I Spent a Week Coding with OpenAI's o1 Model: Here is What Happened

We’ve all been there. You paste a complex chunk of code into ChatGPT, ask it to fix a subtle bug, and it confidently spits back a solution that looks brilliant—until you actually run it, and everything crashes. You tell it it's wrong, it apologizes profusely, and then hands you the exact same broken code again.

For a while now, Large Language Models (LLMs) have felt incredibly smart, but strangely superficial. They are great at predicting the next word, but terrible at actually stopping to think.

That is exactly the problem OpenAI is claiming to solve with their new o1 model, launched recently in 2024. Unlike GPT-4o, which is designed to give you an answer as fast as possible, the o1 model is built around a "Chain of Thought." It is designed to pause, reason through the problem step-by-step, recognize its own mistakes, and then deliver a highly refined answer.

I got access to the o1-preview and decided to put it through the ultimate test: I used it exclusively for my daily software development tasks for an entire week. Here is what I discovered.

The Good: It Actually Understands Context

The biggest shift I noticed immediately was how o1 handles complex logic. I fed it a messy, legacy Python script that dealt with multi-threaded data processing—the kind of script where fixing one race condition usually creates two more.

Normally, GPT-4o would try to patch the specific line I pointed out. The o1 model did something different. It paused (the interface literally shows it "thinking" for 15-20 seconds), analyzed the entire architecture, and then told me that my approach to the threading pool was fundamentally flawed. It didn't just fix the syntax error; it rewrote the concurrency model entirely. And to my shock, the code compiled and ran flawlessly on the first try.

This ability to hold massive amounts of context and logically follow the implications of a code change is, frankly, incredible. It feels less like talking to a fast-typing intern and more like consulting a senior engineer.

The Bad: It Is Painfully Slow

If you are used to the instantaneous rapid-fire responses of standard ChatGPT, using o1 requires a serious adjustment of your expectations.

Because it forces itself to reason through every step, answers take time. Sometimes a simple query takes 10 seconds; sometimes a complex math or logic problem can leave the model "thinking" for over a minute.

I quickly realized that using o1 for simple tasks—like writing a quick regular expression or formatting a JSON payload—is a massive waste of time. It overthinks trivial problems. You don't need a supercomputer to tell you how to center a div in CSS.

The Ugly: API Costs and Rate Limits

As of late 2024, the reasoning power of o1 comes at a steep price. If you are using the API for development, you need to be very careful. Because o1 generates "invisible" reasoning tokens in the background before giving you the final output, your API bills can skyrocket much faster than they would with GPT-4o.

Additionally, the rate limits on the ChatGPT Plus interface are currently quite strict. I found myself hitting the usage cap halfway through my workday, forcing me to switch back to GPT-4o for the afternoon.

The Verdict: A Glimpse into the Future

After a week of heavy use, my conclusion is that the OpenAI o1 model isn't a total replacement for GPT-4o yet. Instead, it is a highly specialized tool.

When you are brainstorming, writing emails, or need quick syntax help, standard models are still the way to go. But when you are stuck on a brutal algorithmic bug, trying to design a complex database schema, or need help understanding deeply nested logic, o1 is an absolute game-changer.

It proves that the next frontier of AI isn't just about knowing more information; it's about the ability to reason with the information it already has. If you haven't tried handing a difficult logic problem to o1 yet, you are missing out on the future of coding.