OpenAI o4-mini

OpenAI small inference model optimized for fast, cost-effective inference

large language model

https://openai.com/o4-mini

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In-depth Report

OpenAI o4-mini is a small inference model released by OpenAI on April 17, 2025, optimized for fast, cost-effective inference. o4-mini performs well on math, programming, and vision tasks and is a high-scoring model on AIME 2024 and 2025 math competition questions. As an important function integrated for the first time in a small model, o4-mini can not only understand images, but also perform in-depth understanding and reasoning. At the same inference cost, o4-mini performs significantly better than o3-mini. In terms of pricing, o4-mini has an input fee of US$1.10 per million tokens and an output fee of US$4.40 per million tokens, making it the most cost-effective option in the o series.
OpenAI is an American artificial intelligence research organization founded in 2015 by Elon Musk and others and headquartered in San Francisco. The company's core mission is general artificial intelligence (AGI), and it has successively released the GPT series of large language models and the o series of inference models. Launched simultaneously with o3 on April 17, 2025, o4-mini signals OpenAI’s commitment to delivering more cost-effective solutions without sacrificing significant performance.
The core functions of the o4-mini model are mainly reflected in the following aspects. The first is image reasoning capability. As an important function integrated for the first time in a small model, o4-mini can not only understand images, but also perform in-depth understanding and reasoning. It can understand complex diagrams, analyze and interpret whiteboards, schematics, flow charts, etc. It has sketch recognition capabilities, can recognize hand-drawn sketches and provide relevant suggestions, and has visual logical reasoning capabilities to perform logical reasoning and answer questions based on image content. The second is the balance between performance and volume, using the latest model compression technology to achieve the goal of small volume and large capacity. The reasoning capability is close to or even surpasses the o1 full-size model, the response speed is more than 35% faster than o1, and the Token processing efficiency is significantly improved. When it comes to benchmarks, the o4-mini performs well. In terms of code generation capabilities, HumanEval scored 87.2%, MBPP scored 80.3%, and CodeContests scored 62.1%. In terms of reasoning ability, the GSM8K math test score was 84.5%, the MMLU multi-subject score was 81.2%, and the ARC general knowledge score was 86.8%. In terms of image understanding ability, the chart interpretation score was 82.5%, the hand-drawing recognition score was 79.3%, and the visual reasoning score was 83.7%. In terms of multi-modal capabilities, o4-mini can process text, images, and audio at the same time, and can act as an Agent to automatically call tools such as network search, image generation, and code analysis to complete complex tasks.
The o4-mini model is priced very competitively and is the most cost-effective option in the o series: o4-mini has an input fee of $1.10 per million tokens and an output fee of $4.40 per million tokens. It is about 65% cheaper than o1 and has a more price advantage than o3-mini. The processing length of approximately 750,000 tokens exceeds that of the Lord of the Rings series. In terms of availability, ChatGPT Plus, Pro, Team users can use it out of the box. Enterprise and education users will gain access a week later. Free users can use o4-mini in Think mode with unchanged rate limits. Developers can access it through the Chat Completions API and Responses API.
Judging from the user feedback searched, the release of o4-mini has been widely recognized. In terms of positive reviews, users generally believe that o4-mini is the most cost-effective option, with fast speed and good results. Most users found the o4-mini to perform better than expected on programming tasks. The characteristics of small size and large capabilities have been recognized by developers. Negative feedback mainly focused on feature limitations. As a small model, the o4-mini does not have as high a ceiling as the o3. Rate limits for free users have upset some users. Not as good as o3 on complex reasoning tasks. In terms of usage scenarios, o4-mini is particularly suitable for light tasks that require fast response, and is a cost-effective choice. For complex tasks that require deep reasoning, o3 is recommended.
From an industry perspective, the release of o4-mini is seen as an important step in the democratization of AI. The industry generally agrees that o4-mini provides powerful inference capabilities while keeping costs low. The first integration of image reasoning capabilities in a small model is regarded as a technological breakthrough.
At the technical level, the reasoning depth of small models is limited, and their reliability in key application scenarios needs to be evaluated. There is still room for improvement in multi-modal fusion. On a commercial level, ongoing pricing strategies may impact OpenAI’s revenue. User confusion over the choice of different versions of the model is also a potential problem.
o4-mini is particularly suitable for the following groups of people: cost-sensitive individual developers, application scenarios that require quick response, image understanding needs in the field of educational technology, and small and medium-sized enterprise-level applications. For complex tasks that require deep reasoning, o3 is recommended. For those on a budget who need maximum performance, o3 is the better choice.
OpenAI o4-mini is currently the most cost-effective small inference model in the o series and performs well in mathematics, programming and vision tasks. Integrating image reasoning capabilities in a small model for the first time is a typical example of high power in a small size. Recommended for user groups who require quick response and high cost performance.

User Reviews

Joseph_Morales_Plus225

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o4-mini is very cost-effective, fast and effective, and is the first choice for daily use.

STrus

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The response speed is more than 35% faster than o1, and the actual test is indeed significantly faster.

n1ql96

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The king of cost performance, the price is only one-tenth of o3, but the effect is almost the same.

Charles.GonzalesIII8

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AIME 2025 math test 93.4% accuracy, is this still a small model?

云烟320

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A small model integrates image reasoning for the first time, making significant progress.

TAmor

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CodeContests 62.1% is higher than o1, and my programming ability amazes me.

Bobby_GrayIII

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The accuracy of chart interpretation is 82.5%, which is very useful for data analysis.

NathanTaylor_Max6

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It is much better than o3-mini, and the price is about the same. It is more cost-effective to choose o4-mini.

LIgon

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The free version also works, but there are rate limits.

GErog

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A typical representative of small size but big power, I love it.

VioletBauer

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The ability to automatically call tools as an Agent is very practical.

BobbyCox_2023

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The sketch recognition function is too friendly to students.

Charles.HughesZ49

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GSM8K 84.5% No problem, enough math skills.

SaraHughes_7

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ARC General Knowledge 86.8%, good common sense reasoning ability.

DClark_2020998

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The cost of enterprise API access is much lower.

兔兔_4

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65% cheaper than o1, the price is amazing.

TerryPerryK

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Multi-modal capabilities have finally been transferred to small models this time.

梅花956

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The response speed is fast and suitable for real-time conversation scenarios.

Daniel_Morales_Pro50

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OpenAI’s most cost-effective inference model deserves its title.

OpenAI o4-mini

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