What Is the Difference Between GPT-4, GPT-4o, and o1? A Guide for Non-Technical Executives

GPT-4 was OpenAI's flagship model released in early 2023. It represented a substantial improvement over GPT-3.5 in reasoning, instruction following, and handling of complex tasks, but was slower and more expensive to run.

GPT-4o (the 'o' stands for 'omni') is a redesigned version released in 2024. It is significantly faster and cheaper than the original GPT-4 while maintaining comparable or better performance on most tasks. It is also natively multimodal, meaning it can process text, images, and audio within the same model architecture rather than through separate systems. GPT-4o is what most ChatGPT users interact with and is the default model in Microsoft Azure OpenAI deployments.

GPT-4o mini is a smaller, faster, cheaper version of GPT-4o suitable for high-volume tasks where cost efficiency matters more than maximum capability: classification, summarisation, simple question answering.

The o1 model series represents a different design philosophy. Where GPT-4o is optimised for speed and general capability, o1 is specifically optimised for complex reasoning tasks.

o1 'thinks' before answering: it generates an internal chain of reasoning steps before producing its response. This approach significantly improves performance on tasks involving logic, mathematics, code, and multi-step problem solving, at the cost of taking longer and consuming more tokens per response.

For most business tasks (writing, summarising, researching, answering questions), GPT-4o is faster and more cost-effective and will give excellent results. For tasks requiring rigorous step-by-step reasoning (financial modelling analysis, legal argument construction, complex data analysis), o1 provides noticeably better output quality.

OpenAI has subsequently released o3, a more capable successor to o1, and o3 mini, a smaller cost-optimised reasoning model. The pattern of pairing a full capability model with a mini variant for cost-sensitive applications is now consistent across the product line.

For organisations accessing OpenAI's models via Azure OpenAI (which most enterprise customers do for data security reasons), model availability and pricing are distinct from consumer ChatGPT. Microsoft makes selected models available in Azure with a lag from consumer release, and pricing is per token consumed rather than per subscription seat.

The practical implication for enterprise AI workflows is that the choice of model matters for cost management. Using o1 for tasks that GPT-4o handles adequately is an unnecessary cost. Equally, using GPT-4o mini for tasks requiring nuanced reasoning or complex analysis is a false economy if the output quality is insufficient.

A sensible enterprise approach is to default to GPT-4o for general tasks, use GPT-4o mini for high-volume, lower-complexity workflows, and reserve o1 or o3 for specific high-value use cases where complex reasoning is genuinely required.