Capabilities of GPT-5 across critical domains: Is it the next breakthrough?
Not on the map. Why?
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.
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
4 of 16 references.
References · 4
- 2025Capabilities of GPT-5 on Multimodal Medical Reasoningcited by 36
- 2024Hidden flaws behind expert-level accuracy of multimodal GPT-4 vision in medicinecited by 31
- 2024Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Coursecited by 19
- 2025Performance of GPT-5 in Brain Tumor MRI Reasoningcited by 5
Built on by
Citing papers per year: 2025: 1; 2026: 5.
6 papers.
- 2025BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Modelscited by 8
- 2026IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inferencecited by 7
- 2026From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequencescited by 3
- 2026From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLMcited by 0
- 2026More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideationcited by 0
- 2026OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advicecited by 0
- 2026OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice9 Jul 2026
- 2026IndexMem: Learned KV-Cache Eviction with Latent Memory for Long-Context LLM Inference25 May 2026
- 2026More Than Can Be Said: A Benchmark and Framework for Pre-Question Scientific Ideation7 May 2026
- 2026From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences11 Apr 2026
- 2026From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM6 Mar 2026
- 2025BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models29 Sep 2025