge for videos ≤5 seconds. Maximum billable duration is 120 seconds.

DurationCost
≤5 seconds$0.005
10 seconds$0.01
30 seconds$0.03
60 seconds$0.06
120 seconds (max)$0.12

Task Modes

TaskDescriptionBest For
generalOpen-ended video understanding and Q&ACustom questions, flexible analysis
summaryConcise overview of video contentQuick content review, cataloging
analysisDetailed breakdown of video elementsIn-depth understanding, reports
countingCount objects, people, or eventsInventory, crowd analysis, metrics
scene_descriptionDescribe scenes and visual elementsAccessibility, content tagging

Best Use Cases

  • Video Cataloging — Automatically generate descriptions for video libraries.
  • Content Moderation — Analyze video content for review workflows.
  • Accessibility — Create text descriptions for visually impaired users.
  • Search & Discovery — Generate metadata for video search systems.
  • Analytics — Count objects, people, or events in footage.
  • Summarization — Create quick summaries for long-form content.

Example Instructions

  • "Focus on the people in the video and describe their actions."
  • "Count how many cars appear in this video."
  • "Summarize the main events in chronological order."
  • "Describe the setting and atmosphere of each scene."
  • "Identify any text or logos visible in the video."
  • "What products are being demonstrated?"

Pro Tips for Best Results

  • Choose the appropriate task mode for your specific need.
  • Use "text" parameter to focus analysis on specific elements.
  • Combine "general" task with custom questions for flexible Q&A.
  • Use "counting" for metrics like people, objects, or occurrences.
  • "scene_description" works great for accessibility and content tagging.
  • Keep videos under 2 minutes for optimal processing.

Notes

  • Maximum supported video duration is 120 seconds (2 minutes).
  • If using a URL, ensure it is publicly accessible.
  • Processing time scales with video length.
  • Different tasks produce different output formats optimized for their purpose.

Duration limit

The maximum supported video duration is 2 minutes.

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Molmo2 Video Understanding API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-understanding with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Molmo2 Video Understanding below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "task": "general"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-understanding" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-understanding";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "task": "general"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
    "task": "general"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-understanding", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Molmo2 Video Understanding API — Frequently asked questions

What is the Molmo2 Video Understanding API?

Molmo2 Video Understanding is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Molmo2-4B Video Understanding: Analyze videos with specialized tasks (general, summary, analysis, counting, scene description). Open-source vision-language model with temporal understanding. 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 Understanding 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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/molmo2-video-understanding.

How much does Molmo2 Video Understanding cost per run?

Molmo2 Video Understanding 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 Understanding accept?

Key inputs: `video`, `task`, `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-understanding.

How long does Molmo2 Video Understanding take to generate?

Median end-to-end generation time on WaveSpeedAI is around 31 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Molmo2 Video Understanding 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.

Molmo2 Video Understanding | AI Video Understanding API on WaveSpeedAI