Why GPT-4.5 is Important for the Future?
Although its performance is lower than reasoner models...
Dear Medical Educator,
OpenAI released GPT-4.5. It is a significant step forward in scaling base models. While it does not outperform the highest reasoning models like OpenAI’s o3-mini in specific benchmarks (see the image below), its role in AI development remains critical. Why?
Understanding Base Models (System 1 Thinking) vs. Reasoning Models (System 2 Thinking)
My previous post discussed System 1 and System 2 thinking in AI models, explaining how non-reasoner models like GPT-4o rely on intuitive responses (System 1), while reasoning models like o1 and o3 use deliberate step-by-step logic (System 2).
There are two main paradigms in AI:
Base Models (e.g., GPT-4o, Claude 3.5) – These models learn from vast amounts of text, helping them recognize patterns, generate creative ideas, and understand a wide range of topics.
Reasoning Models (e.g., o1, o3) – These models employ explicit step-by-step reasoning (System 2 Thinking) to solve complex problems in STEM, logic, and structured tasks.
GPT-4.5 belongs to the first category, meaning it lacks advanced structured reasoning but excels in intuitive responses, creative writing, and broad domain knowledge.
Why Base Models Matter
Although reasoning models achieve superior results in STEM tasks (e.g., OpenAI o3-mini scoring 87.3% on AIME '24 math vs. GPT-4.5’s 36.7%), they are built on top of base models. This makes base models essential for two key reasons:
Better World Knowledge – GPT-4.5 enhances the general knowledge foundation, making it more reliable for factual accuracy . This improvement will benefit reasoning models as they rely on the base model’s pre-trained knowledge before applying their logical steps.
Scalability & Versatility – Unlike specialized reasoners, GPT-4.5 is designed for a wide variety of tasks, from casual conversations to creative problem-solving. While it may not reason as deeply, its balance of efficiency and broad understanding makes it more accessible for everyday AI use.
Hybrid Approach
The future of AI likely lies in hybrid models that blend world knowledge with reasoning capabilities—similar to Claude 3.7 Sonnet’s approach, which we explored in the previous post: What Makes Claude 3.7 Sonnet Better Than o1? Similarly, GPT-5 is expected to integrate both System 1 and System 2 thinking, combining broad knowledge with advanced reasoning.
In short, while GPT-4.5 does not “win” against the best reasoning models in structured tasks, it plays a crucial role in AI evolution, ensuring better world knowledge with less inaccuracies, more natural interactions, and therefore a stronger base for future advanced models.
Yavuz Selim Kıyak, MD, PhD (aka MedEdFlamingo)
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Related #MedEd reading:
Kıyak, Y. S. (2024). Beginner-Level Tips for Medical Educators: Guidance on Selection, Prompt Engineering, and the Use of Artificial Intelligence Chatbots. Medical Science Educator, 34, 1571–1576. https://link.springer.com/article/10.1007/s40670-024-02146-1
Kıyak, Y. S., & Kononowicz, A. A. (2024). Case-based MCQ generator: a custom ChatGPT based on published prompts in the literature for automatic item generation. Medical Teacher, 46(8), 1018-1020.. https://www.tandfonline.com/doi/full/10.1080/0142159X.2024.2314723
Kıyak, Y. S., & Emekli, E. (2024). ChatGPT prompts for generating multiple-choice questions in medical education and evidence on their validity: a literature review. Postgraduate Medical Journal, 100(1189), 858-865. https://academic.oup.com/pmj/advance-article/doi/10.1093/postmj/qgae065/7688383
Kıyak, Y. S., & Emekli, E. (2024). A Prompt for Generating Script Concordance Test Using ChatGPT, Claude, and Llama Large Language Model Chatbots. Revista Española de Educación Médica, 5(3). https://revistas.um.es/edumed/article/view/612381