A Comparative Analysis of ChatGPT and DeepSeek: Capabilities, Applications, and Future Directions ChatGPT &DeepSeek


Authors : Dr. Rahul Vishwanath Dandage

Volume/Issue : Volume 10 - 2025, Issue 2 - February


Google Scholar : https://tinyurl.com/5cc5f8bp

Scribd : https://tinyurl.com/2me66bk9

DOI : https://doi.org/10.5281/zenodo.14899162


Abstract : The rapid advancement of artificial intelligence (AI) has led to the development of sophisticated language models that are transforming various industries. Among these, OpenAI's ChatGPT and DeepSeek's AI models have garnered significant attention due to their capabilities in natural language processing (NLP), machine learning (ML), and their applications across diverse domains. This paper presents a comprehensive comparison between ChatGPT and DeepSeek, focusing on their architectural differences, performance metrics, applications, and potential future directions. The study is based on a literature review of relevant documents, including technical papers, user guides, and industry reports. The findings suggest that while both models excel in NLP tasks, they differ in their underlying architectures, training methodologies, and specific use cases. The paper concludes with recommendations for future research and development in this field.

Keywords : ChatGPT, DeepSeek, Generative AI, NLP, Machine Learning.

References :

  1. OpenAI. (2023). ChatGPT in the age of generative AI and large language models: A concise survey. arXiv. https://arxiv.org/abs/2307.04251
  2. Pranav, R., Zhang, M., & Sharma, S. (2023). ChatGPT: A meta-analysis after 2.5 months. arXiv. https://arxiv.org/abs/2302.13795
  3. DeepSeek AI. (2024). DeepSeek LLM: Scaling open-source language models with longtermism. arXiv. https://arxiv.org/abs/2401.02954
  4. DeepSeek AI. (2024). DeepSeek-V2: A strong, economical, and efficient mixture-of-experts language model. arXiv. https://arxiv.org/abs/2405.04434
  5. DeepSeek AI. (2024). DeepSeek-Coder: When the large language model meets prog ramming—The rise of code intelligence. arXiv. https://arxiv.org/abs/2401.14196
  6. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI.
  7. Eke, D. O. (2023). ChatGPT and the rise of generative AI: Threat to academic integrity? Journal of Responsible Technology, 13, 100060. https://doi.org/10.1016/j.jrt.2023.100060
  8. Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581. https://doi.org/10.1002/asi.24750
  9. Gabashvili, I. S. (2023). The impact and applications of ChatGPT: A systematic review of literature reviews. arXiv preprint arXiv:2305.18086. https://doi.org/10.48550/arXiv.2305.18086
  10. Bobula, M. (2024). Generative artificial intelligence (AI) in higher education: A comprehensive review of challenges, opportunities, and implications. Journal of Learning Development in Higher Education, (30). https://doi.org/10.47408/jldhe.vi30.113

The rapid advancement of artificial intelligence (AI) has led to the development of sophisticated language models that are transforming various industries. Among these, OpenAI's ChatGPT and DeepSeek's AI models have garnered significant attention due to their capabilities in natural language processing (NLP), machine learning (ML), and their applications across diverse domains. This paper presents a comprehensive comparison between ChatGPT and DeepSeek, focusing on their architectural differences, performance metrics, applications, and potential future directions. The study is based on a literature review of relevant documents, including technical papers, user guides, and industry reports. The findings suggest that while both models excel in NLP tasks, they differ in their underlying architectures, training methodologies, and specific use cases. The paper concludes with recommendations for future research and development in this field.

Keywords : ChatGPT, DeepSeek, Generative AI, NLP, Machine Learning.

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