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Molmo2-4B Video Captioner: Generate detailed, accurate captions for videos with customizable detail levels (low, medium, high). Open-source vision-language model with temporal understanding capabilities. Ready-to-use REST API, no cold starts, duration-based pricing.

video-to-text
Input

Idle

In a futuristic cityscape, three red helicopters fly through the air, leaving trails of smoke behind them. The tallest building resembles the Empire State Building, while the others are shorter. The sky is a clear blue, and the scene is filled with tall, gray skyscrapers.

$0.005per run·~200 / $1

ExamplesView all

Related Models

README

Molmo2 Video Captioner

Molmo2 Video Captioner is an intelligent video understanding model that generates detailed captions and descriptions for video content. Upload a video and receive natural-language descriptions of scenes, actions, objects, and events — with adjustable detail levels to match your workflow needs.

Perfect for content creators, accessibility teams, and developers building video understanding pipelines.

Why Choose This?

  • Adjustable detail levels Choose from low, medium, or high detail to control caption depth — from quick summaries to comprehensive scene breakdowns.

  • Scene-aware captioning Understands context, actions, objects, environments, and temporal flow to produce coherent, meaningful descriptions.

  • Flexible video input Accepts video uploads or public URLs for seamless integration into existing workflows.

  • Fast processing Optimized for quick turnaround while maintaining caption accuracy and coherence.

  • Production-ready API Ready-to-use REST endpoint with predictable per-second pricing and no cold starts.

Parameters

ParameterRequiredDescription
videoYesInput video (upload or public URL)
detail_levelNoCaption detail: low, medium (default), or high

Detail Level Options

  • Low — Brief, high-level summary of the video content
  • Medium — Balanced description with key scenes and actions (default)
  • High — Comprehensive breakdown with fine-grained details

How to Use

  1. Upload your video — drag and drop a file or paste a public video URL.
  2. Select detail level — choose low, medium, or high based on your needs.
  3. Submit — the model processes the video and returns a caption.
  4. Use the output — integrate captions into your content, accessibility tools, or data pipelines.

Pricing

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

Video DurationCost
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

  • Accessibility — Generate video descriptions for visually impaired users and screen readers.
  • Content indexing — Create searchable metadata for video libraries and archives.
  • Social media — Auto-generate captions for posts, reels, and stories.
  • Video SEO — Improve discoverability with rich text descriptions for video content.
  • Surveillance and monitoring — Summarize footage for quick review and logging.
  • Education — Describe instructional videos for enhanced learning materials.

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 yields the most accurate captions.
  • Use high detail level for complex scenes; low detail for quick overviews.

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 Captioner API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/video-captioner 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 Captioner 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",
    "detail_level": "medium"
}
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-captioner" \
  -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-captioner";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
        "detail_level": "medium"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "detail_level": "medium"
}

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-captioner", 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 Captioner API — Frequently asked questions

What is the Molmo2 Video Captioner API?

Molmo2 Video Captioner is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Molmo2-4B Video Captioner: Generate detailed, accurate captions for videos with customizable detail levels (low, medium, high). Open-source vision-language model with temporal understanding capabilities. 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 Captioner 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-captioner.

How much does Molmo2 Video Captioner cost per run?

Molmo2 Video Captioner 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 Captioner accept?

Key inputs: `video`, `detail_level`. 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-captioner.

How long does Molmo2 Video Captioner take to generate?

Median end-to-end generation time on WaveSpeedAI is around 7 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 Captioner 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 Captioner | AI Video Understanding API on WaveSpeedAI