# Capabilities of GPT-5 across critical domains: Is it the next breakthrough?

arXiv: https://arxiv.org/abs/2508.19259 · HTML: https://mlsanity.com/paper/2508.19259/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 16 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Capabilities of GPT-5 on Multimodal Medical Reasoning](https://mlsanity.com/paper/2508.08224/) (2025)
- [Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicine](https://mlsanity.com/paper/2401.08396/) (2024)
- [Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course](https://mlsanity.com/paper/2407.05216/) (2024)
- [Performance of GPT-5 in Brain Tumor MRI Reasoning](https://mlsanity.com/paper/2508.10865/) (2025)

## Built on by (most cited first, 6 of 6)

- [BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models](https://mlsanity.com/paper/2509.24210/) (2025)
- [IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference](https://mlsanity.com/paper/2605.25475/) (2026)
- [From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences](https://mlsanity.com/paper/2604.10332/) (2026)
- [From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM](https://mlsanity.com/paper/2603.23520/) (2026)
- [More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation](https://mlsanity.com/paper/2605.06345/) (2026)
- [OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice](https://mlsanity.com/paper/2607.08423/) (2026)

## Abstract (arXiv)

The accelerated evolution of large language models has raised questions about their comparative performance across domains of practical importance. GPT-4 by OpenAI introduced advances in reasoning, multimodality, and task generalization, establishing itself as a valuable tool in education, clinical diagnosis, and academic writing, though it was accompanied by several flaws. Released in August 2025, GPT-5 incorporates a system-of-models architecture designed for task-specific optimization and, based on both anecdotal accounts and emerging evidence from the literature, demonstrates stronger performance than its predecessor in medical contexts. This study provides one of the first systematic comparisons of GPT-4 and GPT-5 using human raters from linguistics and clinical fields. Twenty experts evaluated model-generated outputs across five domains: lesson planning, assignment evaluation, clinical diagnosis, research generation, and ethical reasoning, based on predefined criteria. Mixed-effects models revealed that GPT-5 significantly outperformed GPT-4 in lesson planning, clinical diagnosis, research generation, and ethical reasoning, while both models performed comparably in assignment assessment. The findings highlight the potential of GPT-5 to serve as a context-sensitive and domain-specialized tool, offering tangible benefits for education, clinical practice, and academic research, while also advancing ethical reasoning. These results contribute to one of the earliest empirical evaluations of the evolving capabilities and practical promise of GPT-5.