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Description
Codestral Embed marks Mistral AI's inaugural venture into embedding models, focusing specifically on code and engineered for optimal code retrieval and comprehension. It surpasses other prominent code embedding models in the industry, including Voyage Code 3, Cohere Embed v4.0, and OpenAI’s large embedding model, showcasing its superior performance. This model is capable of generating embeddings with varying dimensions and levels of precision; for example, even at a dimension of 256 and int8 precision, it maintains a competitive edge over rival models. The embeddings are organized by relevance, enabling users to select the top n dimensions, which facilitates an effective balance between quality and cost. Codestral Embed shines particularly in retrieval applications involving real-world code data, excelling in evaluations such as SWE-Bench, which uses actual GitHub issues and their solutions, along with Text2Code (GitHub), which enhances context for tasks like code completion or editing. Its versatility and performance make it a valuable tool for developers looking to leverage advanced code understanding capabilities.
Description
EmbeddingGemma is a versatile multilingual text embedding model with 308 million parameters, designed to be lightweight yet effective, allowing it to operate seamlessly on common devices like smartphones, laptops, and tablets. This model, based on the Gemma 3 architecture, is capable of supporting more than 100 languages and can handle up to 2,000 input tokens, utilizing Matryoshka Representation Learning (MRL) for customizable embedding sizes of 768, 512, 256, or 128 dimensions, which balances speed, storage, and accuracy. With its GPU and EdgeTPU-accelerated capabilities, it can generate embeddings in a matter of milliseconds—taking under 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training ensures that memory usage remains below 200 MB without sacrificing quality. Such characteristics make it especially suitable for immediate, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. Whether used for personal file searches, mobile chatbot functionality, or specialized applications, its design prioritizes user privacy and efficiency. Consequently, EmbeddingGemma stands out as an optimal solution for a variety of real-time text processing needs.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Mistral AI
Founded
2023
Country
United States
Website
mistral.ai/news/codestral-embed
Vendor Details
Company Name
Founded
1998
Country
United States
Website
ai.google.dev/gemma/docs/embeddinggemma