A Comparison of Different Generative AI Models

Year : 2026 | Volume : 13 | Issue : 01 | Page : 16 22
    By

    Arya Bandal,

  • Samiya Attar,

  • Sakshi Badak,

  1. Student, Department of Electronics and Telecommunication, Rajgad Dnyanpeeth’s Shri Chhatrapati Shivaji Maharaj College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  2. Student, Department of Electronics and Telecommunication, Rajgad Dnyanpeeth’s Shri Chhatrapati Shivaji Maharaj College of Engineering, Savitribai Phule Pune University, Maharashtra, India
  3. Student, Department of Electronics and Telecommunication, Rajgad Dnyanpeeth’s Shri Chhatrapati Shivaji Maharaj College of Engineering, Savitribai Phule Pune University, Maharashtra, India

Abstract

Generative models have significantly advanced the field of artificial intelligence by allowing machines to produce complex and realistic outputs such as images, text, and other forms of data. Among the leading frameworks in this domain are generative adversarial networks (GANs), variational autoencoders (VAEs), and architectures based on Transformers. Each model offers specific benefits and drawbacks concerning design structure, training demands, and range of applications. This paper provides a detailed comparison of these generative techniques, with a focus on core aspects like accuracy, computational cost, and usability in real-world scenarios. We delve into the foundational concepts behind each model, evaluate their performance using widely accepted metrics, and analyze their effectiveness across tasks such as image generation, language modeling, and anomaly identification. The study outlines the trade-offs between flexibility, robustness, and scalability, aiming to guide practitioners in choosing the best-suited model for particular use cases. Finally, the paper discusses prospective research pathways to further enhance the power and versatility of generative models.

Keywords: Generative models, generative adversarial networks (GANs), variational autoencoders (VAEs), Transformers, neural networks, artificial intelligence, image synthesis, machine learning models, computational efficiency, likelihood estimation, performance evaluation

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Arya Bandal, Samiya Attar, Sakshi Badak. A Comparison of Different Generative AI Models. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):16-22.
How to cite this URL:
Arya Bandal, Samiya Attar, Sakshi Badak. A Comparison of Different Generative AI Models. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):16-22. Available from: https://journals.stmjournals.com/joaira/article=2026/view=237198


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 12/04/2025
Accepted 11/09/2025
Published 19/02/2026
Publication Time 313 Days
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