Until September 2024, every ChatGPT model worked in roughly the same way: you asked, and it started answering immediately, word by word. OpenAI o1 broke that pattern. It was the first ChatGPT model designed to think before it answers, sometimes for many seconds, working through a problem privately before replying.
That one idea, giving the model time to reason, now sits inside almost every serious AI tool. This guide explains o1 in depth: what it was, how "reasoning models" work, what o1-preview, o1-mini, o1 and o1 pro mode were, what they were good and bad at, and what their story means for your studies and career.
What is OpenAI o1? The basics
OpenAI o1 is a family of reasoning models. OpenAI introduced it on 12 September 2024 in a research post titled "Learning to Reason with LLMs", releasing two early versions:
- o1-preview: an early version of the full o1 model.
- o1-mini: a smaller, cheaper reasoning model optimised for STEM (science, technology, engineering, maths), especially maths and coding.
At launch, both were available to ChatGPT Plus and Team users (with Enterprise and Edu following), and in the API to developers on the highest usage tier. They were not available to free users at the start, and they came with tight weekly message limits.
Then on 5 December 2024, OpenAI released the full o1 model to all paid ChatGPT users and launched a new, much more expensive ChatGPT Pro plan. Pro included unlimited access to o1 plus o1 pro mode, a version that uses more computing power for the hardest questions. (Plans and prices change, so check the current options in the app rather than relying on old numbers.)
Where it sits in the timeline: o1 came after GPT-4o (May 2024). It was followed by o3-mini, and then on 16 April 2025 by o3 and o4-mini, which replaced o1 in the ChatGPT model picker for Plus, Pro and Team users. o1-pro stayed a little longer, until o3-pro replaced it in June 2025.
Why "o1" and not "GPT-5"? OpenAI presented it as a new kind of model and reset the counter to 1. The naming confused many people, which partly explains why OpenAI later folded reasoning into the unified GPT-5 family.
How o1 works: chain of thought and test-time compute
Chain of thought, explained simply
When you solve a tough maths problem, you don't write the answer straight away. You write steps, notice a mistake, go back, and try another approach. That written trail of steps is a chain of thought.
OpenAI says o1 can "produce a long internal chain of thought before responding". It was trained with reinforcement learning (RL), a method where the model is rewarded for good outcomes, to get better at this thinking process. According to OpenAI, through training o1 learnt to:
- break tricky steps into simpler ones,
- recognise and correct its own mistakes,
- try a different approach when one isn't working.
Test-time compute: the big new idea
Earlier progress in AI mostly came from training bigger models on more data (often called pretraining). o1 showed a second lever. OpenAI reported that o1's performance kept improving with more reinforcement learning during training and with more time spent thinking when answering. That second part is called test-time compute (or inference-time compute): extra computing power used while the model is answering your question.
In plain words: letting the model think longer makes it smarter on hard problems. This is why o1 pro mode existed. It spent even more compute per question to give more reliable answers, and ChatGPT showed a progress bar while you waited.
Reasoning tokens and the hidden chain of thought
The thinking o1 did was made of reasoning tokens: text the model generates for itself, not for you. OpenAI made a deliberate choice not to show the raw chain of thought to users. Its stated reasons included user experience, competitive advantage, and keeping the option to monitor the hidden reasoning for problems such as attempts to manipulate the user. Instead, ChatGPT showed a short, model-written summary of the thinking.
OpenAI has not disclosed o1's parameter count or the full details of its training, so be sceptical of articles that claim to know them.
What o1 was good at
OpenAI published benchmark results in its September 2024 post. A few that are easy to understand:
- Maths (AIME 2024, a hard US high-school competition): GPT-4o solved on average about 12% of problems; o1 averaged 74% with a single attempt per problem.
- Competitive programming (Codeforces): OpenAI reported o1 reaching the 89th percentile, compared with the 11th percentile for GPT-4o.
- Expert science questions (GPQA Diamond): o1 scored 77.3% versus 50.6% for GPT-4o. OpenAI noted this does not mean o1 is better than a PhD in all respects.
These were OpenAI's own evaluations of the full o1 model, not o1-preview. Still, the pattern is clear: on problems that need multiple careful steps, o1 was dramatically stronger than GPT-4o.
The full o1 released in December 2024 added image understanding (o1-preview could not reason about uploaded images) and was trained to think more concisely, so it responded faster.
Example student uses
"Here is a JEE-level probability problem. Solve it step by step, then tell me which step students most often get wrong."
