# Moondream

Canonical page: https://fuser.studio/models/moondream

Moondream in Fuser answers questions about an image and writes captions from a text prompt. Use it to tag assets or check generations on one canvas.

## Specs

| Spec | Value |
| --- | --- |
| Inputs | Image, Question (text) |
| Output | text |
| Cost | 7 credits per run |

Extract instant visual answers, detailed scene captions, and precise spatial layouts from any image with sub-second latency

Creator: Vikhyat Korrapati

Also known as: Moondream 3

[Inspect Image](https://app.fuser.studio)

## How Moondream works

Go from raw image upload to grounded visual intelligence in three straightforward steps.

### Upload your image

Provide a clear JPEG or PNG image focusing on the subjects or interface you want to inspect.

### Choose task and prompt

Select between broad scene captioning or targeted visual queries with token limits up to 256.

### Receive instant visual intelligence

Get concise, objective descriptions and grounded answers in fractions of a second.

## What Moondream is good at

Engineered for near-instant visual comprehension, spatial reasoning, and clean textual extraction without heavy infrastructure.

### Spatial Question Answering

Ask targeted queries about object positions, item counts, or physical attributes, strictly grounded in the visual evidence of your input image.

### Zero-Shot E-Commerce Tagging

Extract structured product attributes, color profiles, materials, and categories for automated cataloging at high volume.

### Instant Accessibility Captions

Generate objective, literal scene descriptions focusing on composition, lighting, and subjects without flowery embellishments or hallucinations.

### UI and Layout Inspection

Analyze interface screenshots to identify navigation bars, active buttons, typography hierarchy, and component layouts in sub-second time.

## Made with Moondream

Explore how Moondream parses visual layouts, identifies materials, and transcribes scene details across diverse image domains.

### Product attribute inspection

### Exterior layout and material parsing

### Fashion lookbook scene captioning

### Poster typography and palette identification

### Archival scene analysis

## What people build with Moondream

From e-commerce catalog teams to accessibility engineers, see how developers deploy Moondream for high-volume visual workflows.

### Automated Product Cataloging

Generate structured product tags, color classifications, and material descriptions across high-volume catalog batches.

### High-Volume Alt-Text Generation

Produce literal, screen-reader-ready image captions that accurately reflect visual subjects, spatial orientation, and lighting.

### Interface Component Auditing

Parse web and mobile application screenshots to audit UI element placement, contrast, button presence, and hierarchy.

### Visual Search Indexing

Enrich media asset libraries with searchable visual metadata, spatial object counts, and scene descriptions at minimal compute cost.

### Real-Time Spatial Reasoning

Leverage lightweight 2B active token inference for camera feeds, automated monitoring, and edge workflows requiring sub-second responses.

## Frequently Asked Questions

### What is the relationship between Moondream Next and Moondream 3?

Moondream Next is the architecture previewing Moondream 3, utilizing a sparse Mixture of Experts (MoE) design with 9 billion total parameters that activates 2 billion parameters per token for near-instant inference.

### What tasks is Moondream Next best suited for?

Moondream Next excels at real-time image captioning, spatial visual question answering, e-commerce product tagging, alt-text generation, and UI layout inspection. Its sub-second response times make it ideal for high-throughput batch processing where larger models are too slow or cost-prohibitive.

### Where does Moondream Next struggle?

The model is not built for full-page dense document OCR, multi-step logical reasoning, pixel-perfect coordinate drafting, or generating long-form creative narratives. For workflows requiring dense document transcription or deep multi-paragraph reasoning, pair Moondream's initial extraction with a dedicated text LLM.

### How should I configure the task type and token limits?

Set task_type to 'query' for targeted questions and information extraction, or 'caption' for a complete scene summary. Use a max_tokens limit between 16 and 64 for short, single-sentence answers, and increase it up to 256 for detailed multi-sentence captions.

### Who created Moondream and how is it built?

Moondream was created by Vikhyat Korrapati at M87 Labs. It combines an efficient visual encoder with a language decoder in a sparse Mixture of Experts structure to deliver edge-ready visual intelligence.

## Try Moondream on Fuser

Extract instant visual answers, detailed scene captions, and precise spatial layouts from any image with sub-second latency

[Inspect Image](https://app.fuser.studio)

## Related models

- [Gemini Image](https://fuser.studio/models/gemini-image.md)
- [GPT Image](https://fuser.studio/models/gpt-image.md)
- [Seedream](https://fuser.studio/models/seedream.md)
- [FLUX.3 Image](https://fuser.studio/models/flux-3-image.md)
- [FLUX.2](https://fuser.studio/models/flux-2.md)
- [Topaz Image Upscale](https://fuser.studio/models/topaz-image-upscale.md)

Node reference: [Moondream docs](https://docs.fuser.studio/docs/nodes/image/moondream.md)