OpenAI launches o1, models that reason before answering

OpenAI introduced o1-preview and o1-mini on Thursday, a new family of models that uses more compute to reason before generating a response. The development matters because it shifts part of AI's improvement away from model size and toward the time spent solving each problem.

The company had previewed this line of research under the codename Strawberry. Its first commercial result does not replace GPT-4o: it is designed for tasks where a fast answer matters less than a well-formed solution, such as a complex math problem, a scientific query, or debugging code.

Taking more time before answering

Conventional language models generate text by predicting, one word at a time, what the most likely continuation should be. o1 adds a longer internal reasoning phase. During training, OpenAI used reinforcement learning, a technique that rewards strategies leading to a correct answer and penalizes mistakes.

As a result, the model can try different approaches, detect when one does not work, and correct itself before delivering its final answer. That does not mean it understands a problem like a person or makes every response correct. It does change how it handles tasks that require chaining together several logical steps.

OpenAI does not show users the model's full chain of thought. Instead, the interface may display a brief indication of the process while the model works and then deliver the answer. That is an important precaution: internal reasoning should not be mistaken for a verifiable demonstration. In math, research, or professional work, the answer still needs human review and comparison against independent sources or calculations.

Better results on math and coding tests

The company accompanied the launch with results that capture o1's goal. On a qualifying exam for the International Mathematical Olympiad, GPT-4o correctly solved 13% of the questions, while o1 reached 83%, according to OpenAI. In Codeforces programming competitions, the model ranked in the 89th percentile.

These tests do not measure intelligence as a whole or guarantee the same performance in real-world work. But they highlight a known weakness of generative assistants: they can confidently explain an incorrect solution when a problem requires maintaining logical consistency across many steps. o1's advance lies precisely in reducing that failure in structured domains.

o1-preview is the new series' more capable general-purpose version. It is available today to ChatGPT Plus and Team subscribers, with an initial limit of 30 messages per week. It is also coming to the API for developers who meet OpenAI's access requirements.

The second version, o1-mini, focuses on programming and STEM subjects—the acronym for science, technology, engineering, and mathematics. OpenAI offers it as a faster alternative that is 80% cheaper than o1-preview: it costs $3 per million input tokens and $12 per million output tokens, compared with $15 and $60, respectively, for o1-preview. A token is a unit of text that models use to process and generate language.

More capability, but fewer features than GPT-4o

The launch has clear limitations. In ChatGPT, o1-preview and o1-mini still lack tools already common in GPT-4o, such as web browsing, file uploads, and image analysis. They are also slower: that pause is not an accidental flaw but the cost of devoting more resources to reasoning.

This means choosing a model based on the task. For drafting, summarizing a document, or keeping a conversation moving quickly, GPT-4o will remain the more practical option in many cases. For solving a difficult technical exercise, reviewing a programming strategy, or exploring a scientific hypothesis, o1 may justify the wait and the higher price.

OpenAI has also evaluated this family under its framework for advanced-risk preparedness. The company classifies o1-preview as presenting medium risk in capabilities related to chemical, biological, radiological, and nuclear threats, and applies specific safeguards to those queries. It is a sign that more capable reasoning does not just improve useful answers: it also requires raising the bar for safety evaluation before access is expanded.

The first version of o1 is therefore less a universal assistant than a bet on models that know when to stop and work through a problem. Its evolution will depend on whether OpenAI can extend that reasoning to more tools and tasks without turning every everyday query into a costly wait.