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What is Mistral Medium 3?

Mistral Medium 3 is a breakthrough in AI technology, offering the perfect balance of cutting-edge performance and significantly reduced costs. This model introduces a new era of enterprise AI, with a focus on simplifying deployments while still providing exceptional performance. Its ability to deliver high-level results at just a fraction of the cost of its competitors makes it a game-changer in industries that rely on complex AI tasks. Mistral Medium 3 is particularly strong in professional use cases like coding, where it competes closely with larger models that are typically more expensive and slower. The model supports hybrid and on-premises deployments, offering enterprise users full control over customization and integration into their systems. Businesses can leverage Mistral Medium 3 for both large-scale deployments and fine-tuned, domain-specific training, allowing for enhanced efficiency in industries such as healthcare, financial services, and energy. The addition of continuous learning and the ability to integrate with enterprise knowledge bases makes it a flexible, future-proof solution. Customers in beta are already using Mistral Medium 3 to enrich customer service, personalize business processes, and analyze complex datasets, demonstrating its real-world value. Available through various cloud platforms like Amazon Sagemaker, IBM WatsonX, and Google Cloud Vertex, Mistral Medium 3 is now ready to be deployed for custom use cases across a range of industries.

What is Ministral 3?

Mistral 3 marks the latest development in the realm of open-weight AI models created by Mistral AI, featuring a wide array of options ranging from small, edge-optimized variants to a prominent large-scale multimodal model. Among this selection are three streamlined “Ministral 3” models, equipped with 3 billion, 8 billion, and 14 billion parameters, specifically designed for use on resource-constrained devices like laptops, drones, and various edge devices. In addition, the powerful “Mistral Large 3” serves as a sparse mixture-of-experts model, featuring an impressive total of 675 billion parameters, with 41 billion actively utilized. These models are adept at managing multimodal and multilingual tasks, excelling in areas such as text analysis and image understanding, and have demonstrated remarkable capabilities in responding to general inquiries, handling multilingual conversations, and processing multimodal inputs. Moreover, both the base and instruction-tuned variants are offered under the Apache 2.0 license, which promotes significant customization and integration into a range of enterprise and open-source projects. This approach not only enhances flexibility in usage but also sparks innovation and fosters collaboration among developers and organizations, ultimately driving advancements in AI technology.

Media

Media

Integrations Supported

Amazon SageMaker
C
C++
CSS
Clojure
F#
Go
HTML
Julia
Kotlin
Microsoft Foundry
Microsoft Foundry Models
Mistral AI
Mistral Code
OpenTag
Ruby
Rust
TypeScript
Visual Basic

Integrations Supported

NexaSDK

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

Not specified

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mistral-3

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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