GPT-5 and open-weight large language models: Advances in reasoning, transparency, and control

Abstract

The rapid evolution of Generative Pre-trained Transformers (GPTs) has revolutionized natural language processing, enabling models to generate coherent text, solve mathematical problems, write code, and even reason about complex tasks. This paper presents a scientific review of GPT-5, OpenAI’s latest flagship model, and examines its innovations in comparison to previous generations of GPT. We summarize the model’s architecture and features, including hierarchical routing, expanded context windows, and enhanced tool-use capabilities, and survey empirical evidence of improved performance on academic benchmarks. A dedicated section discusses the release of open-weight mixture-of-experts models (GPT-OSS), describing their technical design, licensing, and comparative performance. Our analysis synthesizes findings from recent literature on long-context evaluation, cognitive biases, medical summarization, and hallucination vulnerability, highlighting where GPT-5 advances the state of the art and where challenges remain. We conclude by discussing the implications of open-weight models for transparency and reproducibility and propose directions for future research on evaluation, safety, and agentic behavior.