Text-Embedding-Modelle
Modellranglisten aktualisiert auf Basis tatsächlicher Nutzungsdaten.
Modelle werden nach der Gesamtzahl der Prompt- und Completion-Token eingestuft, die in den letzten 7 Tagen über die OpenRouter-API verarbeitet wurden. Die 10 bestplatzierten Modelle werden angezeigt. Ranglisten messen die Nutzung auf OpenRouter und spiegeln die Verbreitung wider, nicht die Modellqualität oder die Leistung in Vergleichstests.
Embedding-Modelle wandeln Text in dichte Vektoren um, die semantische Suche, Retrieval-Augmented Generation (RAG), Clustering und Ähnlichkeitsabgleiche ermöglichen. Diese Sammlung ordnet Embedding-Modelle nach ihrer Nutzung auf OpenRouter in der vergangenen Woche. Die aktuellen Spitzenmodelle sind Qwen3 Embedding 8B, Text Embedding 3 Small und bge-m3. Greifen Sie über eine einzige API darauf zu und vergleichen Sie Dimensionen, Preise und Leistung, ohne mehrere Anbieterintegrationen verwalten zu müssen.
Embedding-Modelle auf OpenRouter
Qwen: Qwen3 Embedding 8B
224B Token
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
OpenAI: Text Embedding 3 Small
168B Token
text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.
BAAI: bge-m3
41,4B Token
The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.
Google: Gemini Embedding 001
32,8B Token
gemini-embedding-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard since the experimental launch in March.
OpenAI: Text Embedding 3 Large
24,1B Token
text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.
Google: Gemini Embedding 2
17,8B Token
Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity — you can embed a text query and retrieve the most relevant images, or vice versa — making it well-suited for multimodal search, recommendation, and document understanding pipelines.
Perplexity: Embed V1 0.6B
16,2B Token
pplx-embed-v1-0.6B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 0.6B parameter model targeting lightweight, low-latency embedding generation.
Qwen: Qwen3 Embedding 4B
10,4B Token
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
VoyageAI by MongoDB: voyage-4
8,59B Token
voyage-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embedd about voyage-4 here: mongodb.com/products/platform/ai-search-and-retrieval/models
Perplexity: Embed V1 4B
7,73B Token
pplx-embed-v1 -4B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 4B parameter model maximizing retrieval quality.