Mistral and Microsoft accelerate AI innovation with the launch of Mistral Large on Azure

Paris-based startup Mistral AI has announced the launch of a new flagship large language model called Mistral Large. The newly launched Mistral Large AI model has reasoning capabilities and is designed to rival other top-tier artificial intelligence models like GPT-4 and Claude 2.

In addition to Mistral Large, the company is also launching its alternative to ChatGPT with a new service called Le Chat. This chat assistant is currently available in beta. 

The Mistral Large AI model is supposed to bridge the gap between pioneering research and real-world solutions and will bring a new dimension to the AI industry, which is undergoing a significant transformation with growing interest in more efficient and cost-effective models. 

Must Check: What is Mistral 8x7B? Performance, Capabilities, and How to Access the Open-Weight Model?

The Mistral Large AI model is the outcome of a multi-year partnership between Microsoft and Mistral AI. Both companies are fueled by a steadfast dedication to innovation and practical applications. This partnership with Microsoft enables Mistral AI, with access to Azure’s cutting-edge AI infrastructure, to accelerate the development and deployment of next-generation large language models (LLMs) and represents an opportunity for Mistral AI to unlock new commercial opportunities, expand to global markets, and foster ongoing research collaboration.  

“We are thrilled to embark on this partnership with Microsoft. With Azure’s cutting-edge AI infrastructure, we are reaching a new milestone in our expansion, propelling our innovative research and practical applications to new customers everywhere. Together, we are committed to driving impactful progress in the AI industry and delivering unparalleled value to our customers and partners globally,” says Arthur Mensch, Chief Executive Officer, Mistral AI.  

Mistral Large: How it is rivaling OpenAI ChatGPT 4

Mistral Large is our new cutting-edge text generation model. It has top-tier reasoning capabilities. It can be used for complex multilingual reasoning tasks, including text understanding, transformation, and code generation.  Mistral Large achieves strong results on commonly used benchmarks, making it the world’s second-ranked model generally available through an API (next to GPT-4) 

Comparison of GPT-4, Mistral Large (pre-trained), Claude 2, Gemini Pro 1.0, GPT 3.5, and LLaMA 2 70B on MMLU: Measuring massive multitask language understanding

Also Read: Mistral 7B Tutorial: A Step-by-Step Guide on How to Use Mistral LLM

What are Mistral’s Large new capabilities and strengths?

1. It is natively fluent in English, French, Spanish, German, and Italian, with a nuanced understanding of grammar and cultural context.
2. Its 32K token context window allows precise information recall from large documents.
3. Its precise instruction-following enables developers to design their moderation policies and used it to set up the system-level moderation of le Chat.
4. It is natively capable of function calling. This, along with the constrained output mode, implemented on La Plateforme, enables application development and tech stack modernization at scale.

Comparison: Mistral Large Vs Claude 2, Gemini Pro 1.0, GPT 4 & 3.5, and LLaMA

Reasoning and knowledge: Mistral Large shows powerful reasoning capabilities. In the following figure, we report the performance of the pre-trained models on standard benchmarks. Mistral Large performance on widespread common sense, reasoning, and knowledge benchmarks of the top-leading LLM models on the market: MMLU (Measuring massive multitask language in understanding), HellaSwag (10-shot), Wino Grande (5-shot), Arc Challenge (5-shot), Arc Challenge (25-shot), TriviaQA (5-shot) and TruthfulQA. Further Reading: How Mistral 7B Outperforms LLaMA 2 and GPT-3.5 by running 6x faster

Multi-lingual Capacities: Mistral Large has native multi-lingual capacities. It strongly outperforms LLaMA 2 70B on HellaSwag, Arc Challenge, and MMLU benchmarks in French, German, Spanish, and Italian. Comparison of Mistral Large, Mixtral 8x7B, and LLaMA 2 70B on HellaSwag, Arc Challenge, and MMLU in French, German, Spanish, and Italian.

Maths & Coding: Mistral Large shows top performance in coding and math tasks. In the table below, we report the performance across a suite of popular benchmarks to evaluate the coding and math performance for some of the top-leading LLM models. Mistral Large performance on popular coding and math benchmarks of the leading LLM models on the market: HumanEval pass@1, MBPP pass@1, Math maj@4, GSM8K maj@8 (8-shot) and GSM8K maj@1 (5 shot).

JSON format and Function Calling: JSON format mode forces the language model output to be valid JSON. This functionality enables developers to interact with our models more naturally to extract information in a structured format that can be easily used in the remainder of their pipelines. 

Function calling lets developers interface Mistral endpoints with a set of their tools, enabling more complex interactions with internal code, APIs, or databases. You will learn more in our function calling guide.

Function calling and JSON format are only available on mistral-small and mistral-large. We will be adding formatting to all endpoints shortly, as well as enabling more fine-grained format definitions.

How to try the Mistral Large AI Model on Microsoft Azure:

Developers and users can try Mistral Large and Mistral Small on La Plateforme and Azure. Mistral Large is also exposed on our beta assistant demonstrator, le Chat.  Visit the Mistral Large model card and sign in with your Azure subscription to get started with Mistral Large on Azure AI. You can also review the technical blog to learn how to use Mistral Large on Azure AI. Visit Mistral AI’s blog to get deeper insights about the model.