moondream3-preview

Name: moondream3-preview
Author: moondream

by moondream

Compact visual reasoning model for fast image QA and scene captions

276Kdl/month

Compact VLMparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

moondream/moondream3-preview

Feature URI

mixpeek://image_extractor@v1/moondream3_preview_v1

Overview

Moondream3 Preview is a compact image-text model from Moondream focused on visual question answering, captioning, and deployable visual reasoning. It continues the Moondream line's emphasis on small-model ergonomics while keeping enough visual reasoning quality for production perception pipelines.

On Mixpeek, Moondream3 is a useful second-stage model after cheap embedding retrieval. Use it to caption candidate images, answer bounded visual questions, or extract concise observations that an agent can cite.

Architecture

Image-text-to-text model exposed through Hugging Face Transformers custom code. It supports caption generation, visual question answering, and streaming output for interactive applications.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so moondream3-preview runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The model produces text, so it lands in payload. Give the
          // collection a text vector index and embed that text to make it
          // searchable rather than only filterable.
          payload: { extracted_text: modelOutput, source_key: "archive/2026/asset-00412" },
          vectors: { "multimodal-embedding": embeddingOfModelOutput },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// universal_extractor@v1 runs google/gemini-embedding-2
// (3072-d) over a bucket, with no inference of your own.