o-sorozat (OpenAI)

in English: OpenAI o-series (o1, o3, o4-mini)

advancedwidespread

OpenAI's o1, o3, and o4-mini models reason step-by-step before answering, making them slower but more reliable for complex problems.

In plain terms

The o-series is the family of so-called reasoning models from OpenAI. While the traditional chat model answers almost immediately, o-models first 'think': they work through the task internally and only then respond.

It is like two students taking a math test. One writes the answer from memory, the other works it out on a draft sheet, checks it, and only then writes the final number. The second is slower but makes fewer mistakes on difficult problems.

That is why the o-series is useful where several steps must be built on one another: in mathematical and logical tasks, programming, longer analysis or planning. For a simple letter or a quick question it is unnecessary, because it is more expensive and you have to wait longer for the answer.

The series' significance goes beyond specific models: it made the solution of giving the model thinking time common. Today, almost every major developer offers such a mode.

How it works

You enter the question, and the model does not start answering immediately. First, it prepares an internal thought process: it breaks the problem into subtasks, tries out solution paths, and seeks a new direction if it hits a dead end. This behavior was trained into it using reinforcement learning.
This reasoning process is generated as text in tokens just like the response, but you do not see it in full, receiving at most a brief summary.
Thinking is time- and compute-intensive: a response can take from a few seconds to several minutes to prepare, and you must also pay for hidden thinking tokens.
Developers can set how much effort the model should exert (low, medium, high), allowing them to balance speed, cost, and thoroughness.
Later members (o3, o4-mini) also use tools while thinking: they search the web, run code, and analyze images. This makes them a good foundation for independently working agents.

An everyday example

In ChatGPT, you ask a complex question, for example, which of three loan offers is better over ten years. On the screen, it shows for half a minute that the model is 'thinking,' and only then comes the reasoned answer. At such times, a reasoning model is working, like those made famous by the o-series.

Who is affected

developerIt must decide when a slower, more expensive reasoning model is worth it and when a fast, cheap one suffices, such as when building a coding agent. → Code

employeeFor complex analysis, tabular calculations, or planning, you get better results if you choose the reasoning mode. → Work & HR

entrepreneur, executiveWhen planning AI costs, it matters that reasoning models are slower and more expensive, so it is worth deciding per task where the reasoning mode delivers real value.

parent, student, teacherThese models can solve most school math and physics problems, including the derivations. This can aid learning, but it also makes it easier for students to have the AI write their homework, and the derivations may also be incorrect. → Education & Family

A common misconception

MythO-models think like humans, so whatever they output is guaranteed to be correct.

In reality"Thinking" here means the model reaches the answer in multiple steps by writing intermediate text. This makes it less prone to errors, but it can still make mistakes and confidently state falsehoods.

Limits and risks

It is slower and more expensive than a traditional model, so it is not worth using for simple questions.
Long-chain reasoning is no guarantee: models can reach incorrect conclusions with convincing derivations, so verify important calculations.
You do not see the full internal reasoning, only a summary, making it difficult to judge exactly how the answer was reached.
Model names become obsolete quickly: o1, o3, and o4-mini have been replaced by newer models, so recommendations in older articles may no longer be valid.

Similar, but not the same

GPT-4o – The 'o' at the end of GPT-4o comes from 'omni' (handling text, images, and audio alike), and it is a fast, general model, not a member of the reasoning o-series.
Chain of thought – Chain-of-thought is a method that allows you to ask any model to reason step by step, while the o-series is a specific product family that has this functionality built in by default.
GPT-5 – GPT-5 is a later model family from OpenAI that combines fast response with the reasoning mode known from the o-series in a single system.

Related concepts

Broader

Reasoning model, Large language model, OpenAI, Foundation model

Narrower

o1, o3, o3-mini, o4-mini

On the edge

Chain of thought, ChatGPT, GPT-5, Extended Thinking, AI-agent

A brief history

OpenAI introduced the first members of the series in September 2024, the o1-preview and o1-mini models. The full o1 was released in December 2024, at which time the o3 was also announced. o3-mini arrived in January 2025, and o3 and o4-mini in April 2025. The name 'o2' was skipped to avoid conflict with the telecommunications brand of the same name. GPT-5, released in August 2025, already merged the reasoning and general models into a single system.

Where things stand

As a standalone product line, the o-series has ended; its role has been taken over by the thinking modes of the GPT-5 family. The solution itself, thinking before answering, has become a standard basic function at major developers by the end of 2026.

Status: 2026 November

Also known as

o-sorozat · o-series · OpenAI o-series · o1 · o3 · o4-mini · reasoning models · reasoning models

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.