Understanding the Difference Between Chat Models (GPT-4o) and Reasoning Models (OpenAI’s O3-Mini)

If you’ve interacted with ChatGPT, you’ve used a chat model, but did you know there are also reasoning models designed for different types of AI tasks? I've been working with both and I see each have their strengths. Understanding these strengths will make it easier for you to pick the right tool for the need.

Here’s how they differ and when to use each.

Chat Models (e.g., GPT-4o) – Designed for Language and Content Generation

A chat model, like OpenAI’s GPT-4o, is built for:

• Answering questions
• Engaging in natural conversations
• Generating content (emails, reports, code, etc.)
• Summarizing and explaining information

Example Use Case: A manager wants a quick summary of a market research report. They ask GPT-4o: "Can you summarize the key insights from this 20-page report?" GPT-4o scans the text, extracts the main themes, and provides a concise summary that highlights trends and recommendations.

Chat models are best for tasks that require understanding and generating language quickly and coherently.

Reasoning Models (e.g., OpenAI’s O3-Mini) – Built for Structured Analysis and Decision Support

A reasoning model, like OpenAI’s O3-Mini, is designed for structured problem-solving and business analysis. These models focus on:

• Evaluating multiple variables in a scenario
• Identifying patterns and making logical inferences
• Supporting decision-making with structured reasoning

Example Use Case: A company is evaluating whether to expand into a new market. They input data into O3-Mini, such as historical sales figures, economic conditions, and competitor presence, and ask: "Given this data, what are the potential risks and benefits of expanding into this market?" Instead of generating a simple summary, the model breaks down the financial, operational, and competitive factors step by step, highlighting dependencies and potential challenges.

Reasoning models are particularly valuable when business decisions require logical structuring of data, multi-step analysis, and strategic trade-offs.

When to Use Each Model

• Need natural language understanding and content creation? GPT-4o is the right choice.
• Need structured analysis and decision-making support? A reasoning model like O3-Mini is better suited.

In business settings, chat models are great for communication and knowledge retrieval, while reasoning models help analyze scenarios, evaluate trade-offs, and structure complex decisions. This is a topic we'll be adding to our AI Literacy online course.

Major Microsoft Document AI Updates: Greater Flexibility and Lower Costs

For those working with document driven processes, especially those with handwritten forms, highly variable formats like property tax bills, and other structured documents, there are two significant updates that could greatly improve efficiency and reduce costs.

More Flexibility in Data Extraction

A major limitation in document AI has been the requirement for manually labeled training data. Previously, extracting specific elements required tagging multiple sample documents, which was time-consuming and restrictive, particularly for documents with varying formats, such as property tax forms.

With the latest Microsoft Document AI update, this process has been significantly improved. Instead of pre-labeling examples, we can now use unlabeled training, where we describe the required data, and the system will extract it accordingly. This provides far greater flexibility, particularly for documents with high variability, such as emails and multi-format records that require classification.

Real-World Application

We recently applied this new capability in a project with a company, and the results have exceeded expectations. The overall process time savings will be measured in days.

Key improvements include:

• Extracting 20+ data elements from lengthy, multi-page PDFs
• Automating workflows using this data and Power Automate
• Enhancing reporting and analytics through Power BI

Additionally, we have a fully functional solution for property tax form processing available for demonstration. If you are interested in seeing how this works, I would be happy to schedule a session.

Substantially Lower Processing Costs

In addition to these enhancements, there is also a significant reduction in processing costs.

• Current cost: $0.05 per page
• New cost (effective March 1): $0.005 per page!

This represents a 90 percent reduction, making AI-driven document processing far more cost-effective. A 20-page document that currently costs $1 to process will cost only $0.10 starting next month.

Next Steps

These improvements make AI-powered document processing more accessible, efficient, and affordable. If you are exploring automation solutions for high-volume or complex document workflows, this is an excellent opportunity to consider new possibilities.

Thanks for reading! Please let us know what you think and share it with others who can use this information.