o3-mini
OpenAI's reasoning model optimized for STEM tasks, coding, and math. Uses chain-of-thought reasoning.
o3-mini is text & chat AI model from OpenAI, priced at €11.00 per 1M input tokens with a 200K tokens context window.
0.7
Examples
See what o3-mini can generate
Prove that the square root of 2 is irrational
Assume for contradiction that sqrt(2) is rational, meaning sqrt(2) = p/q where p and q are integers with no common factors. Squaring both sides gives 2 = p²/q², so p² = 2q². This means p² is even, which implies p is even—write p = 2k. Substituting gives 4k² = 2q², so q² = 2k², meaning q is also even. But if both p and q are even, they share a common factor of 2, contradicting our assumption. Therefore sqrt(2) must be irrational.
Three boxes are labeled 'Apples', 'Oranges', and 'Mixed'. All labels are wrong. You can pick one fruit from one box. How do you determine the correct labels?
Pick a fruit from the box labeled 'Mixed'. Since all labels are wrong, this box contains only one type of fruit—either all apples or all oranges. Say you draw an apple: the 'Mixed' box is actually the Apples box. Now consider the remaining two boxes labeled 'Apples' and 'Oranges'. The box labeled 'Apples' cannot be apples (wrong label) and cannot be mixed (we found that), so it must be Oranges. The box labeled 'Oranges' is therefore the Mixed box. One draw is all you need.
Pricing
API Integration
Use our OpenAI-compatible API to integrate o3-mini into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("o3-mini", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("o3-mini", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("o3-mini", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — OpenAI's o3-mini
OpenAI was founded in 2015 as a non-profit lab by Sam Altman, Elon Musk, Greg Brockman, Ilya Sutskever, Wojciech Zaremba and John Schulman, transitioning to a capped-profit structure in 2019 to raise capital from Microsoft (now over $13B invested). OpenAI's research output includes the GPT series (GPT-1 in 2018 through GPT-4 in 2023), the InstructGPT/RLHF paper (2022), the GPT-4o System Card (2024) and the o-series reasoning papers. The o-series began with o1-preview in September 2024 and o1 in December 2024, introducing test-time compute scaling via long internal chains-of-thought trained with reinforcement learning. o3 and o3-mini followed in January-February 2025, with o3-mini specifically launched on 31 January 2025 as a fast, cost-efficient reasoning model available to free, Plus and Team ChatGPT users. The o-series has been described by OpenAI as the first models to solve previously-unsolved Frontier Math benchmark problems. Sam Altman remains CEO and the company's 2025 valuation exceeded $300 billion.
Visit OpenAI →o3-mini is the small reasoning model in the o-series, released by OpenAI on 31 January 2025. Like its o1 and o3 siblings it is a decoder-only Transformer trained to produce long internal chains-of-thought during inference, then return a concise final answer to the user. Reinforcement learning was the core training technique: the model is rewarded for correct answers on verifiable tasks (math, code, scientific problems) and learns to allocate test-time compute by generating longer or shorter reasoning traces as needed. o3-mini supports three reasoning effort settings ('low', 'medium', 'high') that developers can choose to trade latency for accuracy. The training corpus and base pretraining are not disclosed in detail but build on OpenAI's frontier pretraining stack. o3-mini is the first o-series model to support function calling, Structured Outputs with strict JSON schemas, streaming and developer messages from day one, making it deployable in production agentic stacks. On benchmarks the medium-effort setting matched o1's performance at significantly lower cost; the high-effort setting outperformed o1 on AIME math, GPQA Diamond and Codeforces. Vision input is not supported (use o1 or GPT-4o for multimodal). The model retains OpenAI's deliberative alignment training, in which the safety policy itself is reasoned about explicitly during inference.
- Parameters
- Undisclosed (estimated tens of billions of active parameters)
- Context
- 200K tokens
- Three reasoning effort modes: low, medium, high
- Strong math performance: o3-mini-high matches or beats o1 on AIME and GPQA
- Competitive Codeforces ratings on coding contests
- 200K context window with 100K max output
- Function calling and parallel tool calls
- Structured Outputs with strict JSON schemas
- Streaming and developer-message support
- Lower latency than o1 at equal accuracy
- Available in ChatGPT Free, Plus, Team and API
- Deliberative alignment: reasons about safety policy at inference time
- Best for: math, coding contests, scientific reasoning, multi-step tool agents.
Pretrained on OpenAI's standard multi-trillion-token corpus (composition undisclosed). Post-training relies heavily on reinforcement learning against verifiable rewards on math, code and scientific problem datasets, plus deliberative alignment training where the model is taught to reason about OpenAI's safety policy.
License: Proprietary commercial license via OpenAI API, Azure OpenAI Service and ChatGPT. Commercial use permitted.
Known limitations
- No vision or audio input
- Long internal chains-of-thought add latency at high effort
- Hidden reasoning tokens count against pricing
- Knowledge cutoff October 2023 from base pretraining
- Not always cheaper than GPT-4o for short answer tasks
Frequently asked questions
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