对比GPT-4o与DeepSeek-V3，揭示大模型在安全与开源间的权衡。
Challenges and Applications of Large Language Models: A Comparison of GPT and DeepSeek family of models
- 对比闭源GPT-4o与开源DeepSeek-V3的实现思路
- 闭源模型更安全可靠，开源模型更高效可调
- 适合研究者、开发者与决策者参考模型选型
大型语言模型（LLMs）正重塑人工智能产业，但其研发与部署仍面临诸多挑战。本文综述了构建与使用LLMs中的16项关键挑战，并以两种先进模型为例进行分析：OpenAI的闭源GPT-4o（2024年5月更新）与深度求索的开源Mixture-of-Experts模型DeepSeek-V3-0324（2025年3月）。通过对比，展示了闭源模型在安全性与可靠性上的优势，以及开源模型在效率与可适配性上的潜力。同时，文章探讨了大模型在聊天机器人、代码工具、医疗健康和教育等多领域的应用，指出不同场景下应匹配相应的模型特性。本文旨在为研究人员、开发者及决策者提供对当前大模型能力、局限与最佳实践的理解。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are transforming AI across industries, but their development and deployment remain complex. This survey reviews 16 key challenges in building and using LLMs and examines how these challenges are addressed by two state-of-the-art models with unique approaches: OpenAI's closed source GPT-4o (May 2024 update) and DeepSeek-V3-0324 (March 2025), a large open source Mixture-of-Experts model. Through this comparison, we showcase the trade-offs between closed source models (robust safety, fine-tuned reliability) and open source models (efficiency, adaptability). We also explore LLM applications across different domains (from chatbots and coding tools to healthcare and education), highlighting which model attributes are best suited for each use case. This article aims to guide AI researchers, developers, and decision-makers in understanding current LLM capabilities, limitations, and best practices.
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