o4-mini
A fast, efficient reasoning model from OpenAI that delivers exceptional performance in math, coding, and visual tasks at high throughput.
OpenAI Fast reasoning for math, code, and vision
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.
What o4-mini supports
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.
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.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 83.2% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 78.4% |
| MATH-500 | Undergraduate and competition-level math problems | 98.9% |
| AIME 2024 | American math olympiad problems | 94.0% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 85.9% |
| HLE | Questions that challenge frontier models across many domains | 17.5% |
| SciCode | Scientific research coding and numerical methods | 46.5% |
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.
What people think about o4-mini
Community reception to o4-mini has been generally positive, with users highlighting its strong performance on math and coding tasks and appreciating the higher rate limits compared to the larger o3 model. A Reddit thread noting that OpenAI doubled rate limits for o4-mini received nearly 300 upvotes, reflecting enthusiasm for its accessibility at scale.
Some community discussion has focused on the model's longer-term availability, with a notable thread reporting that OpenAI plans to retire o4-mini from ChatGPT in February 2026. Users have also drawn comparisons to OpenAI's open-weight releases, with one thread suggesting the 120B open-weight model is roughly equivalent to o4-mini in capability.
OpenAI will retire GPT-4o, GPT-4.1, GPT-4.1 mini, and o4-mini from ChatGPT on February 13
gpt-oss just dropped. OpenAI’s open-weight models are wild. 120B = o4-mini. 20B runs on MacBooks.
Introducing OpenAI o3 and o4-mini
OpenAI has DOUBLED the rate limits for o3 and o4-mini inside ChatGPT
Documentation & links
Parameters & options
Used 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.
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