Molmo2 Image Qa

Playground

Molmo2-4B Image QA: Answer questions about images with support for multi-image comparison (1-2 images). Open-source vision-language model. Ready-to-use REST API, no cold starts, affordable pricing.

Features

Molmo2 Image QA

Ask questions about images and get intelligent answers with Molmo2 Image QA. This vision-language model analyzes single or multiple images and responds to natural language queries — perfect for image understanding, visual analysis, and automated image-based workflows.

Why It Works Great

Multi-image support: Analyze and compare multiple images at once.
Natural language: Ask questions in plain English.
Visual understanding: Comprehends objects, scenes, text, and relationships.
Instant answers: Fast processing for real-time applications.
Ultra-affordable: Just $0.002 per query — 500 queries for $1.
Versatile analysis: From simple identification to complex reasoning.

Parameters

Parameter	Required	Description
images	Yes	One or more images to analyze (upload or public URLs).
text	Yes	Your question or prompt about the image(s).

How to Use

Upload image(s) — drag and drop or paste public URLs.
Click ”+ Add Item” — to add additional images for comparison.
Enter your question — describe what you want to know.
Run — click the button to get your answer.

Pricing

Flat rate per query.

Output	Cost
Per query	$0.002
100 queries	$0.20
1,000 queries	$2.00

Best Use Cases

Image Analysis — Describe what’s in an image in detail.
Object Identification — Identify objects, people, or elements.
Text Extraction — Read and transcribe text visible in images.
Comparison — Compare multiple images for differences or similarities.
Quality Assessment — Evaluate image quality or content.
Data Extraction — Pull structured information from visual content.

Example Questions

“What objects are in this image?”
“Describe the scene in detail.”
“What text is visible in this image?”
“How do these two images differ?”
“What is the dominant color in this photo?”
“Is there a person in this image? What are they doing?”
“What brand logo is shown?”
“Count the number of items on the table.”

Pro Tips for Best Results

Be specific with your questions for more precise answers.
Upload multiple images to compare or analyze together.
Use for OCR tasks — the model can read text in images.
At $0.002 per query, batch processing is extremely cost-effective.
Combine with other Molmo2 tools for comprehensive image workflows.
Ask follow-up questions about the same images for deeper analysis.

Notes

Supports multiple images in a single query.
If using URLs, ensure they are publicly accessible.
Processing is near-instant for most queries.
Works with photos, screenshots, diagrams, and more.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/image-qa" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "enable_sync_mode": false
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

Parameter	Type	Required	Default	Range	Description
images	array	Yes	[]	1 ~ 2 items	Array of image URLs for question answering (1-2 images). Supports common image formats (JPEG, PNG, WebP).
text	string	Yes	-	-	Your question about the image(s).
enable_sync_mode	boolean	No	false	-	If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.

Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
data.id	string	Unique identifier for the prediction, Task Id
data.model	string	Model ID used for the prediction
data.outputs	array	Array of URLs to the generated content (empty when status is not `completed`)
data.urls	object	Object containing related API endpoints
data.urls.get	string	URL to retrieve the prediction result
data.has_nsfw_contents	array	Array of boolean values indicating NSFW detection for each output
data.status	string	Status of the task: `created`, `processing`, `completed`, or `failed`
data.created_at	string	ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.error	string	Error message (empty if no error occurred)
data.timings	object	Object containing timing details
data.timings.inference	integer	Inference time in milliseconds

Result Request Parameters

Parameter	Type	Required	Default	Description
id	string	Yes	-	Task ID

Result Response Parameters

Parameter	Type	Description
code	integer	HTTP status code (e.g., 200 for success)
message	string	Status message (e.g., “success”)
data	object	The prediction data object containing all details
data.id	string	Unique identifier for the prediction, the ID of the prediction to get
data.model	string	Model ID used for the prediction
data.outputs	string	Array of URLs to the generated content (empty when status is not completed).
data.urls	object	Object containing related API endpoints
data.urls.get	string	URL to retrieve the prediction result
data.status	string	Status of the task: `created`, `processing`, `completed`, or `failed`
data.created_at	string	ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.error	string	Error message (empty if no error occurred)
data.timings	object	Object containing timing details
data.timings.inference	integer	Inference time in milliseconds

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