Comparative Review of Large Language Models
The rapid advancement of large language models (LLMs) has reshaped the field of natural language processing, enabling a wide range of applications from intelligent assistants to automated content generation. This review offers a comparative analysis of six widely used LLMs: ChatGPT (OpenAI), Claude 3 (Anthropic), Gemini 1.5 Pro (Google DeepMind), Mixtral (Mistral AI), DeepSeek-V2 (DeepSeek AI), and Perplexity LLM (Perplexity.ai). We evaluate these models based on architecture, functionality, performance benchmarks, safety measures, openness, cost, and ecosystem integration. Proprietary models like Claude, Gemini, and ChatGPT excel in performance and multimodal support, while open-source models like Mixtral and DeepSeek provide transparency and adaptability. Perplexity uniquely incorporates real-time web search for enhanced factual accuracy. This review aims to assist researchers, developers, and decision-makers in selecting appropriate LLMs for various academic, industrial, or commercial needs, and highlights the emerging trends shaping the future of AI language systems.
Authors
- P. Manikandan (ORCID: https://orcid.org/0000-0002-5737-0235)
- C. Swedheetha
- T. Akilan
- B. Pushpavanam
- M. Saravanan
- Naveen Palanichamy (ORCID: https://orcid.org/0000-0003-4601-9770)
Institutions
- Velammal Medical College Hospital and Research Institute (IN)
- PSG INSTITUTE OF TECHNOLOGY AND APPLIED RESEARCH (IN)
- Social Service Sericulture Project Trust (IN)
- Sri Eshwar College of Engineering
- Kalasalingam Academy of Research and Education (IN)
Publication Details
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1109/icscsa66339.2025.11170906
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00