Introduction

The AI world is buzzing with excitement over the release of Mistral 2. This new version brings impressive advancements, especially in code generation, mathematics, and reasoning. In this article, we’ll dive deep into what makes Mistral 2 special, compare it with other leading models like Llama 3.1 and GPT-4, and explore its capabilities in various tests. Whether you’re a tech enthusiast or just curious about AI, this guide will help you understand Mistral 2 in simple terms.

Table of Contents

1. Overview of Mistral 2
2. Code Generation Performance
3. Mathematical Abilities
4. Multilingual Capabilities
5. Tool Use and Function Calling
6. Programming Tests
7. Logical and Reasoning Tests
8. Safety Measures and Ethics
9. Conclusion

Overview of Mistral 2

Mistral 2 is the latest AI model with a 128,000-token context window, making it highly capable in various tasks. It stands out in code generation and mathematical problem-solving, often surpassing other models like Llama 3.1. However, its performance varies across different benchmarks.

Code Generation Performance

When it comes to generating code, Mistral 2 matches the performance of Llama 3.1’s 405 billion parameter model. It excels in several programming languages, including C++, Java, TypeScript, PHP, and C#. However, for certain tasks like the GSM 8K 8-shot, Llama 3.1 performs slightly better.

Mathematical Abilities

In mathematics, Mistral 2 even outshines Llama 3.1, making it a strong contender for tasks requiring advanced math skills. It handles complex calculations and problem-solving with ease.

Multilingual Capabilities

Mistral 2 isn’t just limited to English. It performs exceptionally well in languages like French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi. Although it falls slightly short of Llama 3.1 in multilingual performance, it still surpasses many other models.

Tool Use and Function Calling

Mistral 2 supports both parallel and sequential function calls, making it versatile in handling various tasks. In benchmarks, it outperforms GPT-4 in this area, demonstrating its advanced capabilities.

Programming Tests

Python Challenges

To test Mistral 2’s programming skills, we used the Edabit website for Python challenges. It easily solved easy and medium tasks. For hard challenges like finding a domain name from a DNS pointer and creating an identity matrix, Mistral 2 performed exceptionally well.

Expert Level Programming

When faced with expert-level tasks, such as the fares sequence, Mistral 2 struggled, showing errors similar to other top models like Llama 3.1 and GPT-4. This suggests that Mistral 2 is on par with these models in programming but may hit limits with very complex tasks.

Logical and Reasoning Tests

For logical reasoning, we tested Mistral 2 with a simple question about three sisters playing chess. Although it failed this specific test, it generally performed well, answering 8-9 out of 10 questions correctly.

Safety Measures and Ethics

We also tested Mistral 2’s response to sensitive questions. When asked about breaking into a car, it refused to provide an answer, demonstrating good ethical standards. Even when framed as an educational query, it maintained its stance, indicating a strong safety protocol.

Conclusion

Mistral 2 is a powerful AI model that excels in code generation, mathematics, and reasoning. While it performs on par with models like Llama 3.1 and GPT-4, it still faces challenges with very complex tasks. Its multilingual capabilities and ethical standards are commendable, making it a reliable choice for various applications. Share your thoughts on Mistral 2 in the comments and stay tuned for more AI explorations.