Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects
Anichur Rahman, Shahariar Hossain Mahir, Md Tanjum An Tashrif, Airin Afroj Aishi, Md Ahsan Karim, Dipanjali Kundu, Tanoy Debnath, Md. Abul Ala Moududi, MD. Zunead Abedin Eidmum
TL;DR
The paper conducts a systematic, cross-model comparison of DeepSeek, ChatGPT, and Gemini to illuminate architectural and data-driven trade-offs across domain-specific inference, broad NLP tasks, and multimodal processing. It employs a PRISMA-guided literature review, diverse datasets, and a standardized benchmarking framework to evaluate accuracy, reasoning, coding, and multilingual capabilities. Key contributions include a state-of-the-art feature survey, a technical comparison of architectures and training efficiency, a dataset-centric analysis, and a discussion of open challenges with future opportunities for hybrid, efficient, and safe AI systems. The findings emphasize that DeepSeek excels in resource-efficient domain reasoning, ChatGPT delivers rapid and versatile dialogue, and Gemini offers strong multimodal integration, suggesting that future systems will likely combine these strengths for practical, scalable AI deployments.
Abstract
Nowadays, DeepSeek, ChatGPT, and Google Gemini are the most trending and exciting Large Language Model (LLM) technologies for reasoning, multimodal capabilities, and general linguistic performance worldwide. DeepSeek employs a Mixture-of-Experts (MoE) approach, activating only the parameters most relevant to the task at hand, which makes it especially effective for domain-specific work. On the other hand, ChatGPT relies on a dense transformer model enhanced through reinforcement learning from human feedback (RLHF), and then Google Gemini actually uses a multimodal transformer architecture that integrates text, code, and images into a single framework. However, by using those technologies, people can be able to mine their desired text, code, images, etc, in a cost-effective and domain-specific inference. People may choose those techniques based on the best performance. In this regard, we offer a comparative study based on the DeepSeek, ChatGPT, and Gemini techniques in this research. Initially, we focus on their methods and materials, appropriately including the data selection criteria. Then, we present state-of-the-art features of DeepSeek, ChatGPT, and Gemini based on their applications. Most importantly, we show the technological comparison among them and also cover the dataset analysis for various applications. Finally, we address extensive research areas and future potential guidance regarding LLM-based AI research for the community.