Exploring DeepSeek: A Survey on Advances, Applications, Challenges and Future Directions

Zehang Deng, Wanlun Ma, Qing-Long Han, Wei Zhou, Xiaogang Zhu, Sheng Wen, Yang Xiang

Abstract: The rapid advancement of large models has led to the development of increasingly sophisticated models capable of generating diverse, personalized, and high-quality content. Among these, DeepSeek has emerged as a pivotal open-source initiative, demonstrating high performance at significantly lower computation costs compared to closed-source counterparts. This survey provides a comprehensive overview of the DeepSeek family of models, including DeepSeek-V3 and DeepSeek-R1, covering their core innovations in architecture, system pipeline, algorithm, and infrastructure. We explore their practical applications across various domains, such as healthcare, finance, and education, highlighting their impact on both industry and society. Further-more, we examine potential security, privacy, and ethical concerns arising from the widespread deployment of these models, emphasizing the need for responsible AI development. Finally, we outline future research directions to enhance the performance, safety, and scalability of DeepSeek models, aiming to foster further advancements in the open-source large model community.