o3-mini

o3-mini

o3 Mini is the cost-efficient member of OpenAI’s o-series reasoning models. The launch materials focus on STEM tasks such as science, math, and coding, with lower latency and lower cost than larger reasoning models. It is a strong option when users need reasoning behavior but cannot spend the time or budget required by a full o-series model.

Context Window

200K tokens

Maximum Output

100K tokens

Release Date

Dec 20, 2024

Modalities

o3-mini Pricing

Input Price	Output Price	Cache Read
$1.1/M	$4.4/M	$0.55/M

o3-mini API Capabilities

Reasoning

Supported

Tool calling

Supported

Temperature parameter

Not supported

Attachments

Not supported

Knowledge Base

2024-05-01

Endpoint Protocols

Completions APIMessages APIgemini

o3-mini Model Highlights

o3-mini provides compact text reasoning, long-context processing and structured task execution for focused analytical workloads.

Compact Reasoning

Offers reasoning intelligence in a smaller model for tasks that need analytical depth with lower latency and cost targets.

Long Text Context

Supports a 200,000-token context window for reasoning over lengthy text, code and structured records.

Structured Output Control

Can organize analytical results into consistent schemas, supporting reliable downstream processing of text-based tasks.

o3-mini Use Cases

o3-mini fits text-based reasoning workloads such as policy analysis, text-to-SQL conversion and structured entity extraction.

Policy Analysis

Reads lengthy policies, evaluates a case against stated conditions and explains the resulting decision.

Text-to-SQL Conversion

Translates analytical questions and database schemas into SQL queries, then explains the query logic for review.

Entity Relationship Extraction

Identifies entities and their connections in large text collections and returns records suitable for graphs or databases.

How to Use o3-mini via the TokenHub API

Create API key

import OpenAI from "openai"

const client = new OpenAI({
  apiKey: process.env.TOKENHUB_API_KEY,
  baseURL: "https://us-api.tokenhub.com/v1",
})

const result = await client.chat.completions.create({})
console.log(result.choices[0]?.message?.content)

o3-mini Benchmarks

Index score
Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index summarizes broad model capability across evaluation families.	Artificial Analysis broad capability aggregate	18.4
Artificial Analysis Coding IndexArtificial Analysis Coding Index summarizes model performance on coding and software engineering tasks.	Artificial Analysis software task aggregate	17.3
Knowledge & Reasoning
MMLU-ProMMLU-Pro evaluates broad academic and professional knowledge with more challenging questions than the original MMLU.	Advanced multi-task knowledge	80.2%
GPQAGPQA is a graduate-level science benchmark designed to test expert reasoning.	Advanced science problem solving	77.3%
HLEHLE is a broad, challenging benchmark for expert-level knowledge and reasoning across many domains.	Broad expert-level exam set	12.3%
Coding & Engineering
LiveCodeBenchLiveCodeBench evaluates coding ability on recent programming problems designed to reduce benchmark contamination.	Live coding problems	73.4%
SciCodeSciCode evaluates Python programming for scientific computing, including algorithmic implementation from scientific context.	Scientific coding challenges	39.8%
Terminal-Bench HardTerminal-Bench Hard evaluates whether an AI agent can complete difficult tasks in a terminal environment.	Hard terminal task execution	6.1%
Math
MATH-500MATH-500 evaluates mathematical problem solving across a curated set of challenging questions.	Advanced math problem solving	98.5%
AIMEAIME measures mathematical reasoning on competition-style problems.	Competition math problems	86%
Instruction Following & Agent Tasks
IFBenchIFBench measures how reliably a model follows detailed instructions and constraints.	Prompt constraint adherence	67.1%
AA-LCRAA-LCR measures reasoning quality over extended context windows.	Long-context reasoning	39.3%
τ²-Benchτ²-Bench evaluates conversational agents in realistic workflows with tool and state constraints.	Agent workflow tasks	31.3%

Metrics sourced from Artificial Analysis

o3 Mini FAQ

o3 Mini: capabilities, use cases, limits, and TokenHub guidance.

How should teams view o3 Mini?+

o3 Mini is a OpenAI model for cost-efficient STEM and coding reasoning.

What is o3 Mini best for?+

Best for mathematical reasoning, scientific reasoning and code reasoning, especially when speed and cost efficiency is the priority.

What is o3 Mini's main strength?+

Key strength: strong STEM and coding reasoning in a smaller text-only model.

Is o3 Mini always the best choice?+

It is deprecated or superseded, so it is a poor default for new integrations. Use GPT-5.4 Mini for new integrations.

What is the safest setup?+

Confirm availability; use the current recommended model for new integrations.

Ready to use o3-mini?

Use one API key to access o3-mini and more AI models through TokenHub.