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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.

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

APIPark
AiAssistWorks
BlueGPT
C++
Elixir
EvalsOne
Graydient AI
Groq
Kotlin
Lewis
Microsoft Foundry Models
Noma
OpenLIT
Ragas
ReByte
Superinterface
Visual Basic
WebLLM
kluster.ai

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Free Version
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

24 Hour Support
Web-Based Support

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/news/mistral-nemo/

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

Foundation Models

Not specified

Large Language Models

Not specified

Small 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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