+
+

Related Products

  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    1,161 Ratings
    Visit Website
  • Couchbase
    417 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website
  • Haast
    5 Ratings
    Visit Website
  • ScreenMeet
    35 Ratings
    Visit Website
  • Wallester
    277 Ratings
    Visit Website
  • Visual Lease
    450 Ratings
    Visit Website
  • FISPAN
    5 Ratings
    Visit Website
  • Framnex
    1 Rating
    Visit Website

About

Cohere's Embed is a leading multimodal embedding platform designed to transform text, images, or a combination of both into high-quality vector representations. These embeddings are optimized for semantic search, retrieval-augmented generation, classification, clustering, and agentic AI applications.​ The latest model, embed-v4.0, supports mixed-modality inputs, allowing users to combine text and images into a single embedding. It offers Matryoshka embeddings with configurable dimensions of 256, 512, 1024, or 1536, enabling flexibility in balancing performance and resource usage. With a context length of up to 128,000 tokens, embed-v4.0 is well-suited for processing large documents and complex data structures. It also supports compressed embedding types, including float, int8, uint8, binary, and ubinary, facilitating efficient storage and faster retrieval in vector databases. Multilingual support spans over 100 languages, making it a versatile tool for global applications.

About

EmbeddingGemma 2 is an open, lightweight multimodal embedding model designed to map text, code, images, video, and audio into a shared embedding space for search, retrieval, classification, routing, and RAG applications. Built on the Gemma 4 architecture and released under the Apache 2.0 license, it has 740 million parameters and is optimized for on-device inference. Its modular design can use as little as 270M parameters for text-only workloads, with optional vision and audio encoders for full multimodal support. Matryoshka Representation Learning lets developers reduce output vectors from 768 dimensions to 512, 256, or 128, lowering storage and memory requirements for local vector databases. The model supports an 8K-token context window and can process up to 5.5 minutes of audio, 29 images, 58 video frames, or interleaved combinations on local hardware.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

AI teams seeking a solution for generating high-quality, multimodal embeddings that enhance search accuracy and contextual understanding

Audience

Developers and AI teams wanting to build private, efficient, on-device multimodal search, retrieval, RAG, and semantic indexing systems

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Not Supported

API

Offers API Supported

Screenshots and Videos

Screenshots and Videos

Pricing

$0.47 per image
Free Version Not Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Supported
Live Online Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

Cohere
Founded: 2019
Canada
cohere.com/embed

Company Information

Google
Founded: 1998
United States
blog.google/innovation-and-ai/technology/developers-tools/embeddinggemma-2/

Alternatives

Codestral Embed

Codestral Embed

Mistral AI

Alternatives

txtai

txtai

NeuML

Categories

Categories

Embedding Models Supported

Integrations

Cohere Supported
voyage-4-large Supported

Integrations

Cohere Not Supported
voyage-4-large Not Supported
Claim Cohere Embed and update features and information
Claim Cohere Embed and update features and information
Claim EmbeddingGemma 2 and update features and information
Claim EmbeddingGemma 2 and update features and information