What is GPT o1

So, what is o1? OpenAI’s o1 model is their latest iteration focused on advanced reasoning and chain-of-thought processing. Unlike previous models like GPT-4o or GPT-4, o1 is specifically designed to “think” before responding, meaning it doesn’t just generate text but goes through multiple steps of reasoning to solve complex problems before responding. This approach makes it better at tasks that require detailed reasoning, like solving math problems or coding challenges. It’s pretty much like us, thinking before we speak.

When you ask a question, it takes a longer because it’s spending more compute on inference — basically, it’s taking the time to reflect and refine its response. Just as we would ask to “think through it step by step” with Chain-of-Thought prompting, but it does that every time because of how they further trained the model with reinforcement learning to force it to think step by step each time and reflect back before answering. Unfortunately, there is no detail on the dataset used for that other than that it is “in a highly data-efficient training process.”

Key Differences Between o1 and GPT-4o

First, what really sets o1 apart from models like GPT-4o is obviously its built-in reasoning capabilities. In testing, o1 outperformed GPT-4o on reasoning-heavy tasks like coding, problem-solving, and academic benchmarks. One of the standout features of o1 is its ability to chain thoughts together, which means it’s better equipped to tackle multi-step problems where earlier models might have struggled.

For example, in tasks like math competitions and programming challenges, o1 was able to solve significantly more complex problems. On average, o1 scored much higher on benchmarks like the AIME (American Invitational Mathematics Examination), where it solved 74% of the problems, compared to GPT-4o’s 9%.

It also does a great job handling multilingual tasks. In fact, in tests involving languages like Yoruba and Swahili, which are notoriously difficult for earlier models, o1 managed to outperform GPT-4o across the board.

Inference Time and Performance Trade-Off

Here’s where o1’s strengths turn into its potential weakness. While the model is much better at reasoning, that comes at the cost of inference time and the number of tokens. The chain-of-thought reasoning process means that o1 is slower than GPT-4o because it spends more time thinking through problems during inference, so when it talks with you, instead of focusing on using high computes strictly for training the model. It’s pretty cool to see another avenue being explored here, improving the results by a lot, and now viable thanks to the efficiency gains in token generation from recent models continuously reducing generation prices and latency. Still, it increases both significantly.

Hallucination Reduction

Another area where o1 shines is reducing hallucinations — those moments when the model just makes stuff up. During testing, o1 hallucinated far less than GPT-4o, particularly on tasks where factual accuracy is critical. For example, in the SimpleQA test, o1 had a hallucination rate of just 0.44, compared to GPT-4o’s 0.61. This makes o1 more reliable for tasks where getting the facts right is essential.

Final Thoughts on o1So, OpenAI’s new Strawberry, or the o1 model, isn’t such a big leap forward. It’s basically just a better model implementing the chain-of-thought prompting most of us already were using, and it has been done before. The issue is that it took longer to generate and cost more through higher token usage, so people stopped doing it. It seems like OpenAI decided otherwise and went all in on this. Indeed, it’s slower than models like GPT-4o because it takes time to think through problems, but if you need a model that excels at solving complex tasks, o1 is your go-to choice.