o3 mini
Balanced o-series variant with emphasis on tool use during reasoning.
About o3 mini
Fast, affordable reasoning built for STEM — that is the clearest way to describe o3-mini. Released in January 2025, it brings OpenAI's chain-of-thought reasoning to a price point most projects can sustain, starting at $0.55 per million input tokens. Users consistently praise its speed (24% faster than o1-mini) and its ability to handle math, coding, and scientific problems with accuracy that matches o1 at medium reasoning effort. The adjustable reasoning effort levels — low, medium, or high — let you dial in the latency-versus-accuracy trade-off per request, which makes it practical for everything from quick code review to rigorous math tutoring. That said, users flag real limitations: an Artificial Analysis Intelligence Index of 26 sits below the median for comparable models, and performance on ambiguous or edge-case problems can be inconsistent, so outputs in uncertain territory warrant a second look. A capable, honest workhorse for structured problem-solving where budget discipline matters more than chasing peak benchmark scores.
Best for
- Math and STEM problem-solving, including tutoring, proof verification, and scientific computation
- Code generation and debugging for well-defined algorithmic tasks
- Financial and business analysis where reasoning depth matters but cost ceilings apply
- High-volume API workloads that need chain-of-thought quality at a fraction of full o3 pricing
- Education platforms delivering personalized step-by-step explanations across math and science subjects
Specs & capabilities
How o3 mini stacks up — intelligence, speed, context, and modalities.
Intelligence
Low
Speed
Fast
Context window
200,000 tokens
Max output
100,000 tokens
Knowledge cutoff
Not specified in public documentation
Frequently asked questions
How much does o3-mini cost?
Standard pricing is $0.55 per million input tokens and $2.20 per million output tokens. The high-reasoning variant costs $1.10 input / $4.40 output per million tokens. Cached reads drop to $0.11 per million on standard.
What is the context window?
200,000 tokens input, with up to 100,000 tokens of output.
What are reasoning effort levels and why do they matter?
You can set reasoning effort to low, medium, or high per request. Higher effort improves accuracy on hard problems at the cost of more latency and tokens used; low effort is faster and cheaper for simpler tasks.
How does o3-mini compare to the full o3 model?
The full o3 significantly outperforms o3-mini on hard benchmarks — for example, 96.7% versus 87.3% on AIME 2024. o3-mini is the cost-efficient option; o3 is the ceiling for accuracy-critical work.
Where does o3-mini fall short?
Its Artificial Analysis Intelligence Index (26) is below the median of 36 for comparable models, and it can give inconsistent answers on ambiguous or self-referential edge cases. Outputs in uncertain domains should be verified.
Has o3-mini been superseded?
It has been followed by o4-mini (April 2025) and the full o3, but o3-mini remains in production. If you need the latest generation of compact reasoning from OpenAI, o4-mini is the current recommendation.