Comparison of ChatGPT-4, LLama, and Mistral LLMs

Introduction

This document provides a comparative analysis of three prominent Large Language Models (LLMs): OpenAI's ChatGPT-4, LLama from the Allen Institute for AI, and Mistral from Facebook AI. The comparison focuses on performance benchmarks, cost, efficiency, and practical applications, intending to present the information in an easily understandable manner.

Performance Benchmarks

ChatGPT-4

• Capabilities: ChatGPT-4 is a multimodal model capable of accepting both image and text inputs and producing text outputs. It demonstrates human-level performance on various professional and academic benchmarks, including a simulated bar exam where it scored in the top 10% of test takers
• Benchmarks: On traditional NLP benchmarks, it outperforms previous large language models and most state-of-the-art systems. In the MMLU benchmark, GPT-4 not only outperforms existing models in English but also demonstrates strong performance in other languages
• Limitations: Like earlier GPT models, GPT-4 is not fully reliable (e.g., prone to "hallucinations"), has a limited context window, and does not learn from experience

LLama

• Capabilities: LLama models range from 7B to 65B parameters and are trained on trillions of tokens. LLama-13B outperforms GPT-3 on most benchmarks, and LLama-65B is competitive with the best models like Chinchilla-70B and PaLM-540B
• Benchmarks: Focuses on achieving the best possible performance at various inference budgets by training on more tokens than typical. LLama-13B notably outperforms GPT-3, despite being significantly smaller in size
• Data Usage: LLama only uses publicly available data, making their work compatible with open-sourcing, unlike most existing models that rely on data that is not publicly available or undocumented

Mistral

• Capabilities: Mistral 7B is a 7-billion-parameter language model engineered for high performance and efficiency. It outperforms the best open 13B model (Llama 2) across all evaluated benchmarks, and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation
• Efficiency: Utilizes grouped-query attention (GQA) for faster inference and sliding window attention (SWA) for handling longer sequences with reduced computational cost
• Goal: Aims to balance high performance with efficiency in large language models, making them more affordable and suitable for a wide range of real-world applications

Cost and Efficiency

While specific cost details for these models vary and are often not publicly disclosed, the general trend is that larger models like ChatGPT-4 and LLama (especially their larger variants) require more computational resources, thus potentially increasing operational costs. Mistral, on the other hand, is designed for efficiency, which can lead to lower operational costs in real-world applications.

Practical Applications and Code Examples

ChatGPT-4

• Applications: Ideal for customer service chatbots, educational tools, and creative writing assistance.
• Code Example: Usage of ChatGPT-4 API for generating text can be found in OpenAI's documentation.

LLama

• Applications: Suitable for research in AI ethics, educational platforms, and language analysis tools.
• Code Example: Code for implementing LLama in various tasks is available LLama GitHub.

Mistral

• Applications: Best for industrial automation, energy-efficient AI deployments, and mobile applications.
• Code Example: Mistral's GitHub repository provides examples for deploying the model in different scenarios: Mistral GitHub.

Conclusion

Each model offers unique strengths: ChatGPT-4 excels in generating human-like text and complex dialogues, LLama provides a balance of performance and interpretability, and Mistral stands out for its efficiency and cost-effectiveness.