If you used ChatGPT between early 2025 and the middle of 2026, you probably saw names like o3-mini, o3, o4-mini and o4-mini-high in the model picker and wondered why OpenAI's naming suddenly looked like a password. These were OpenAI's reasoning models, the "o-series", and they changed what people expected from an AI assistant. Instead of answering instantly, they took time to think. And with o3 and o4-mini, they also learned to use tools on their own and even to "think with images".
This guide explains what o3 and o4-mini were, when they launched, how they worked in plain English, what they were good and bad at, where they stand in October 2026, and what their story teaches you as a student or fresher building a career around AI skills.
What are o3 and o4-mini?
The o-series is a separate line of OpenAI models built for reasoning: working through a problem step by step before giving a final answer. The line started with o1 in 2024. The models in this article came in two waves:
- o3-mini (31 January 2025). A small, fast reasoning model that OpenAI said was optimised for STEM reasoning: science, maths and coding. OpenAI's release notes say Free, Plus, Team and Pro users could access it from launch day, and its announcement called this the first time a reasoning model was available to free ChatGPT users. Free users tried it by selecting "Reason" in the message box. Paid users also got o3-mini-high, a version that thinks harder and answers more slowly.
- o3 and o4-mini (16 April 2025). OpenAI called o3 its "most powerful reasoning model that pushes the frontier across coding, math, science, visual perception, and more." o4-mini was the smaller, cost-efficient sibling, built for fast, high-volume reasoning with higher usage limits. Plus, Pro and Team users got o3, o4-mini and o4-mini-high in the model picker, replacing o1, o3-mini and o3-mini-high. Free users could try o4-mini by choosing "Think" before sending a message.
- o3-pro (10 June 2025). A version of o3 designed to think for longer and give more reliable answers. According to OpenAI's release notes, it appeared in the model picker for Pro and Team users, replacing o1-pro, with Enterprise and Edu following the next week.
Developers could also use these models through OpenAI's API (the paid interface programmers use to build apps), via the Chat Completions and Responses APIs.
How reasoning models work, in plain English
A normal chat model like GPT-4o reads your message and starts writing its answer straight away, one word-piece (a token) at a time. A reasoning model adds a step in the middle: it first produces a hidden chain of thought, a stream of internal "thinking" tokens where it breaks the problem down, tries approaches, checks its work and backtracks. Only then does it write the answer you see. In ChatGPT you would often see a short summary of this thinking while you waited.
Think of it as the difference between a student who blurts out the first answer and one who writes rough work in the margin first. The rough work costs time, but on hard problems it usually pays off.
Two ideas from OpenAI's announcements are worth knowing:
- Reasoning effort. For o3-mini, developers could choose low, medium or high effort, trading speed for depth. In ChatGPT, o3-mini ran at medium by default, and "o3-mini-high" or "o4-mini-high" meant the higher setting.
- More thinking, better answers. OpenAI described training these models with large-scale reinforcement learning (learning by trial and reward) so that they get better the longer they are allowed to think. That is also why o3-pro, which thinks longer, was positioned as the most reliable option.
OpenAI has not published the size or detailed architecture of o3 or o4-mini, so be wary of any article that quotes parameter counts.
What was new: agentic tool use and "thinking with images"
The April 2025 launch was a bigger deal than "a smarter model". OpenAI said o3 and o4-mini were its first reasoning models that could agentically use and combine every tool within ChatGPT: web search, Python for analysing files and data, visual reasoning, and image generation.
"Agentic" means the model decides for itself when a tool would help, uses it, looks at the result, and keeps going, sometimes chaining several tools in one answer. OpenAI's example was a question about summer energy use in California: the model could search for public data, write Python to build a forecast, make a chart and explain the result, all in one go.
The second headline feature was thinking with images. Earlier models could "see" a picture you uploaded. OpenAI said o3 and o4-mini could put the image inside their chain of thought and reason with it, and could rotate, zoom or transform images as part of that reasoning. In practice, that meant they could make sense of a blurry photo of a whiteboard, a hand-drawn diagram, or a sideways picture of a textbook page.
For students, that opened up useful workflows:
Here is a photo of my handwritten solution to this circuit problem. Check each step, tell me where I first went wrong, and explain the mistake without giving me the full answer.
I've uploaded a CSV of my monthly expenses. Use Python to find my three biggest spending categories and plot them as a bar chart.
Alongside the launch, OpenAI also released Codex CLI, an open-source coding agent that runs in your computer's terminal and was built to make use of o3 and o4-mini's reasoning.
Strengths and limitations
Where o3 and o4-mini shone
- Multi-step maths, science and coding. OpenAI said external experts found o3 made 20 percent fewer major errors than o1 on difficult real-world tasks, with particular strength in programming, business consulting and creative ideation.
- Research-style questions. Because they could search, read and run code, they handled "find, compare and calculate" tasks much better than chat models.
- Visual problems. Diagrams, charts, handwritten notes and screenshots of errors.
