> ## Documentation Index
> Fetch the complete documentation index at: https://veniceai-mintlify-f533effe.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Embedding-Modelle

> Venice-Embedding-Modelle für semantische Suche, RAG-Retrieval und Clustering, inklusive Preise, Dimensionen und OpenAI-kompatibler /embeddings-Nutzung.

<div id="model-search-placeholder" data-filter="embedding">
  Verwenden Sie den `id`-Wert als `model`-Parameter in API-Anfragen. Derzeit sind 9 Modelle verfügbar.

  | Model                          | ID                                              | Input (pro 1M Tokens) | Privacy    |
  | ------------------------------ | ----------------------------------------------- | --------------------- | ---------- |
  | BGE-EN-ICL                     | `text-embedding-bge-en-icl`                     | \$0.01                | Private    |
  | BGE-M3                         | `text-embedding-bge-m3`                         | \$0.15                | Private    |
  | Gemini Embedding 2 Preview     | `gemini-embedding-2-preview`                    | \$0.25                | Anonymized |
  | Multilingual E5 Large Instruct | `text-embedding-multilingual-e5-large-instruct` | \$0.01                | Private    |
  | Nemotron Embed VL 1B v2        | `text-embedding-nemotron-embed-vl-1b-v2`        | \$0.01                | Private    |
  | Qwen3 Embedding 0.6B           | `text-embedding-qwen3-0-6b`                     | \$0.01                | Private    |
  | Qwen3 Embedding 8B             | `text-embedding-qwen3-8b`                       | \$0.01                | Private    |
  | Text Embedding 3 Large         | `text-embedding-3-large`                        | \$0.16                | Anonymized |
  | Text Embedding 3 Small         | `text-embedding-3-small`                        | \$0.03                | Anonymized |
</div>

***

<Note>
  Verwendungsbeispiele finden Sie in der [Embeddings-API](/api-reference/endpoint/embeddings/generate).
</Note>