o4-mini

Basic Information

OpenAI o4-mini is a lightweight inference model from the o series released by OpenAI on April 16, 2025. As the successor to o3-mini, it is designed to deliver fast and cost-effective inference. This model is now available to users of ChatGPT Plus, Pro, and Team editions (replacing the previous o3-mini). Users of ChatGPT Enterprise and Edu editions will gain access one week later. It is also available to developers via the Chat Completion API and the Reply API (some developers will need to verify their organizations). Its use cases cover high-volume, high-throughput requirements, delivering outstanding performance in areas such as mathematics, coding, and vision tasks while maintaining small size and low cost.

Core Features

o4-mini features multi-modal processing capabilities, enabling it to handle both text and images simultaneously. It can perform deep reasoning on visual inputs, accurately interpreting even blurry or inverted images. It also supports image operations such as rotation and scaling. Moreover, it can autonomously invoke all tools within ChatGPT, including web search, Python for analyzing data files, and image generation. It can determine the appropriate timing and method for using these tools, typically providing detailed answers within one minute. It excels at following instructions, producing more useful and verifiable responses, and leveraging memory from previous conversations to enhance the naturalness and personalization of interactions.

Technical Highlights

In terms of performance, o4-mini achieved accuracy rates of 93.4% and 92.7% respectively in the AIME 2024 and 2025 math competitions without any tools. After invoking the Python interpreter, its AIME 2025 pass@1 score reached 99.5%, and its consensus@8 score hit 100%. In Codeforces competitions, its ELO rating was 2719, surpassing both o1 (1891) and o3-mini (2073). In terms of efficiency, o4-mini offers significantly improved cost-effectiveness compared to o3-mini, making it cheaper in most scenarios and featuring stricter usage limits that better suit high-throughput demands. Regarding security, thanks to re-engineered secure training data, it can reject inappropriate requests such as biothreats and malware generation. Combined with an LLM monitoring program for inference, it achieves a detection rate of approximately 99% in manual red-team testing for biological risk scenarios.

Market Impact

With its balanced strengths in “performance, speed, and cost,” o4-mini is driving the widespread adoption of small-scale AI inference applications, suitable for scenarios such as educational tutoring, data analysis, and software development. Its cost-effectiveness and high throughput characteristics help reduce AI application costs for businesses and developers, accelerating the deployment of AI solutions in high-traffic environments. As a benchmark for the lightweight models in the o series, it provides guidance for the development of small-scale inference models across the industry and promotes the wider application of AI technology in resource-constrained environments.

This model has been superseded by the GPT-5 mini.