Modèles de plongement vectoriel de texte
Classement des modèles mis à jour à partir de données d’utilisation réelles.
Les modèles sont classés selon le nombre total de jetons de prompt et de réponse traités via l’API OpenRouter au cours des 7 derniers jours. Affichage des 10 meilleurs modèles. Les classements mesurent l’utilisation sur OpenRouter et reflètent l’adoption, et non la qualité des modèles ou leurs performances aux tests de référence.
Les modèles de plongement vectoriel convertissent le texte en vecteurs denses qui permettent la recherche sémantique, la génération augmentée par récupération (RAG), le regroupement et la recherche par similarité. Cette collection classe les modèles de plongement vectoriel selon leur utilisation sur OpenRouter au cours de la semaine écoulée. Les meilleurs modèles actuels sont Qwen3 Embedding 8B, Text Embedding 3 Small et bge-m3. Accédez-y via une seule API et comparez les dimensions, les tarifs et les performances sans gérer plusieurs intégrations de fournisseurs.
Modèles de plongement vectoriel sur OpenRouter
Qwen: Qwen3 Embedding 8B
223B jetons
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
167B jetons
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
41,3B jetons
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 jetons
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
24B jetons
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 jetons
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 jetons
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
10,3B jetons
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
8,53B jetons
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 jetons
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.