Year: 2025
Type: article
Abstract: The rapid growth in artificial intelligence (AI) has immensely changed natural language processing (NLP), with two prevalent large language models (LLMs) in the form of DeepSeek and ChatGPT. DeepSeek's Mixture-of-Experts (MoE) model enables efficient scaling, cost-effectiveness, and problem-solving and is, therefore, best for use in STEM, coding, and processing structured information. In contrast, ChatGPT's dense transformer model is best for fluency, conversation, general NLP, customer service, content creation, and interactive use cases. However, DeepSeek's cloud-dependent model raises security concerns and must be locally run via LM Studio or Ollama for added security and information protection. This article compares architectures, training processes, performance tests, and real-life use cases of both LLMs, offering a complete analysis of both the strengths and weaknesses of both models. In the future, AI development must strive for a model with both MoE efficiency and transformer-based fluency, allowing for scalability, accuracy, and cost-effective AI use in industries.
Author: Alex Mathew -
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407528081