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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
MAI-Code-1.1-Flash is a compact and effective coding model aimed at enhancing the speed and quality of code development for engineering teams. Currently implemented in GitHub Copilot and integrated into VS Code, it caters to the actual workflows of developers, specifically enhancing command-line operations and .NET tasks based on user input. When compared to the version unveiled at Microsoft Build in June, this model showcases a significant improvement in code quality, achieved with reduced token usage and quicker streaming responses. Microsoft claims a 22% enhancement on Terminal-Bench 2.1 for GitHub Copilot CLI and a 15% boost in .NET task performance. Additionally, production outcomes indicate a 4% rise in code survival rates and a 9% increase in users returning to the platform. Notably, in GitHub Copilot, tokens are streamed 25% faster, and the model requires 25% fewer tokens for task completion, which translates to quicker responses, reduced wait times, and enhanced productivity from each token processed. These advancements stem from refined training methods and improved operational efficiencies, with a strong focus on practical application in real-world scenarios. Ultimately, MAI-Code-1.1-Flash represents a significant leap forward in coding assistance technology.
API Access
Has API
API Access
Has API
Integrations
.NET
GitHub
GitHub Copilot
Microsoft Azure
Microsoft Foundry
Mistral AI
Mistral Code
Visual Studio Code
Integrations
.NET
GitHub
GitHub Copilot
Microsoft Azure
Microsoft Foundry
Mistral AI
Mistral Code
Visual Studio Code
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
Microsoft AI
Founded
2024
Country
United States
Website
microsoft.ai/news/mai-code-1-1-flash-br-better-faster-at-a-quarter-of-the-cost/