O3-Pro Is Available – Now What?

You can now choose O3-Pro directly inside ChatGPT if you’re on the Pro or Team plan, or use it through the API. This is a reasoning model, which means it’s not built for chat. Instead of pulling you into a conversation, it walks itself through a chain of tasks to get to the right output - and you can see it doing that. That’s very different from chat-based models, which are designed to go back and forth with you in real time. You’d pick O3-Pro for research-style tasks - when you need the model to ask itself the hard questions, not wait for you to ask them one by one.

🗣️ What the Company Is Saying

OpenAI says O3-Pro is now their top performer when it comes to deep reasoning. In their words, it outperforms everything else they’ve tested, especially in math, education, programming, business, and writing tasks.

“In expert evaluations, reviewers consistently prefer o3‑pro over o3 in every tested category and especially in key domains like science, education, programming, business, and writing help.”
- OpenAI blog

Even Sam Altman didn’t believe how well it performed at first:

“I didn’t believe the win rates relative to o3 the first time I saw them.”
- Sam Altman, CEO

This is OpenAI drawing a clear line between “casual chat” and “serious thinking.”

🧠 What That Means (In Human Words)

You can now choose O3-Pro directly inside ChatGPT if you’re on the Pro or Team plan, or use it through the API. This is a reasoning model, which means it’s not built for chat. Instead of pulling you into a conversation, it walks itself through a chain of tasks to get to the right output - and you can see it doing that. That’s very different from chat-based models, which are designed to go back and forth with you in real time. You’d pick O3-Pro for research-style tasks - when you need the model to ask itself the hard questions, not wait for you to ask them one by one.

Let’s Connect the Dots

There’s been a lot of noise around this release-but not a lot of clarity. “Fast,” “smart,” “next-gen”-we’ve heard it all. But when you need to understand what a model like O3-Pro actually does, information is everything. So let’s break it down.

🧠 What is a reasoning model

When a task is too complex to carry through one conversation at a time, reasoning is your model.

Chat models are built to go back and forth with you - ask, respond, adjust. But some problems need more than that. They need a model that can think for itself, not wait for you to spell out every step.

A reasoning model breaks down the task internally. It asks itself the follow-up questions. It runs through the logic, checks the structure, and only then gives you the result. You’re not part of the process - you’re getting the outcome of it.

That’s why it feels slower. Because it’s not chatting. It’s solving.

💻 What this means to developers

When you use a third-party model through an API, you’re putting real tasks - and sometimes real consequences - in its hands. That’s why accuracy matters.

O3-Pro gives you more of it.

It’s not here to hold a casual chat. It’s built to think through complex tasks, so you don’t have to script every step. That means fewer safety checks, fewer logic patches, and fewer moments of “did it actually get that right?”

If you care about getting the right answer the first time - especially on critical workflows - this model doesn’t just improve your stack. It makes your life easier.

✅ Use cases that are better suited for O3-Pro

Here are 10 examples where O3-Pro fits better than standard chat models:

1. Auditing a 100-page policy document for contradictions

2. Reviewing code for logic errors across multiple files

3. Building a financial model with multiple dependencies

4. Researching a topic and summarising it into structured points

5. Debugging a machine learning pipeline step-by-step

6. Drafting a board-level strategy document

7. Evaluating product-market fit across datasets

8. Writing academic-style responses with references

9. Comparing multiple regulatory frameworks across countries

10. Creating step-by-step decision trees for business logic

📊 Comparison: How does O3-Pro stack up?

To understand its value, you can’t look at it alone. Comparing it to other top-tier models gives context.

Why Perplexity = Medium?
It’s great at finding and summarising facts, but it doesn’t build its own logic chain. It’s fast and helpful - but not a deep thinker.

📌 Bottom Line

• Price:
  O3-Pro: $20 in / $80 out per million tokens (was $150 / $600)
  O3 base model: $2 in / $8 out (was $10 / $40)

• Access:
  Available now in ChatGPT (Pro + Team) and via API

• More Info:
  https://help.openai.com/en/articles/9624314-model-release-notes 

❄️ Stop the AI cult - by having a perspective

Frozen Light Team Perspective

Making O3-Pro available to more people is the kind of move we’d like to see more of. It gives people options. And lowering the price? That’s how you take a new, improved algorithm and actually make it available - not in theory, but in practice.

What stands out to us is how the experience of using previous models keeps showing up in the development of the next gen. It looks like OpenAI took what it learns from us using the modules - how we use them, how we question them, how we challenge them (or didnot 🙂) - and turns that into features we all benefit from. That’s not magic. That’s knowledge turned into design.

But let’s not skip the simple truth underneath all of this:
Progress in AI always comes down to two things.

Infrastructure.
When a new model is first released, it usually comes packed with GPUs and a whole lot of backend support - enough to handle scale and pressure while it’s still learning. Then, slowly, a new improved version shows up. Fewer GPUs, lighter infrastructure, and suddenly more people can use it.
How? Well… apparently we’re not as sophisticated as first expected. 😅
Maybe we’re not asking hard enough questions. Or maybe we don’t trust the model to ask them on its own. Pick whichever version feels true to you.
Either way, infrastructure isn’t just cost - it’s what fuels the evolution. What we do with these models feeds the next generation. That’s how progress actually works.

Memory.
That’s infrastructure too - just with a different purpose. Not for performance, but for presence. For remembering who we are, what we talked about, and what we prefer.
And yes, every time memory comes up, so does the debate about privacy. But let’s be real - memory also supports productivity. One doesn’t come without the other.

When you prioritise model performance - as a user or a developer - you’re also prioritising what gets remembered. What gets stored. And what gets better next time.

Keeping that in mind doesn’t ruin the fun. It keeps the conversation real.
It helps you find the balance - between what we give up, and what we gain.

That’s the kind of evolution we’re here for.

We’re all for this new drop. More access, better tools, lower price - that’s a win.

But like everything else, we’re not here to decide for you. You - your goals, your needs, your reality - will decide if this one’s for you.

We just hope we helped by offering a balanced take, and maybe gave you a few new things to think about.

Got more questions? Come find us. We’ll be happy to help.