What Is Pixtral – The New Multi-Modal Large Language Model

Dominic Anderson January 22, 2025

Key Takeaways

Pixtral is a powerful AI model that can process text and images.
Industries like law, finance, and research can benefit from Pixtral.
Pixtral Large can outperform top AI models in several regards.

Mistral, a French artificial intelligence (AI) startup, has cemented its position as a major disruptor in the AI industry. And it has one model to thank for this – Pixtral Large. Here’s everything you need to know about it.

What Is Pixtral?

Pixtral is a sophisticated multi-modal language model. So far, the Pixtral family consists of two models – Pixtral 12B and Pixtral Large. Since Pixtral Large is practically a more powerful version of its predecessor – Pixtral 12B – this guide will primarily focus on its capabilities.

This 124B-parameter Pixtral model consists of two parts – a text decoder and a vision decoder. The former focuses on understanding written language. The latter helps the model understand images. This combination gives Pixtral Large a unique ability to work with both text and pictures at the same time, which earns it the flattering title of a “multi-modal” model.

Pixtral Large can handle a huge amount of information – up to 30 high-resolution images or the equivalent of a 300-page book in a single go. This makes it similar in power to other leading AI models, like those from OpenAI.

What Are the Key Features of Pixtral Large?

Some of the key features of this Pixtral model are obvious from its description. Still, let’s break these features down and dig a little bit deeper.

An Expansive Context Window for Complex Tasks

A context window refers to the amount of text a model can “remember” or process at once. In this regard, Pixtral Large stays true to its name. It has a large context window of 128,000 tokens. This means that it can process large chunks of data without splitting it into smaller parts.

Flexible Vision Processing Across Resolutions

As mentioned, Pixtral Large is equipped with a vision encoder. Well, that encoder can process images at varying resolutions. This flexibility allows the model to adapt to different types of tasks. A quick image processing or a high-precision analysis… it’s all the same to this Pixtral model.

Standardized Performance With MM-MT-Bench

Mistral developed an open-source benchmark called MM-MT-Bench. The goal of this tool is to provide consistent evaluation standards for multi-modal models like Pixtral Large. As a result, researchers can assess just how well Pixtral Large performs compared to other models.

Pixtral Large has been trained on datasets that combine both text and image. Trained – and fine-tuned. This allows it to follow complex instructions that involve both types of data simultaneously. For example, a customer support chatbot could analyze both an image of a damaged product and the customer’s message explaining the issue at the same time. Pixtral Large would allow it to understand the problem thoroughly and maintain context across multiple exchanges. That’s not to mention also providing an accurate solution in the end.

Scalability Across Applications

With Pixtral Large, you can tackle virtually any task. You can do something small and specific like analyze a contract. Or, Pixtral Large can help you build a multi-modal search engine for e-commerce. It’s just so versatile. This versatility makes this Pixtral model ideal for a wide range of industries and use cases. Common real-world examples include:

Document analysis and management in the legal and finance industries
Data visualization and analysis in research and data science
Customer support in e-commerce and technology

Mistral might be a relatively new player in the AI space. However, it can already compete with AI giants. Not only that, but it can outperform them.

Pixtral Large continues this trend. This Pixtral model has excelled in benchmark tests against top multi-modal models. Here are just a few highlights.

What Is Pixtral – The New Multi-Modal Large Language Model 2

Outperformed Claude-3.5, Sonnet, and Llama-3.2 in mathematical reasoning with visual data
Surpassed GPT-4o and Gemini-1.5 Pro in understanding and reasoning with charts, tables, and scanned documents
Outperformed Claude-3.5, Sonnet, Gemini-1.5 Pro, and GPT-4o in real-world multi-modal applications with text and image