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What is Mistral Saba?

Mistral Saba is a sophisticated model featuring 24 billion parameters, developed from meticulously curated datasets originating from the Middle East and South Asia. It surpasses the performance of larger models—those exceeding five times its parameter count—by providing accurate and relevant responses while being remarkably faster and more economical. Moreover, it acts as a solid foundation for the development of highly tailored regional applications. Users can access this model via an API, and it can also be deployed locally, addressing specific security needs of customers. Like the newly launched Mistral Small 3, it is designed to be lightweight enough for operation on single-GPU systems, achieving impressive response rates of over 150 tokens per second. Mistral Saba embodies the rich cultural interconnections between the Middle East and South Asia, offering support for Arabic as well as a variety of Indian languages, with particular expertise in South Indian dialects such as Tamil. This broad linguistic capability enhances its flexibility for multinational use in these interconnected regions. Furthermore, the architecture of the model promotes seamless integration into a wide array of platforms, significantly improving its applicability across various sectors and ensuring that it meets the diverse needs of its users.

What is Mistral NeMo?

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

Media

Media

Integrations Supported

Mistral AI
StackAI

Integrations Supported

Mistral AI
StackAI
AI-FLOW
DataChain
Expanse
F#
LibreChat
Literal AI
Melies
Memo AI
Microsoft Foundry Models
Nebius Token Factory
Nutanix Enterprise AI
Overseer AI
PromptPal
R
Ruby
Visual Basic
Yaseen AI
bolt.diy

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad

Supported Platforms

SaaS

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mistral-saba

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mistral-nemo/

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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