Embedding Model Comparison for 2026
Compare ModelsCompare text and multimodal embedding models from Jina, Qwen, Google, OpenAI and more. Find the best model for semantic search, RAG, and recommendation systems.
Available Models
Jina Embeddings v4
Jina AIUniversal embedding model for multimodal and multilingual retrieval. Supports both single-vector and...
Jina Embeddings v3
Jina AIMultilingual embeddings with task-specific LoRA adapters. Optimized for text retrieval, semantic mat...
Qwen3-Embedding-8B
QwenState-of-the-art multilingual text embedding model. #1 on MTEB multilingual leaderboard. Supports 10...
Gemini Embedding 001
GoogleGoogle's text embedding model for semantic search, classification, and clustering. Supports flexible...
mxbai-embed-large-v1
MixedbreadSOTA BERT-large sized embedding model. Outperforms OpenAI text-embedding-3-large and matches models ...
Cohere Embed v4
CohereMultimodal embedding model for text, images, and PDFs. Supports flexible dimensions and 128K context...
Cohere Embed English v3
CohereEnglish-optimized text and image embedding model. Fast and accurate for classification and retrieval...
Cohere Embed Multilingual v3
CohereMultilingual embedding model supporting 100+ languages. Ideal for cross-lingual retrieval and classi...
Voyage 3 Large
Voyage AIState-of-the-art general-purpose and multilingual embedding. Outperforms OpenAI-v3-large by 9.74% ac...
GTE-Qwen2-7B-instruct
AlibabaTop-ranked embedding model on MTEB English and Chinese. Built on Qwen2-7B with bidirectional attenti...
Linq-Embed-Mistral
Linq AITop retrieval model on MTEB, ranking #1 with 60.2 score. Built on Mistral-7B with advanced data refi...
LFM2-ColBERT-350M
Liquid AILate interaction retriever with excellent multilingual and cross-lingual performance. Fast inference...
Jina Embeddings v5 Omni Small
Jina AI1.74B-parameter multimodal omni embedding model accepting text, images, video, and audio with shared...
Jina Embeddings v5 Omni Nano
Jina AI1.04B-parameter compact multimodal omni embedding model accepting text, images, video, and audio. Sh...
Jina Embeddings v5 Omni Nano Text-Matching
Jina AI0.95B-parameter text-matching-targeted variant of the v5 omni-nano family. Accepts text, images, vid...
Jina Embeddings v5 Text Small
Jina AI677M-parameter multilingual text embedding model scoring 71.7 on MTEB English v2 and 67.7 on MMTEB. ...
Jina Embeddings v5 Text Nano
Jina AI239M-parameter lightweight multilingual text embedding model scoring 71.0 on MTEB English v2 and 65....
Jina Embeddings v5 Text Small Text-Matching
Jina AI677M-parameter text-matching-targeted variant of v5-text-small. Optimized for symmetric pairwise sim...
What Are Embedding Models?
Embedding models convert text, images, and other data into numerical vectors that capture semantic meaning. These vectors enable semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG) for AI applications.
Key Features to Compare
Dimensions
Higher dimensions capture more nuance but require more storage. Range from 384D to 8192D.
Context Length
Maximum tokens the model can process in a single input. Critical for long documents.
Modalities
Support for text, images, PDFs, and more. Multimodal models enable unified search.
Embedding Types
Dense (most common), sparse (keyword-focused), late interaction (ColBERT), and more.
External Resources
Learn more about embedding models and vector search from these authoritative sources:
What You Can Do on This Page
Compare Embedding Models by Dimensions and Multilingual Support
Our embedding model comparison page helps you evaluate models by output dimensionality, maximum input token length, pricing per million tokens, and multilingual capabilities. Compare popular models like mxbai-embed-large, Cohere Embed v5, Jina Embeddings v5, and Google's Gemini Embedding. Each offers different trade-offs between dimension size and retrieval accuracy for RAG and semantic search applications.
Find the Best Embedding Model for Vector Search and RAG
Choosing the right embedding model is critical for retrieval-augmented generation, document retrieval, and semantic search pipelines. Compare embedding dimensions, max token lengths, and pricing to optimize your vector database performance. Whether you need Cohere embeddings for enterprise search, Jina AI's v5 models for multilingual text, or open-source options like mxbai-embed-large, our comparison covers dense, sparse, and late-interaction embedding types.
Explore New Embedding Models Including Qwen3 and GTE
The embedding model landscape is evolving rapidly with new releases like Qwen3-Embedding-8B, GTE-Qwen2-7B-Instruct, and Jina AI's v5 family. These models offer improved performance on retrieval benchmarks and support for longer contexts. GPUvec tracks embedding models from Jina AI, Cohere, OpenAI, Google, and the open-source community so you can evaluate the latest options for your specific use case.