From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences

TL;DR

This paper compares the evolution of GPT models from GPT-3 to GPT-5, highlighting technical, functional, and societal changes, and emphasizing the shift from simple language models to complex, integrated AI systems.

Contribution

It offers a comprehensive, comparative analysis of GPT model developments, focusing on capabilities, deployment, limitations, and societal implications, beyond mere size or accuracy improvements.

Findings

GPT models evolved into multimodal, tool-oriented systems

Persistent limitations include hallucination and prompt sensitivity

The transition signifies a broader reformulation of AI deployment and responsibility

Abstract

We present the progress of the GPT family from GPT-3 through GPT-3.5, GPT-4, GPT-4 Turbo, GPT-4o, GPT-4.1, and the GPT-5 family. Our work is comparative rather than merely historical. We investigates how the family evolved in technical framing, user interaction, modality, deployment architecture, and governance viewpoint. The work focuses on five recurring themes: technical progression, capability changes, deployment shifts, persistent limitations, and downstream consequences. In term of research design, we consider official technical reports, system cards, API and model documentation, product announcements, release notes, and peer-reviewed secondary studies. A primary assertion is that later GPT generations should not be interpreted only as larger or more accurate language models. Instead, the family evolves from a scaled few-shot text predictor into a set of aligned, multimodal, tool-oriented, long-context, and increasingly workflow-integrated systems. This development complicates simple model-to-model comparison because product routing, tool access, safety tuning, and interface design become part of the effective system. Across generations, several limitations remain unchanged: hallucination, prompt sensitivity, benchmark fragility, uneven behavior across domains and populations, and incomplete public transparency about architecture and training. However, the family has evolved software development, educational practice, information work, interface design, and discussions of frontier-model governance. We infer that the transition from GPT-3 to GPT-5 is best understood not only as an improvement in model capability, but also as a broader reformulation of what a deployable AI system is, how it is evaluated, and where responsibility should be located when such systems are used at scale.