o4-mini

o4-mini

o4 Mini is a smaller reasoning model optimized for fast, cost-efficient performance across math, coding, and visual tasks. OpenAI’s release notes emphasize that it can use tools while reasoning, making it useful beyond plain text answers. It should be described as a compact reasoning-and-tool model for production workloads.

Context Window

200K tokens

Maximum Output

100K tokens

Release Date

Apr 16, 2025

Modalities

o4-mini Pricing

Input Price	Output Price	Cache Read
$0.15/M	$0.6/M	$0.075/M

o4-mini API Capabilities

Reasoning

Supported

Tool calling

Supported

Temperature parameter

Not supported

Attachments

Supported

Knowledge Base

2024-05-01

Endpoint Protocols

Completions APIMessages APIgemini

o4-mini Model Highlights

o4-mini combines fast reasoning with efficient coding and visual analysis for focused technical workloads.

Fast Reasoning

Is optimized to reason effectively in a smaller model while keeping response time and processing cost practical.

Efficient Coding

Applies reasoning to code generation, diagnosis and modification with an emphasis on efficient technical execution.

Visual Task Reasoning

Reasons over text and image inputs to interpret diagrams, screenshots and visual problem statements.

o4-mini Use Cases

o4-mini fits code troubleshooting, visual technical question answering and high-volume reasoning tasks where efficiency matters.

Code Troubleshooting

Analyzes code and error details, identifies likely causes and proposes focused corrections for developer review.

Diagram Question Answering

Interprets diagrams and accompanying text to explain components, relationships and the solution to a technical question.

Batch Reasoning Workloads

Processes large queues of technical or analytical questions that need reasoning beyond simple classification.

How to Use o4-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)

o4-mini Benchmarks

o4-mini (high)

Index score
Artificial Analysis Intelligence IndexArtificial Analysis Intelligence Index summarizes broad model capability across evaluation families.	Artificial Analysis broad capability aggregate	25.6
Artificial Analysis Coding IndexArtificial Analysis Coding Index summarizes model performance on coding and software engineering tasks.	Artificial Analysis software task aggregate	25.6
Artificial Analysis Math IndexArtificial Analysis Math Index summarizes mathematical reasoning and competition-style problem solving.	Artificial Analysis math reasoning aggregate	90.7
Knowledge & Reasoning
MMLU-ProMMLU-Pro evaluates broad academic and professional knowledge with more challenging questions than the original MMLU.	Advanced multi-task knowledge	83.2%
GPQAGPQA is a graduate-level science benchmark designed to test expert reasoning.	Advanced science problem solving	78.4%
HLEHLE is a broad, challenging benchmark for expert-level knowledge and reasoning across many domains.	Broad expert-level exam set	17.5%
Coding & Engineering
LiveCodeBenchLiveCodeBench evaluates coding ability on recent programming problems designed to reduce benchmark contamination.	Live coding problems	85.9%
SciCodeSciCode evaluates Python programming for scientific computing, including algorithmic implementation from scientific context.	Scientific coding challenges	46.5%
Terminal-Bench HardTerminal-Bench Hard evaluates whether an AI agent can complete difficult tasks in a terminal environment.	Hard terminal task execution	15.2%
Math
MATH-500MATH-500 evaluates mathematical problem solving across a curated set of challenging questions.	Advanced math problem solving	98.9%
AIMEAIME measures mathematical reasoning on competition-style problems.	Competition math problems	94%
AIME 2025AIME 2025 measures performance on challenging competition math problems.	Competition math problems	90.7%
Instruction Following & Agent Tasks
IFBenchIFBench measures how reliably a model follows detailed instructions and constraints.	Prompt constraint adherence	68.7%
AA-LCRAA-LCR measures reasoning quality over extended context windows.	Long-context reasoning	55.0%
τ²-Benchτ²-Bench evaluates conversational agents in realistic workflows with tool and state constraints.	Agent workflow tasks	55.6%

Metrics sourced from Artificial Analysis

o4 Mini FAQ

o4 Mini: capabilities, use cases, limits, and TokenHub guidance.

What problem does o4 Mini address?+

o4 Mini is a OpenAI model for fast, cost-efficient math, coding, and visual reasoning.

When should I choose o4 Mini?+

Best for mathematical reasoning, complex coding and visual reasoning, especially when speed and cost efficiency is the priority.

What does o4 Mini do well?+

Key strength: fast, economical reasoning with particularly strong coding and visual work.

What does o4 Mini sacrifice?+

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

How do I use o4 Mini in TokenHub?+

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

Ready to use o4-mini?

Use one API key to access o4-mini and more AI models through TokenHub.

Create API key

o4-mini

o4-mini Pricing

o4-mini API Capabilities

o4-mini Model Highlights

Fast Reasoning

Efficient Coding

Visual Task Reasoning

o4-mini Use Cases

Code Troubleshooting

Diagram Question Answering

Batch Reasoning Workloads

How to Use o4-mini via the TokenHub API

o4-mini Benchmarks

Models Similar to o4-mini

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o4 Mini FAQ

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