Why Pixtral 12B should be on every developer's radar

As soon as I came across Pixtral 12B, I knew it was different.

I had been on the hunt for an open-source AI model that didn't come with the usual frustrations—restricted APIs, limited access, and hefty pricetags. I needed something flexible, easy to integrate, and powerful enough for real-world tasks.

So when I came across this recently released model, I was thrilled.

Pixtral 12B is the first-ever multimodal model from Mistral AI, available under an Apache 2.0 license. It handles both text and multimodal prompts, generating solid text-based responses while offering full control over your deployment.

After testing it, I found Pixtral refreshingly open, practical for developers, and designed to fit into real workflows without tying your hands.

Here’s what you’ll find in this newsletter:

• What Pixtral 12B can do: Architecture, features, and highlights.

• How it compares: Strengths, weaknesses, and competitors.

• Real-world use cases: OCR, image analysis, and more.

• What’s next: Limitations and areas to watch for future updates.

I spent time exploring the model, and I’m excited to share what I found. If open-source AI is on your radar, Pixtral is worth a closer look. Let’s dive in.

Meet Mistral AI#

Mistral AI is a Paris-based company founded by former Meta and Google DeepMind experts, including Arthur Mensch, Grégoire Webber, Léon Bottou, and Thomas Wolf. Known for launching some of the most capable open models, Mistral has driven AI innovation to the frontier.

Their models, like the Mistral Large 2 designed for high-complexity tasks with a 128K token context window, and Mistral Small v24.09, a fast, cost-efficient model for tasks like translation, are reshaping enterprise AI.

Meanwhile, specialized models like Codestral for coding and Mistral Embed for semantic text representation showcase Mistral’s versatility. The Pixtral 12B, a vision-capable model, offers robust image analysis under the Apache 2.0 license, allowing seamless deployment without third-party dependence.

An overview of Mistral AI models#

Mistral have launched the world’s most capable open models, paving the way for frontier AI innovation. Curious about the origins of Pixtral and other variants? Let’s explore the details:

1. Ministral:  This is a pair of two small language models (SLMs) released recently on the anniversary of Mistral AI. Ministral 3B (3 billion parameters) and Ministral 8B (8 billion parameters) are specifically designed for on-device and edge computing. These models excel in knowledge, reasoning, function-calling, and efficiency, setting a new standard for sub-10B models. With up to 128K context length and advanced attention mechanisms, Ministral 3B and 8B are versatile tools for various applications.

2. Mistral Large 2: A 123 billion parameter model designed for complex reasoning tasks, supporting multilingual outputs (European, Asian, and Arabic languages). It offers a large 128K token context window, enhanced function calling, JSON outputs, and proficiency in over 80 coding languages, making it ideal for sophisticated applications.

3. Mistral Small v24.09: A 22-billion parameter, cost-efficient, enterprise-grade model that excels in translation, summarization, and sentiment analysis. Despite its smaller size, it features a 128K token context window and is known for its speed and versatility, available under the Mistral Research License.

4. Mistral NeMo: A powerful 12B (12 billion) model developed with NVIDIA, supporting multilingual tasks and featuring a large 128K token context window. It is one of the most robust models in its category and is available under the open-source Apache 2.0 license.

5. Codestral: A 22 billion Mistral model specialized for coding tasks, trained in over 80 programming languages including Python, Java, and C++. It is optimized for low latency, with a smaller size and a 32K token context window, making it efficient for coding solutions.

6. Mistral Embed: A state-of-the-art model for text embedding and semantic representation in English, achieving high benchmark retrieval scores. It is built specifically for extracting text representations.

7. Pixtral 12B: A vision-capable model that enables image analysis and search. It is available under Apache 2.0, allowing deployment in your environment without needing third-party providers for file uploads. Let’s dive into the details of this model.

These models can be accessed using La Plateforme or Le Chat.

Exploring Pixtral’s architecture#

In Pixtral, we find a multimodal model that excels at image and text tasks. Pixtral’s architecture is built around two key components:

1.  A vision encoder that is responsible for tokenizing images.

2. A multimodal transformer decoder that predicts the next text token based on a sequence of both text and images.

With its interleaved training on diverse datasets, Pixtral shows remarkable capability in following complex instructions while keeping its edge on traditional text-only benchmarks.

At its core, the architecture features a robust 400M parameter vision encoder trained from scratch, coupled with a powerful 12B parameter multimodal decoder based on Mistral Nemo. This setup supports variable image sizes, aspect ratios, and multiple images, all within an expansive 128k token context window. 

