OpenAI o1-preview: Everything you have to know about the new reasoning model

For developers seeking a more efficient solution, OpenAI is introducing o1-mini, a faster and more cost-effective reasoning model that excels in coding tasks. This streamlined version of o1 is 80% less expensive than the o1-preview model, making it an excellent choice for applications that require strong reasoning abilities without the need for extensive world knowledge. Additionally, o1-mini is nearly four times faster than the o1-preview model, allowing for quicker response times and improved productivity. With o1-mini, developers can benefit from enhanced reasoning in a more accessible and budget-friendly manner, enabling innovative projects without significant costs.

Chain of thought refers to a reasoning approach used in AI models where the system breaks down complex problems into smaller, sequential steps to arrive at a solution. Instead of providing a quick, single-shot answer, the model follows a step-by-step process, much like how humans think through a problem. This allows the model to address each part of the task individually, leading to more accurate and thoughtful responses. By using this method, the AI can handle more intricate tasks, catch potential errors along the way, and explain its reasoning more clearly. In essence, chain of thought enables models to think more logically and methodically, improving their performance on tasks that require deep reasoning or multi-step problem solving.

Let’s visualize chain of thought reasoning with an example:

Question:
If a train travels 60 miles per hour and the distance to the destination is 180 miles, how long will the journey take?

Chain of Thought Reasoning:

1. Step 1: The train is traveling at a speed of 60 miles per hour.

2. Step 2: The total distance to the destination is 180 miles.

3. Step 3: To find the time, we can use the formula: time = distance ÷ speed.

4. Step 4: Plug in the numbers: time = 180 miles ÷ 60 miles per hour.

5. Step 5: The result is 3 hours.

Final Answer: The journey will take 3 hours.

In this example, instead of just giving the answer "3 hours," the AI breaks down the problem into smaller steps, reasoning through each one. This chain-of-thought process not only ensures the correct answer but also makes it easier for humans to follow the logic and see how the solution was reached.

According to OpenAI, in a recent test based on qualifying questions from the International Mathematics Olympiad (IMO), GPT-4o managed to solve just 13% of the problems, whereas new o1 achieved a remarkable 83 % success rate. While o1 is still an early model and lacks some of the user-friendly features found in ChatGPT - like web browsing and file or image uploads - it significantly outperforms GPT-4o in tasks requiring advanced reasoning. Although GPT-4o remains more practical for everyday tasks at the moment, o1 represents a major step forward in AI's ability to tackle complex tasks and solve harder problems. 

Furthermore, OpenAI o1 ranks in the 89th percentile on competitive programming challenges and demonstrates exceptional performance on standardized tests, surpassing human PhD-level accuracy in benchmarks for physics, biology, and chemistry. 

This step forward in reasoning power is why OpenAI has decided to reset its model naming convention with the introduction of o1, marking the start of a new chapter in AI development.

Below you can find a detailed comparison of the technical aspects of both OpenAI models:

o1 is particularly suited for tasks that require advanced reasoning, making it ideal for fields like science, math, and coding. Its ability to think through complex problems with clarity allows it to address challenges that involve multiple steps or intricate logic. Whether you’re working on scientific research, developing software, or analyzing mathematical data, o1's reasoning capabilities can help navigate through these complexities efficiently. This makes it an excellent tool for professionals who deal with highly technical and intellectually demanding work.

OpenAI o1 is priced at $15 per million input tokens and $60 per million output tokens, while the more affordable OpenAI o1-mini costs $3 per million input tokens and $12 for output tokens. On the other hand, GPT-4o offers a much lower rate of $2.50 for input tokens and $10 for output tokens. While GPT-4o models are significantly more cost-effective, OpenAI’so1 models may be a better choice if higher reasoning capabilities are required. 

The release of o1 is definitely a major leap forward in AI development. Instead of just making bigger models, we're now building specialized tools that can think more like human experts. To sum up, o1 excels at tackling complex problems in science, math, and coding because it can reason through issues step by step. GPT-4o, on the other hand, is designed for speed, providing faster and more efficient answers when time is of the essence. Different AI models have different strengths, so companies and users should choose the one that best fits their needs. This shift highlights how AI is becoming more versatile and human-like, capable of handling a variety of challenges more effectively.