A Unique and Practical Deep Dive into the OpenAI o3 Model: Comprehensive Explanation Including Comparative Analysis with ChatGPT-4o and DeepSeek R1, Search Features, Coding, Advanced Data Analysis, and More!

Hello everyone!
The evolution of AI models has been remarkable lately, hasn't it?

In this video, I have actually used OpenAI's new 'o3' model and verified its differences from ChatGPT-4o. I would like to introduce the use cases I've identified for each.

I also discuss how to use it in conjunction with DeepSeek. By comparing them, I will share the appeal of 'o3' and how to choose between them!

In addition, I cover how to use search features, coding, advanced data analysis, and more—it's a content-packed episode!

[Related Videos]

In the video, I also explain 'complex system prompts,' which are advanced analyses for tackling complicated problems, so please be sure to watch it. How good is o3's analytical and problem-solving capability?

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What is the o3 model?

The newly announced 'o3' from OpenAI is a large language model with advanced reasoning capabilities that surpass previous AI models. This model excels particularly in complex tasks such as mathematics, programming, and scientific reasoning.

One of the features of o3 is its ability to solve problems step-by-step using a technique called 'Chain of Thought.' This allows it to handle complex problems with high precision. Additionally, o3 has a self-verification function that can improve the accuracy of its answers.

Furthermore, a lightweight version called 'o3-mini' has also been released. It is cost-effective and offers the flexibility for users to adjust processing speed as needed. o3-mini is particularly well-suited for use in the fields of science and mathematics.

As such, o3 and o3-mini are important steps in pioneering the future of AI, and their application is expected in various fields. I'm looking forward to seeing how they develop!

Basic characteristics of the o3 model

The most interesting point about the o3 model is its 'science-oriented' approach. It demonstrates particularly excellent performance in fields such as data processing, analysis, and coding.

Simply put, one might describe the o3 model as having a 'science-oriented' approach, while ChatGPT-4o has a 'humanities-oriented' approach. This difference in characteristics significantly impacts the actual user experience.

Practical verification of context comprehension

The most notable finding in the actual verification was the difference in context comprehension. Compared to ChatGPT-4o, the o3 model showed some weakness in terms of context understanding. This suggests that while the o3 model is specialized for mathematical thinking and logical processing, its ability to understand context and explore latent space may be relatively weaker.

For example, in a context comprehension test regarding the Default Mode Network (DMN), the o3 model tended to rely on search features to obtain accurate information. When search was restricted, it was observed to struggle in situations requiring meaning comprehension from context.

This may be because the o3 model limits context comprehension for text generation in order to specialize as a mathematical model.

Therefore, it seems that ChatGPT-4o is better for everyday use.

So, in what situations should o3 be used?
I will explain that to you now!

Practical Use Cases

News Search and Summarization Features

On the other hand, the O3 model truly shines in situations where data handling and information structuring are required. In news search and summarization, it is highly responsive to prompt control, accurately extracting necessary information and providing it in an organized format.

Meanwhile, when searching with ChatGPT-4o, complex search prompts could not be executed. Looking at this, the o3 model might be better for searching!

Coding and Canvas Features

What is particularly noteworthy is code generation. Since you can immediately verify it using the Canvas feature, I think the combination of o3 and Canvas is quite good. For tasks requiring mathematical processing or visual output, the O3 model demonstrates stable, high performance.

Characteristics of O3 Model Output

O3 model output tends to be very structured and organized. However, this can sometimes give a 'stiff' impression. In terms of flexibility and natural expression in writing, I feel that ChatGPT-4o is superior.

Prompt control is relatively easy, and it follows instructions accurately, especially regarding information structuring and format specification. However, if you want a more natural writing style or flexible expression, additional prompt adjustments may be necessary.

For this case, I recommend adding prompts like 'write a long text' or 'use prose format'!
Sometimes, prompts like 'be emotionally expressive' or 'be empathetic' might also be good.

I have covered this in past videos, so if you want to know about other additional prompts, please check out my past articles.

Points for Choosing Between Models

Situations where the O3 model is particularly effective

• When data analysis or numerical processing is required

• Code generation and technical tasks

• When structured information output is required

• Situations where accuracy and consistency are critical

Situations where ChatGPT-4o is suitable

• When more natural text generation is needed

• Tasks where contextual understanding is important

• Situations requiring creative thinking

• When flexible interaction is required

[Insert the following section before the 'Utilization in Long-form Generation' section of the previous blog]

Further Differentiation: 'o3 Model vs. DeepSeek': Differences in Expertise and Versatility

Here, let's take a closer look at the o3 model. In fact, the ChatGPT series, including the o3 model, has its own unique strengths. That is the ability to provide general and versatile answers.

For daily questions, general information provision, and basic analysis, the ChatGPT series is easier to use and allows for more understandable interaction. In particular, the ability to provide balanced answers across a wide range of topics is a major benefit for many users.

However, in specific specialized fields, especially in Eastern philosophy, comparative cultural studies, and situations requiring deep academic analysis, DeepSeek is overwhelmingly superior. This is thought to be because DeepSeek's training data itself contains a lot of more specialized and academic content.

In fact, even for specialized questions that the o3 model cannot answer, DeepSeek provides detailed analysis and insights. This shows that the difference in data quality has a significant impact on the AI's response capabilities.

Therefore, if more specialized analysis or research is required, I recommend using an AI specialized in that field (in this case, DeepSeek). On the other hand, for daily use and general information gathering, the ChatGPT series may be more suitable.

However, in terms of safety, ChatGPT is better. Various risks have been pointed out regarding DeepSeek. Therefore, if you want to use DeepSeek relatively safely, you should use a platform that does not go through China. Please see below for details (video included).

Summary: Towards the Utilization of Complementary AI Models

The o3 model and ChatGPT-4o each have different strengths. The o3 model excels in mathematical and logical processing, while ChatGPT-4o demonstrates strength in contextual understanding and natural interaction.

These models are considered to be complementary rather than competing with each other. By using them differently depending on the purpose and situation, more effective AI utilization will be possible.

For example, when creating technical documents, one could combine them by using the o3 model to organize and structure the technical content, and then using ChatGPT-4o to improve the natural flow of the text.

The evolution of AI tools continues day by day. As we explore new possibilities, it will become increasingly important to find ways to utilize each model by leveraging its unique characteristics.

I encourage everyone to understand the features of each model and try using them selectively according to your specific goals!

[Profile]
Wonder Motohiko Sato
After working at medical and psychology research institutes, he became independent and is now conducting research on AI and the mind-body connection.
He is the author of "Oriental Medicine and the Latent Motor System," has written for professional journals for two years, and is currently developing AI co-creation writing methods.
He is developing the field of AI Co-creation Studies by applying techniques from psychology, counseling, and coaching to AI.