Comparing Pixtral’s capabilities#

Pixtral can understand charts, compare images, and transcribe receipts and old documents. It can also perform OCR with structured output. It excels in understanding natural images and documents, scoring 52.5% on the MMMU reasoning benchmarkThe MMMU (Multimodal multitask unified) reasoning benchmark evaluates AI models' ability to perform reasoning across diverse modalities (text, images, etc.) and tasks, providing a unified measure of multimodal understanding and problem-solving capabilities.. It performs strongly in chart analysis, document Q&A, and multimodal reasoning tasks.

Various open and closed models were re-evaluated using the same evaluation framework. A single prompt was selected for every dataset to replicate the results of leading multimodal models, such as GPT-4o and Claude-3.5-Sonnet.

All models were subsequently tested using this same prompt. Pixtral consistently outperforms other open models of similar scale and often surpasses closed models like Claude 3 Haiku. It even matches or exceeds the performance of larger models like LLaVa OneVision 72B on multimodal benchmarks. 

Real-world use cases#

Let’s see some examples of Pixtral 12B in action.

Historical document transcription #

I had the chance to put Pixtral to the test with a set of old historical manuscripts that were difficult to read, let alone transcribe.

The intricate handwriting and aged paper presented a challenge, but Pixtral handled it impressively. It accurately transcribed the text into a clean, structured format, saving me hours of manual work. This tool has become invaluable for preserving these documents digitally, making it easier to analyze them in detail and uncover historical insights I wouldn’t have noticed otherwise. Here is a screenshot of proof:

Customer support automation#

I tested Pixtral with a real-world example: a customer requesting a refund, claiming they’d received rotten apples. I had an image as proof, and I wanted to see how Pixtral would handle it.

Impressively, Pixtral analyzed the image to determine if the apples were truly spoiled or just had minor imperfections. It could distinguish fresh produce from signs of spoilage and offered an accurate assessment of the situation.

From there, it generated a polite, professional response suggesting next steps—like verifying storage conditions or offering a replacement. This demonstrated Pixtral’s ability to seamlessly process both visual and textual inputs, making it a powerful tool for resolving product-related claims quickly and efficiently.

Limitations and areas for improvement#

Pixtral was trained on data until October 2023 and excels in text-based tasks and single- and multi-image instruction-following. However, it can't currently fine-tune image capabilities or generate images. During interactions, a few limitations were observed: Pixtral sometimes exhibits self-awareness issues and can hallucinate responses, leading to occasional inaccuracies.

Pixtral's future potential#

Pixtral holds immense promise, with a few key areas for growth that could take it to the next level.

One critical improvement is reducing hallucinations. Enhancing reliability in more complex tasks will make Pixtral even more dependable. Another exciting opportunity lies in unlocking image generation capabilities, which would transform it into a true multimodal powerhouse and significantly expand its versatility.

Given the Mistral AI team’s track record, these advancements are likely on the horizon, setting Pixtral up to make an even greater impact on the AI landscape.

Why developers should explore Pixtral#

Pixtral offers developers a unique combination of open-source flexibility, multimodal power, and cost-effective deployment. Because it operates under that Apache 2.0 license, it removes third-party dependencies, making integration seamless across various applications.

Whether you’re tackling image analysis, OCR, or text-based tasks, Pixtral excels at handling complex, multimodal data with its expansive 128K token context window. Its ability to process multiple images, variable sizes, and intricate prompts gives developers unmatched control and customization.

With ongoing enhancements in accuracy and the potential for features like image generation, Pixtral has the potential to become a pretty powerful tool for developers—it’s a forward-thinking platform built for the future of AI development.

Pixtral: Innovation in progress#

My experience with Pixtral has been rewarding—and if you try it, I think yours will be, too.

It's an impressive model and while it's not perfect, it offers developers an exciting mix of flexibility and innovation that feels new and fresh in the AI space.

To me, Pixtral feels like a glimpse into what's next for multimodal AI. It has lots of potential to grow and evolve, and I'm eager to see how the Mistral team continues to refine it. If you're exploring Generative AI, Pixtral is a solid choice you should check out.

And if you're interested in learning how else developers can use LLMs, then I encourage you to check out the following Educative courses:

1. Getting Started with Google Gemini

2. Introduction to Prompt Engineering with Llama 3