- o4-mini for volume. When you needed lots of quick reasoning (say, checking twenty practice problems), o4-mini's higher limits made it the practical pick.
Where they struggled
- Speed. Thinking takes time. For a simple question, a reasoning model could feel slow compared with GPT-4o.
- Still not always right. Reasoning reduces errors but does not remove them. The models could still state wrong facts confidently, so checking sources remained essential.
- Casual writing and chat. For friendly conversation or short emails, many users preferred faster chat models.
On safety, OpenAI said it rebuilt its safety training data for these models and evaluated them under its Preparedness Framework, reporting that both stayed below its "High" risk threshold in the tracked categories.
Where do o3 and o4-mini stand in October 2026?
Both have now left ChatGPT:
- o4-mini was retired from ChatGPT on 13 February 2026, together with GPT-4o, GPT-4.1 and GPT-4.1 mini. OpenAI announced this on 29 January 2026.
- o3 was retired from ChatGPT on 26 August 2026, after a 90-day sunset period announced on 28 May 2026 (the same announcement scheduled GPT-4.5's retirement for 27 June 2026). At the time, OpenAI said these changes applied to ChatGPT only, with no API changes.
- o3-mini has been marked as deprecated in the API on OpenAI's announcement page, and the o3 and o4-mini launch page now notes that the models are no longer in ChatGPT and are deprecated in the API. If you're a developer, check OpenAI's API deprecations page for exact shutdown dates rather than relying on any blog, including this one.
What replaced them? The GPT-5 family folded reasoning into the main ChatGPT experience, with "Thinking" modes that decide how long to reason, and today's ChatGPT runs on GPT-6 (Sol for paid plans, Luna for Free and Go), rolled out on 7 and 8 October 2026. The idea the o-series proved, that a model should think before answering hard questions, now lives inside every mainstream model rather than in a separate menu item.
Career lessons from the o-series for students and freshers
The o3 and o4-mini story is useful even though you can no longer pick them in ChatGPT. It teaches three skills that employers increasingly want.
1. Know when to make AI "think"
Fast answers and deep answers are different tools. Learning to recognise which problems need step-by-step reasoning (debugging, maths, planning, comparing options) and which don't is a real skill. In today's ChatGPT, that means knowing when to switch on a Thinking mode or ask the model to reason step by step.
2. Understand agents and tool use
o3 was one of the first widely used models that behaved like an agent: searching, running code and combining results. Roles such as AI application developer, data analyst, automation engineer and QA engineer now often involve building or testing workflows where a model calls tools. If you can explain how a model decides to use a tool, and how you'd check its output, you'll stand out in interviews.
3. Verify, always
A model that reasons well can still be wrong. Make a habit of asking for sources, re-running calculations, and testing code before you trust it. Interviewers love candidates who say "I used AI, and here's how I checked it."
Practical ways to apply this:
- Learning: upload a photo of a solved problem and ask the model to find your first error, not to solve it for you.
- Projects: build a small tool that uses an AI API with web search or code execution, and write up how you evaluated its answers. Our courses can help you with the Python and API basics.
- Resume: describe AI work in concrete terms, for example "built a script that uses an LLM to summarise CSV reports, validated against manual checks". Then test the wording with the Ucanly AI resume review.
- Interviews: practise explaining the difference between a chat model and a reasoning model in two sentences. It's a common warm-up question for AI-adjacent roles.
Where this model fits in the ChatGPT family
Here's the short timeline around the o-series:
- o1 (2024) introduced reasoning models to ChatGPT.
- o3-mini (31 January 2025) brought reasoning to free users and focused on STEM.
- o3 and o4-mini (16 April 2025) added agentic tool use and thinking with images. o3-pro followed in June 2025.
- GPT-4.5 (February 2025) and GPT-4.1 (May 2025 in ChatGPT) were the non-reasoning chat models of the same period.
- GPT-5 and later models merged fast chat and deep reasoning into one system, leading to today's GPT-6.
For the full series, see the ChatGPT models overview on the Ucanly blog. If you're new to these tools altogether, start with our guide on how to use ChatGPT and Claude.
What you should do next
- Try a reasoning mode on a hard problem. Pick one tough question from your syllabus and compare a quick answer with a "thinking" answer in today's ChatGPT.
- Upload an image and reason with it. Photograph a diagram or your handwritten notes and ask the model to explain or check it.
- Watch a tool get used. Ask a question that needs search plus a calculation, and read how the model combines them. Open every source.
- Learn the basics of an AI API. Even one small project calling a model from Python teaches you more than ten articles.
- Write one honest resume bullet about AI-assisted work, with what you built and how you verified it.
- Practise the explanation. Be ready to explain "reasoning model", "tool use" and "agent" simply in an interview.
- Map your next skill. Create a free Ucanly account to see your hire-readiness and a roadmap for what to learn next.
Models come and go. The habit of asking AI to think carefully, and then checking its thinking, is what stays valuable.