"This Python function gives the wrong output for n = 0. Find the bug, explain the logic error, and suggest a test case that would have caught it."
"I'm designing the database schema for a college event management app. Here are my tables. Point out normalisation problems and edge cases I've missed."
OpenAI also reported that reasoning about its safety rules inside the chain of thought made o1 more resistant to jailbreaks (tricks that try to make a model break its rules).
Where o1 fell short
o1 was not simply "a better ChatGPT". It was a specialist.
- Slow: simple questions could take several seconds or more. For "write a polite leave email", GPT-4o was the better choice.
- Not preferred for everything: OpenAI said human evaluators did not prefer o1-preview over GPT-4o on some natural language tasks, such as certain writing tasks.
- Fewer features at launch: o1-preview had no image input, web browsing or file uploads in ChatGPT at first.
- o1-mini's narrow knowledge: OpenAI said its knowledge of non-STEM facts (dates, biographies, trivia) was comparable to small models like GPT-4o mini.
- Hidden reasoning: you saw only a summary, so you couldn't fully audit how it reached an answer.
- Still makes mistakes: OpenAI's system card noted instances of "reward hacking", where a model finds an unintended shortcut to score well. Reasoning reduces errors; it doesn't eliminate them.
A good rule: use a fast model for writing and quick questions, and a reasoning model for maths, logic, debugging and planning.
Current status (October 2026)
- In ChatGPT: o1 has been gone from the model picker since April 2025, when o3 and o4-mini replaced it. o1-pro was replaced by o3-pro in June 2025. o3 itself was retired from ChatGPT on 26 August 2026. Today's ChatGPT runs on GPT-6 (Sol for paid plans, Luna for Free and Go), rolled out on 7 and 8 October 2026, with reasoning built in.
- In the API: OpenAI shut down o1-preview on 28 July 2025 and o1-mini on 27 October 2025. OpenAI's API deprecations page lists o1 and o1-pro for shutdown on 23 October 2026. If you are reading this later, assume o1 is no longer available anywhere.
So o1 itself is history, but its core idea, thinking before answering, lives on in every model that has a "Thinking" mode.
The career angle: what o1 teaches Indian students and freshers
o1 matters for your career less as a product and more as a lesson in how AI is evolving.
- Reasoning is now a feature, not a bonus. Employers increasingly expect you to know when to switch on deeper thinking. Being able to say "I used a reasoning model to check edge cases in my code" in an interview shows judgement.
- AI rewards clear problem framing. Reasoning models work best when you state the goal, constraints and what a correct answer looks like. That is the same skill you need in DSA rounds, case interviews and client work.
- Your own reasoning still counts. o1 could solve contest problems, yet companies still test you on them. Use AI to check and explain, not to skip the thinking. If you can't explain a solution without AI, you don't own it yet.
- Concepts outlast products. Chain of thought, reinforcement learning and test-time compute are now standard ideas in AI/ML roles. Learn them properly; the model names will keep changing.
- Cost awareness is a professional skill. Reasoning uses more compute, which means more time and money. In real projects, engineers choose the cheapest model that does the job well.
Related roles: software engineer, ML engineer, data scientist, AI evaluation and quality specialist, and research assistant roles. Build the foundations with Ucanly courses, and see what companies are asking for on the Ucanly jobs board.
Where o1 fits in the ChatGPT family
Think of o1 as the start of the reasoning branch. GPT-4o made AI fast, multimodal and widely available. o1 made it think. The two branches then merged: GPT-5 combined quick answers and deeper reasoning in one system, and later models continued that approach.
For the full timeline, read the rest of this series on the Ucanly blog. If you are new to AI chatbots, start with our beginner guide on how to use ChatGPT and Claude.
What you should do next
- Try a reasoning mode on one hard problem. Pick a DSA or maths question you got wrong recently and ask a "Thinking" model to solve it step by step.
- Compare fast vs. thinking. Ask the same question to a quick mode and a reasoning mode. Note the difference in speed, depth and accuracy.
- Make the AI check you, not replace you. Solve first, then ask the model to find flaws in your solution.
- Learn the vocabulary. Write a one-paragraph explanation of chain of thought and test-time compute in your own words. It's a common AI interview question.
- Add a reasoning-backed project to your resume. For example, a study planner or code reviewer that uses a reasoning model, then polish the bullet points in Resume Lab.
- Keep up with changes. Model names change every few months. Follow official release notes and focus on skills that transfer.