Benchmarking of Commercial Large Language Models: ChatGPT, Mistral, and Llama

Abstract Recent developments in artificial intelligence have ushered in a new era of language models, with capabilities that are rapidly advancing the frontiers of technology and communication. The present research conducts a detailed comparative analysis of three prominent large language models—ChatGPT, Mistral, and Llama—utilizing the Hugging Face platform to benchmark their performance across multiple dimensions, including computational efficiency, linguistic accuracy, and ethical alignment. Results indicate that while each model exhibits unique strengths, they also possess distinct limitations which can guide future enhancements. Specifically, ChatGPT excels in linguistic accuracy, Llama in adaptability across languages, and Mistral offers novel approaches in complex language processing. This benchmarking exercise provides critical insights into the current capabilities of large language models, highlighting areas for potential improvement and suggesting avenues for future research to enhance their effectiveness and ethical alignment. The findings showed the necessity for ongoing evaluations to support the development of AI technologies that are both powerful and aligned with ethical standards. The exploration of hybrid models that combine the strengths of these existing systems could pave the way for the next generation of language models that are not only more efficient and accurate but also more aligned with human values and ethical standards.