Chain-of-Thought Reasoning
The model thinks through problems step by step before responding, producing more reliable answers for complex math, science, and logic tasks. It achieved 99.5% pass@1 on AIME 2025 when paired with a Python interpreter.
OpenAI
o4-mini
o4-mini is a compact text generation model developed by OpenAI and released in April 2025 alongside the larger o3 model. It uses a chain-of-thought reasoning approach, thinking through problems step by step before producing a response, which makes it well-suited for structured problem-solving in math, coding, science, and visual tasks. The model supports a 200,000-token context window, allowing it to process and analyze lengthy documents in a single session. What distinguishes o4-mini from earlier reasoning models is its native ability to incorporate images directly into its reasoning process — not just interpreting them, but actively using them as part of its chain of thought, including handling low-quality or rotated images. It is also trained for agentic tool use, meaning it can decide when to invoke tools like web search, Python execution, or file analysis to complete multi-step tasks. Its design prioritizes high throughput, making it a practical choice for developers and applications that require large volumes of reasoning-intensive requests.
Apr 16, 2025
200,000 context
100,000 tokens output
Chain-of-Thought Reasoning
Visual Reasoning
Agentic Tool Use
Code Generation
Large Context Window
Math & Science Problem Solving
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
OpenAI
Model ID
The routed model identifier exposed by upstream providers.
openai/o4-mini
Input Context Window
The number of tokens supported by the input context window.
200,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
100,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Apr 16, 2025
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
April 2025
API Providers
The providers that offer this model. This is not an exhaustive list.
OpenAI
Modalities
Types of data this model can process.
Text
Image
File
What is o4-mini
A fuller summary of positioning, capabilities, and source-specific details for o4-mini.
o4-mini is a compact text generation model developed by OpenAI and released in April 2025 alongside the larger o3 model. It uses a chain-of-thought reasoning approach, thinking through problems step by step before producing a response, which makes it well-suited for structured problem-solving in math, coding, science, and visual tasks. The model supports a 200,000-token context window, allowing it to process and analyze lengthy documents in a single session.
What distinguishes o4-mini from earlier reasoning models is its native ability to incorporate images directly into its reasoning process — not just interpreting them, but actively using them as part of its chain of thought, including handling low-quality or rotated images. It is also trained for agentic tool use, meaning it can decide when to invoke tools like web search, Python execution, or file analysis to complete multi-step tasks. Its design prioritizes high throughput, making it a practical choice for developers and applications that require large volumes of reasoning-intensive requests.
Capabilities
What o4-mini supports
Visual Reasoning
o4-mini can integrate images directly into its chain of thought, actively reasoning with visual inputs rather than just describing them. It handles low-quality, blurry, or rotated images as part of its reasoning process.
Agentic Tool Use
The model is trained to decide when and how to invoke external tools including web search, Python code execution, file analysis, and image generation. It can chain multiple tools together to complete multi-step tasks.
Code Generation
o4-mini generates, analyzes, and debugs code across common programming languages, and can execute Python as part of its reasoning workflow. It is designed for high-throughput use in software development contexts.
Large Context Window
Supports up to 200,000 tokens per request, equivalent to roughly 300 pages of text, enabling analysis of long documents, codebases, or multi-turn conversations in a single call.
Math & Science Problem Solving
Designed with particular strength in quantitative reasoning, the model ranked at the top of AIME 2024 and 2025 math competition benchmarks. It applies structured reasoning to multi-step scientific and mathematical problems.
Pricing for o4-mini
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$1.10
Per million tokens
Output tokens
$4.40
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
Web search
$10000.00
Cache read
$0.28
maxTemperature
1
maxResponseSize
100,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
OpenAI
Provider Endpoints
Endpoint-level provider data currently available for this model.
OpenAI
Configuration & Parameters
The configurable options currently documented for this model.
Reasoning Effort
SelectUsed to give the model guidance on how many reasoning tokens it should generate before creating a response to the prompt. Low will favor speed and economical token usage, and high will favor more complete reasoning at the cost of more tokens generated and slower responses. The default value is medium, which is a balance between speed and reasoning accuracy.
Supported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MATH-500
Undergraduate and competition-level math problems
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Announcement Blog Post
Announcements
→
API Documentation
Documentation
→
Independent Performance Analysis
Other
→
OpenAI Platform Models Overview
Documentation
→
OpenAI Playground
Playground
→
OpenAI System Card: o3 and o4-mini
Research
→
Official Website
→
Usage Policies
→
Enterprise privacy at OpenAI
→
OpenAI Status Page
→
OpenRouter Model Page
OpenRouter
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Community discussion
What people think about o4-mini
o4-mini discussions are most active in r/singularity, r/OpenAI, r/udemyfreebies. Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions.
The strongest match in this snapshot has 4107 upvotes and 368 comments.
What's your opinion as Google models are getting good how will it compare and also about deepseek R2 ? Idk I'm not sure just give us directly gpt 5
FAQ
Common questions about o4-mini
What is the context window size for o4-mini?
o4-mini supports a context window of 200,000 tokens, which is approximately 300 pages of text. This allows it to process long documents, extended conversations, or large codebases in a single request.
When was o4-mini released and what is its training data cutoff?
o4-mini was released in April 2025, alongside OpenAI's o3 model. The training date listed in the metadata is April 2025; for precise knowledge cutoff details, refer to OpenAI's official API documentation.
How does o4-mini handle images?
o4-mini can accept images as inputs and incorporate them directly into its chain-of-thought reasoning process. It can work with low-quality, blurry, or rotated images and manipulate them — such as zooming or rotating — as part of solving a problem.
What tools can o4-mini use in agentic workflows?
o4-mini is trained to use tools including web search, Python code execution, file analysis, and image generation. It decides autonomously when to invoke these tools and can combine them across multiple steps to complete complex tasks.
How does o4-mini's availability compare to the larger o3 model?
o4-mini is designed for high-throughput use and offers significantly higher usage rate limits than the larger o3 model, making it more suitable for applications that require processing large volumes of requests.
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