o1-mini
By OpenAI
o1-mini is a smaller, faster, and lower-cost member of OpenAI's o1 reasoning model family, designed to perform extended internal chain-of-thought reasoning before answering, with particular strength on coding and STEM problems while being…
Definition
o1-mini is a smaller, faster, and lower-cost member of OpenAI's o1 reasoning model family, designed to perform extended internal chain-of-thought reasoning before answering, with particular strength on coding and STEM problems while being more affordable than the full o1 model. It is trained with reinforcement learning to spend variable inference-time computation on harder problems, does not expose its internal reasoning steps to users, and is priced and positioned as a more affordable alternative to the full o1 model for coding and technical use cases.
Overview
o1-mini belongs to OpenAI's o-series of models, which are trained using reinforcement learning to generate an internal sequence of reasoning steps before producing a final answer, a technique intended to improve performance on tasks that require careful, multi-step logical deduction such as mathematics, coding, and scientific problem solving. Unlike standard chat models that generate a response in a single forward pass with minimal deliberation, o1-mini effectively "thinks" for a variable amount of extra computation depending on the difficulty of the prompt. During training, the model is rewarded not just for the correctness of its final answer but for the quality of the intermediate reasoning path that leads there, using reinforcement learning to shape a policy that allocates more internal steps to harder inputs and fewer to simple ones. Compared to the full-size o1 model, o1-mini is smaller and cheaper to run, trading some general knowledge breadth for lower latency and cost, and it was positioned by OpenAI as a strong option specifically for coding and technical reasoning tasks rather than as a general-purpose knowledge assistant. It does not have the same depth of world knowledge as larger general-purpose models like GPT-4, since much of its training emphasis is on reasoning capability. This variable internal computation is the mechanical difference from a standard chat model: rather than producing tokens in a single uninterrupted pass tied directly to output length, the model can spend a portion of its inference budget on steps that never appear in the visible reply, only surfacing a condensed final answer once that process concludes. Because the extended reasoning happens internally, users typically do not see the model's full chain-of-thought in raw form; instead they receive a final, distilled answer, which OpenAI designed partly for safety reasons, to avoid exposing potentially unfiltered intermediate reasoning that could be misused or misinterpreted. Relative to the full o1 model, o1-mini's smaller size and narrower training focus on coding and STEM problem types make it noticeably less capable on tasks requiring broad general or cultural knowledge, positioning it closer to a specialist tool than a like-for-like cheaper substitute for a general-purpose flagship model. o1-mini is accessed through ChatGPT as a selectable model or through the OpenAI API, and its pricing and rate limits are typically lower than the full o1 model, making it a more practical choice for applications with high query volume that still benefit from reasoning-oriented outputs, such as automated code review or math tutoring tools. Development teams have used it to draft first-pass code reviews, to check step-by-step math or logic derivations before a human signs off, and to prototype agent pipelines where a verification step benefits from careful, if slower, deliberation rather than an instant guess. As with other reasoning models, response times can be noticeably longer than standard chat models for harder queries, since the model is spending additional inference-time computation, and it inherits the general limitation that reasoning quality, while improved, is not infallible for very complex or ambiguous problems. Because response latency scales with problem difficulty, applications with strict real-time constraints may find it a poor fit for every request, and since it deliberately does not expose intermediate reasoning, users needing an auditable trail of how an answer was reached must rely on the model's own summarized explanation rather than a raw transcript.
Key Concepts
- Internal chain-of-thought reasoning before producing an answer
- Smaller and cheaper than the full o1 reasoning model
- Particular strength on coding and STEM problem solving
- Variable inference-time compute depending on problem difficulty
- Hidden internal reasoning with only a distilled final answer shown
- Lower API pricing and rate limits than the full-size model