Comparative Analysis of Open Source and Proprietary Large Language Models: Performance and Accessibility

Abstract

The rapid development of large language models (LLMs) has sparked significant interest in both open source and proprietary implementations. This study conducts a comparative analysis of these two categories of LLMs, focusing on their performance metrics and accessibility. We evaluate performance through standardized benchmarks across various natural language processing (NLP) tasks, including text generation, sentiment analysis, and language translation. Accessibility is assessed in terms of availability, cost, licensing, and community support. Our findings indicate nuanced differences in performance across tasks, with proprietary models often showcasing superior results in specific domains, while open source alternatives excel in versatility and customization. Accessibility-wise, open source models demonstrate greater flexibility and community-driven enhancements, albeit with potential challenges in maintenance and support. This comparative analysis provides insights into the strengths and trade-offs of each model type, offering valuable guidance for researchers, developers, and organizations navigating the landscape of LLM adoption.