Which Mistral AI Model Codes Best on a Home Machine? From 3B to 24B Tested

Can artificial intelligence truly replace human developers when it comes to writing code? It’s a bold question, but with the release of Mistral’s new local AI models, ranging from the lightweight Minist 3B to the powerhouse Devstral 2 Small 24B, this idea is inching closer to reality. Will Lamerton breaks down the performance of these open source models, testing their ability to generate a responsive landing page using only HTML, CSS, and JavaScript. The twist? These models run entirely on local hardware, promising greater privacy and control for developers. But do they deliver on their promise, or are they just another set of overhyped AI experiments? The results might surprise you.

In this overview, you’ll discover how each model stacks up in terms of usability, accuracy, and resource demands. From the minimalist Minist 3B to the feature-rich Devstral 2 Small 24B, these AI systems cater to a wide range of hardware setups and coding needs. Whether you’re curious about how a 3 GB model handles basic tasks or want to see if a 24B model can tackle complex animations and responsive design, this breakdown has you covered. By the end, you’ll have a clearer understanding of whether these local-first AI solutions are a practical addition to your development workflow, or just a glimpse of what’s to come in the future of coding.

Mistral Local AI Models Overview

Mistral’s Local AI Models

Mistral’s local AI models are open source and open-weight, making them accessible to developers who prioritize privacy, flexibility, and control. The lineup includes:

• Minist 3B: A lightweight model designed for basic coding tasks and minimal hardware requirements.
• Minist 8B: A mid-range model offering enhanced capabilities for more complex tasks.
• Minist 14B: A robust model capable of handling intricate coding challenges with improved accuracy.
• Devstral 2 Small 24B: The most powerful model in the lineup, tailored for high-end hardware and advanced applications.

These models cater to developers with varying hardware setups, offering scalability and flexibility. By allowing local execution, they provide a viable alternative to cloud-dependent AI solutions, making sure greater control over data and performance.

How the Models Were Tested

To evaluate their coding capabilities, each model was tasked with creating a modern, responsive landing page for an AI-powered YouTube manager SaaS product. The requirements for the task included:

• Vanilla HTML, CSS, and JavaScript for simplicity and compatibility.
• A functional email capture form to demonstrate interactivity.
• Responsive design optimized for both mobile and desktop views.
• Optional animations to enhance the user experience and visual appeal.

The tests were conducted using Olama, a versatile tool that supports running AI models either locally or in the cloud. Each model’s output was assessed based on functionality, design quality, responsiveness, and adherence to the given requirements.

Mistral’s New AI Models (3B, 8B, 14B, 24B) Coding Skills Tested

Expand your understanding of Mistral AI models with additional resources from our extensive library of articles.

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• LM Studio makes it easy to run AI models locally on your PC, Mac

Performance Breakdown

Minist 3B: Basic but Limited

The Minist 3B model successfully generated a simple landing page with a basic structure and minimal styling. While it met the fundamental requirements, it struggled with advanced features such as form validation and animations. This model is best suited for small-scale tasks, such as generating straightforward code snippets or creating simple layouts. Its low memory requirement of 3 GB makes it accessible to users with limited hardware resources, but its capabilities are limited for more demanding projects.

Minist 8B: A Step Up

The Minist 8B model demonstrated noticeable improvements over its smaller counterpart. It produced a more refined design, incorporated animations, and showed better responsiveness. However, it required additional prompts to address issues with the email capture form, indicating room for improvement in handling complex instructions. With a memory requirement of 8 GB, this model strikes a balance between performance and accessibility, making it suitable for small to medium-sized coding tasks.

Minist 14B: Balanced Performance

The Minist 14B model delivered a polished landing page that included animations, a responsive layout, and improved form functionality. It followed instructions more accurately and required fewer corrections compared to the smaller models. However, its higher memory requirement of 16–18 GB may limit its usability for developers with less powerful hardware. This model is ideal for users seeking a balance between performance and resource demands, offering reliable results for moderately complex projects.

Devstral 2 Small 24B: High-End Capabilities

The Devstral 2 Small 24B model stood out as the most capable in the lineup. It successfully created a fully responsive landing page with separate HTML, CSS, and JavaScript files. The output featured animations, a functional navbar, and a well-designed email capture form. However, its high memory requirement of 32 GB restricts its accessibility to users with high-end hardware. This model is best suited for developers tackling complex tasks that demand precision and advanced features.

Resource Requirements and Scalability

The memory requirements for these models scale with their size, allowing developers to choose a model that aligns with their hardware capabilities and project complexity:

• Minist 3B: 3 GB, suitable for basic tasks on consumer-grade hardware.
• Minist 8B: 8 GB, offering enhanced functionality for small to medium-sized projects.
• Minist 14B: 16–18 GB, ideal for moderately complex tasks with higher resource demands.
• Devstral 2 Small 24B: 32 GB, designed for advanced applications on high-end hardware.

This scalability ensures that developers can select a model that meets their specific needs, whether they are working on simple projects or tackling more demanding challenges.

Key Insights

Mistral’s local AI models represent a significant step forward in open source AI for coding tasks. While they do not yet rival the capabilities of state-of-the-art models like GPT-4.5, they offer practical utility for developers seeking local-first solutions. Key observations include:

• Minist 3B and 8B: Best suited for basic tasks and small projects, offering accessibility and ease of use.
• Minist 14B: Provides a balanced solution for users with moderately powerful hardware.
• Devstral 2 Small 24B: Excels in complex tasks but requires high-end hardware for optimal performance.

These models empower developers to work independently of cloud-based tools, offering greater control and flexibility. As local AI continues to evolve, its role in coding and other applications is likely to expand, providing developers with more robust and versatile tools.

Media Credit: Will Lamerton

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