<span style="mso-fareast-language: ZH-TW;">Advantages and Limitations of Open-Source Versus Commercial Large Language Models (LLMs): A Comparative Study of DeepSeek and OpenAI’s ChatGPT

Abstract

Large Language Models (LLMs) emerged as powerful text-processing frameworks with diverse applications, including code generation, text summarization, and research support. Contemporary LLM development followed two approaches: open-source LLMs emphasizing transparency, collaborative refinement, customization, and commercial platforms leveraging proprietary methods within hosted service ecosystems. This paper systematically compared these paradigms through two representative cases—DeepSeek, an open-source LLM, and ChatGPT, a commercial offering. Following a structured analytical framework, we examined essential benefits, significant limitations, and implementation considerations for both systems, supported by recent academic literature. Our findings revealed that open-source LLMs offered superior transparency, customization flexibility, and data governance control, yet faced challenges related to infrastructure requirements, fragmented support ecosystems, and security vulnerabilities. Conversely, commercial LLMs provided robust baseline performance, streamlined deployment, and integrated safety mechanisms, but presented concerns regarding usage costs, architectural constraints, and vendor dependency. Domain-specific analyses demonstrated that DeepSeek excelled in specialized tasks after fine-tuning, particularly in computational domains, while ChatGPT offered higher out-of-the-box performance for general applications. Infrastructure considerations highlighted that while self-hosted models eliminated recurring fees, they demanded substantial technical expertise and computational resources. This comparative analysis aimed to provide valuable insights for organizations and researchers planning to implement LLM technologies in specialized or production contexts, offering a framework for strategic decision-making aligned with specific operational priorities, available expertise, and regulatory requirements.

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