Modelos de embeddings de texto
Clasificaciones de modelos actualizadas según datos de uso reales.
Los modelos se clasifican según el total de tókenes de prompts y respuestas procesados a través de la API de OpenRouter durante los últimos 7 días. Se muestran los 10 modelos mejor clasificados. Las clasificaciones miden el uso en OpenRouter y reflejan la adopción, no la calidad del modelo ni su rendimiento en pruebas de evaluación.
Los modelos de embeddings convierten texto en vectores densos que permiten la búsqueda semántica, la generación aumentada por recuperación (RAG), la agrupación y la búsqueda de coincidencias por similitud. Esta colección clasifica los modelos de embeddings según su uso en OpenRouter durante la última semana. Los mejores modelos actuales son Qwen3 Embedding 8B, Text Embedding 3 Small y bge-m3. Acceda a ellos a través de una sola API y compare dimensiones, precios y rendimiento sin gestionar múltiples integraciones con proveedores.
Modelos de embeddings en OpenRouter
Qwen: Qwen3 Embedding 8B
223B tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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 tókenes
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.