• Home
  • Categories
  • Pricing
  • Submit
    Built with
    Ever Works
    Ever Works

    Connect with us

    Stay Updated

    Get the latest updates and exclusive content delivered to your inbox.

    Product

    • Categories
    • Pricing
    • Help

    Clients

    • Sign In
    • Register
    • Forgot password?

    Company

    • About Us
    • Admin
    • Sitemap

    Resources

    • Blog
    • Submit
    • API Documentation
    All product names, logos, and brands are the property of their respective owners. All company, product, and service names used in this repository, related repositories, and associated websites are for identification purposes only. The use of these names, logos, and brands does not imply endorsement, affiliation, or sponsorship. This directory may include content generated by artificial intelligence.
    Copyright © 2025 Awesome Vector Databases. All rights reserved.·Terms of Service·Privacy Policy·Cookies
    Decorative pattern
    Decorative pattern
    1. Home
    2. Machine Learning Models
    3. Cohere Embed Multilingual v3

    Cohere Embed Multilingual v3

    High-performance multilingual embedding model from Cohere supporting 100+ languages with 1024 dimensions, optimized for semantic search, RAG, and cross-lingual retrieval tasks.

    Overview

    embed-multilingual-v3.0 is a high-performance embedding model from Cohere's Embed V3 model family, specifically tailored for multilingual text representation with support for 100+ languages.

    Key Features

    • Dimensionality: 1024 dimensions
    • Languages: 100+ languages supported
    • Training Data: Nearly 1B English and 0.5B non-English training pairs
    • Cross-lingual: Search within and across languages

    Capabilities

    Within-Language Search

    Search with a French query on French documents

    Cross-Language Search

    Search with a Chinese query on Finnish documents

    Use Cases

    • Multilingual Semantic Search: Find relevant content across languages
    • RAG (Retrieval Augmented Generation): Context retrieval for LLMs
    • Text Classification: Categorize text in multiple languages
    • Document Clustering: Group similar documents regardless of language

    Input Types

    Requires specifying input type for optimal performance:

    • input_type="search_document": For embedding passages/documents
    • input_type="search_query": For embedding search queries

    Availability (2026)

    Accessible through multiple platforms:

    • Cohere API
    • AWS SageMaker
    • Azure AI
    • Oracle Cloud Infrastructure
    • Private deployments with CUDA driver 12.2+ and NVIDIA driver 535+

    Integration

    • Compatible with major vector databases
    • Supported by LangChain and LlamaIndex
    • RESTful API access
    Surveys

    Loading more......

    Information

    Websitedocs.cohere.com
    PublishedMar 22, 2026

    Categories

    1 Item
    Machine Learning Models

    Tags

    3 Items
    #embeddings#multilingual#api

    Similar Products

    6 result(s)

    voyage-3-large

    State-of-the-art general-purpose and multilingual embedding model from Voyage AI that ranks first across eight domains spanning 100 datasets, outperforming OpenAI and Cohere models by significant margins.

    Featured

    Mistral Embed

    State-of-the-art embedding model from Mistral AI that generates 1024-dimensional vectors for text, supporting semantic search, clustering, and retrieval-augmented generation applications.

    Qwen3 Embedding

    Multilingual embedding model supporting over 100 languages and ranking #1 on MTEB multilingual leaderboard. Offers flexible model sizes from 0.6B to 8B parameters with user-defined instructions.

    Featured

    BGE-M3

    A versatile multilingual text embedding model from BAAI that supports 100+ languages and can handle inputs up to 8192 tokens. BGE-M3 is unique in supporting three retrieval methods simultaneously: dense retrieval, multi-vector retrieval, and sparse retrieval.

    gte-Qwen2-1.5B-instruct

    A state-of-the-art multilingual text embedding model from Alibaba's GTE (General Text Embedding) series, built on the Qwen2-1.5B LLM. The model supports up to 8192 tokens and incorporates bidirectional attention mechanisms for enhanced contextual understanding across diverse domains.

    gte-Qwen2-7B-instruct

    A large-scale multilingual text embedding model from Alibaba's GTE series with 7 billion parameters. Built on Qwen2-7B, it achieved a score of 70.24 on MTEB, outperforming NV-Embed-v1 and supporting 100+ languages with up to 8192 token context.