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Molmo2-4B Image Content Moderator: Analyze image content for safety, appropriateness, and policy compliance. Detects violence, nudity, gore, and other harmful visual content. Open-source vision-language model. Ready-to-use REST API, no cold starts, affordable pricing.

content-moderation
Input

Idle

wavespeed-ai/molmo2/image-content-moderator preview unavailable

$0.003per run·~333 / $1

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README

Molmo2 Image Content Moderator

Automatically screen images for harmful content with Molmo2 Image Content Moderator. This AI-powered moderation tool analyzes images and returns safety classifications for harassment, hate speech, sexual content, and violence — essential for content platforms, user-generated content, and compliance workflows.

Need video moderation? Try Molmo2 Video Content Moderator for video analysis.

Why It Works Great

  • Comprehensive detection: Screens for harassment, hate, sexual content, and violence.
  • Child safety: Dedicated detection for content involving minors.
  • Instant results: Fast processing with results in seconds.
  • JSON output: Clean, structured results for easy integration.
  • Ultra-affordable: Just $0.003 per image — 333 images for $1.
  • Custom criteria: Optional text input for additional context.

Parameters

ParameterRequiredDescription
imageYesImage to analyze (upload or public URL).
textNoOptional context or custom moderation criteria.

How to Use

  1. Upload your image — drag and drop or paste a public URL.
  2. Add context (optional) — provide additional text for custom criteria.
  3. Run — click the button to analyze.
  4. Review results — check the JSON output for safety classifications.

Pricing

Flat rate per image analyzed.

OutputCost
Per image$0.003
100 images$0.30
1,000 images$3.00

Output Format

The model returns a JSON object with boolean flags for each content category:

{
 "harassment": false,
 "hate": false,
 "sexual": false,
 "sexual/minors": false,
 "violence": false
}

Detection Categories

CategoryDescription
harassmentBullying, intimidation, or targeted abuse
hateHate speech, discrimination, or prejudice
sexualAdult sexual content or nudity
sexual/minorsAny sexual content involving minors
violenceGraphic violence, gore, or harmful imagery

Best Use Cases

  • Content Platforms — Screen user-uploaded images before publishing.
  • Social Media — Moderate image content at scale.
  • E-commerce — Review product images for policy compliance.
  • Dating Apps — Filter inappropriate profile photos.
  • Forums & Communities — Ensure uploaded images meet guidelines.
  • Pre-screening — Filter content before human review.

Pro Tips for Best Results

  • Use as a first-pass filter before human moderation for edge cases.
  • Integrate via API for automated moderation pipelines.
  • At $0.003 per image, batch processing is extremely cost-effective.
  • Combine with video moderation for comprehensive content screening.
  • Use the text parameter to provide context for borderline content.

Notes

  • If using a URL, ensure it is publicly accessible.
  • Processing is near-instant for most images.
  • Returns boolean values — true indicates detected content.
  • Designed for automated workflows with JSON output.
  • Consider human review for flagged content or edge cases.
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 Image Content Moderator API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/image-content-moderator 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 Image Content Moderator below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}
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/image-content-moderator" \
  -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/image-content-moderator";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}

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/image-content-moderator", 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 Image Content Moderator API — Frequently asked questions

What is the Molmo2 Image Content Moderator API?

Molmo2 Image Content Moderator is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Molmo2-4B Image Content Moderator: Analyze image content for safety, appropriateness, and policy compliance. Detects violence, nudity, gore, and other harmful visual content. Open-source vision-language model. Ready-to-use REST API, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Molmo2 Image Content Moderator 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-image-content-moderator.

How much does Molmo2 Image Content Moderator cost per run?

Molmo2 Image Content Moderator starts at $0.003 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 Image Content Moderator accept?

Key inputs: `image`, `enable_sync_mode`, `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-image-content-moderator.

How long does Molmo2 Image Content Moderator take to generate?

Median end-to-end generation time on WaveSpeedAI is around 17 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 Image Content Moderator 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 Image Content Moderator | AI Content Moderation API on WaveSpeedAI