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

Parameter	Required	Description
image	Yes	Input image (upload or public URL)
detail_level	No	Caption 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

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

Pricing

Item	Cost
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.

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; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Molmo2 Image Captioner below.

HTTP example

# Submit the prediction
curl -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 '{
    "image": "https://example.com/your-input.jpg",
    "detail_level": "medium",
    "enable_sync_mode": false
}'

# 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/image-captioner", {
        "image": "https://example.com/your-input.jpg",
        "detail_level": "medium",
        "enable_sync_mode": false
});

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

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/molmo2/image-captioner",
    {
    "image": "https://example.com/your-input.jpg",
    "detail_level": "medium",
    "enable_sync_mode": false
}
)

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

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 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-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?

Average end-to-end generation time on WaveSpeedAI is around 9 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 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.

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