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Molmo2-4B Image Captioner: Generate detailed, accurate captions for images with customizable detail levels (low, medium, high). Open-source vision-language model with object grounding capabilities. Ready-to-use REST API, no cold starts, affordable pricing.

image-to-text
Input

Idle

A pixelated landscape poster showcases a magnifying glass over a mountain scene. The poster features various pixelated images, including a woman, a man, and a camera. The design has a retro, video game-inspired aesthetic with vibrant colors and a nostalgic feel.

$0.002per run·~500 / $1

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README

Molmo2 Image Captioner

Molmo2 Image Captioner is an intelligent image understanding model that generates detailed captions and descriptions for any image. Upload an image and receive natural-language descriptions of scenes, objects, people, and context — with adjustable detail levels to match your workflow needs.

Perfect for content creators, accessibility teams, and developers building image 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.

  • Rich visual understanding Understands context, objects, people, text, environments, and spatial relationships to produce coherent, meaningful descriptions.

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

  • Fast and affordable Optimized for quick turnaround at just $0.002 per image.

  • Production-ready API Ready-to-use REST endpoint with simple flat-rate pricing and no cold starts.

Parameters

ParameterRequiredDescription
imageYesInput image (upload or public URL)
detail_levelNoCaption detail: low, medium (default), or high

Detail Level Options

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

How to Use

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

Pricing

ItemCost
Per image$0.002

Simple flat-rate pricing — no hidden fees or complex calculations.

Best Use Cases

  • Accessibility — Generate image descriptions for visually impaired users and screen readers.
  • Content indexing — Create searchable metadata for image libraries and archives.
  • Social media — Auto-generate alt text and captions for posts.
  • Image SEO — Improve discoverability with rich text descriptions for visual content.
  • E-commerce — Automatically describe product images for catalogs.
  • Education — Describe visual materials for enhanced learning resources.

Notes

  • If using a URL, ensure it is publicly accessible. A preview thumbnail in the interface confirms successful access.
  • Clear, well-lit images yield the most accurate captions.
  • Use high detail level for complex scenes; low detail for quick overviews.
  • Supports common image formats including JPEG, PNG, and WebP.
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 Captioner API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/image-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 Image Captioner 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",
    "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/image-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/image-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "detail_level": "medium"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "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/image-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 Image Captioner API — Frequently asked questions

What is the Molmo2 Image Captioner API?

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

How much does Molmo2 Image Captioner cost per run?

Molmo2 Image Captioner starts at $0.002 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 Captioner accept?

Key inputs: `image`, `detail_level`, `enable_sync_mode`. 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-captioner.

How long does Molmo2 Image Captioner take to generate?

Median end-to-end generation time on WaveSpeedAI is around 12 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 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 Image Captioner | AI Image Understanding API on WaveSpeedAI