Moondream3 Point finds objects in images and returns precise coordinate points for computer vision tasks, enabling accurate point localization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Boşta
{
"answer": "The woman is wearing a pink baseball cap with a strap across her forehead. She is also wearing large silver hoop earrings and a pink fuzzy sweater. Her blonde hair is styled in loose waves, and she has her tongue sticking out slightly while looking directly at the camera. Behind her, there are several posters visible, including one with a pink background and an image of a cup."
}$0.001çalıştırma başına·~1000 / $1
hat
person
wall
picture
mirror
person
castle
ice-cream
flower
Moondream 3 Point is a vision-language model designed to identify and describe specific objects within an image using natural language. Instead of returning coordinates, it provides a concise textual description of the detected object, making it ideal for lightweight interactive queries and content understanding.
Locate and Describe Objects Enter a short text query (e.g., “hat”, “watch”, “phone”) and receive a natural-language description of that item in context.
Fast Single-Object Queries Optimized for fast, low-latency inference — perfect for real-time applications.
Readable Natural Output The model outputs a fluent English sentence describing the object’s appearance, position, and context.
Multilingual Understanding Capable of recognizing and describing objects in a wide range of visual scenarios.
{
"image": "https://example.com/photo.jpg",
"prompt": "hat"
}
{
"answer": "The woman is wearing a pink baseball cap with a strap across her forehead. She is also wearing large silver hoop earrings and a pink fuzzy sweater."
}
Use concise object names (e.g., “hat”, “car”, “tree”) for more accurate detection.
For precise bounding boxes or coordinates, use:
Moondream 3 Detect — returns x_min, y_min, x_max, y_max bounding boxes.
A coordinate-enabled version of Moondream 3 Point (coming soon).
Supported formats: JPEG, PNG, WebP
Maximum image size: 10 MB
{"answer": "..."}
— it does not output coordinates.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/point 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 Moondream3 Preview Point below.
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/point" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"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].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/moondream3-preview/point", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"enable_sync_mode": false
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/moondream3-preview/point",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://example.com/your-input.jpg",
"enable_sync_mode": false
}
)
print(output["outputs"][0]) # → URL of the generated outputMoondream3 Preview Point is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Moondream3 Point finds objects in images and returns precise coordinate points for computer vision tasks, enabling accurate point localization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
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/moondream3-preview-point.
Moondream3 Preview Point starts at $0.001 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.
Key inputs: `prompt`, `image`, `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/moondream3-preview-point.
Average end-to-end generation time on WaveSpeedAI is around 5 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.
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.