In O3 and O4 – Mini of OpenAI: Unlock new possibilities with multimodal inference and integration tools

On April 16, 2025, OpenAI released an upgraded version of its advanced inference model. These new models, called O3 and O4-Mini, have improved their predecessors O1 and O3-Mini, respectively. The latest models offer enhanced performance, new features and greater accessibility. This article explores the main benefits of O3 and O4-Mini, outlines their main features, and discusses how they affect the future of AI applications. However, before we dig deep into what makes O3 and O4-Mini unique, it is important to understand how Openai’s model evolves over time. Let’s start by briefly overviewing Openai’s journey in developing increasingly powerful language and inference systems.

The evolution of Openai’s large language model

The development of Openai’s large language models began with GPT-2 and GPT-3, which brought Chatgpt into mainstream use because they were able to produce fluent and context-accurate text. These models are widely used in tasks such as abstracts, translations, and question answering. However, their disadvantages become clear as users apply it to more complex solutions. These models often struggle with tasks that require deep reasoning, logical consistency, and multi-step problem solving. To address these challenges, OpenAI introduced GPT-4 and shifted its focus to enhancing the inference capabilities of its models. This transformation led to the development of O1 and O3 Mini. Both models use an approach called a “thought chain” hint, which allows them to generate more logical and accurate responses through step-by-step reasoning. Although O1 is designed to address the needs of advanced problem solving, the O3-Mini is designed to provide similar features in a more efficient and cost-effective way. OpenAI has built the foundation on the ground and now launches O3 and O4-Mini, which further enhances its LLM’s reasoning capabilities. These models are designed to produce more accurate and well-thought-out answers, especially in technical fields such as programming, mathematics, and scientific analysis where logical accuracy is crucial. In the next section, we will look at how O3 and O4-Mini improve their predecessors.

Major advances in O3 and O4-Mini

Enhanced reasoning skills

One of the main improvements to O3 and O4-Mini is that they enhance the reasoning capabilities of complex tasks. Unlike previous models that provide fast responses, the O3 and O4-Mini models require more time to process each prompt. This additional treatment allows them to reason more thoroughly and produce more accurate answers, improving the baseline results. For example, O3 is better than 9% on LiveBench.ai, a benchmark for evaluating the performance of multiple complex tasks such as logic, mathematics, and code. On the SWE bench of reasoning in the test software engineering task, O3 scored 69.1%, even surpassing competitive models such as the Gemini 2.5 Pro, with a score of 63.8%. Meanwhile, O4-Mini scored 68.1% on the same benchmark, providing nearly the same depth of reasoning at much lower costs.

Multimodal Integration: Image Thinking

One of the most innovative features of O3 and O4-Mini is their ability to “think with images.” This means that they can not only process text information, but also integrate visual data directly into their inference process. They can also understand and analyze images, even if they are of low quality (such as handwritten notes, sketches or charts). For example, a user can upload a chart of a complex system, which can analyze it, identify potential problems, and even suggest improvements. This ability bridges the gap between text and visual data, thus enabling more intuitive and comprehensive interaction with AI. Both models can perform operations such as scaling details or rotating images to better understand them. This multimodal reasoning is a significant advancement over predecessors like O1, which is mainly text-based. It opens up new possibilities for applications in areas such as education (visual aids are crucial), and charts and charts are often at the heart of understanding.

Advanced tool usage

O3 and O4-Mini are the first OpenAI models to use all the tools available in Chatgpt at the same time. These tools include:

• Web browsing: Allows the model to get the latest information on time-sensitive queries.
• Python code execution: enables them to perform complex computations or data analysis.
• Image processing and generation: Enhance its ability to use visual data.

By using these tools, O3 and O4-Mini can solve complex multi-step problems more effectively. For example, if a user asks questions that require current data, the model can perform a web search to retrieve the latest information. Likewise, for tasks involving data analysis, it can execute Python code to process data. This integration is an important step in taking a larger autonomous AI proxy that can handle a wider range of tasks without intervention. The introduction of Codex CLI is a lightweight open source encoding proxy used with O3 and O4-Mini, further enhancing its utility for developers.

Meaning and new possibilities

The launch of O3 and O4-Mini has a wide range of industry implications:

• educate: These models can help students and teachers by providing detailed explanations and visual aids, making learning more interactive and effective. For example, students can upload sketches of mathematical problems, and the model can provide step-by-step solutions.
• Research: They can speed up discovery by analyzing complex datasets, generating hypotheses and interpreting visual data such as charts and charts, which is invaluable for fields like physics or biology.
• industry: They can optimize processes, improve decisions and enhance customer interaction by processing text and visual queries such as analyzing product design or troubleshooting technical issues.
• Creativity and Media: Authors can use these models to turn chapter outlines into simple storyboards. Musicians match visual effects to melody. Movie editors will receive pacing suggestions. The architect transforms a hand-drawn floor plan into a detailed 3-D blueprint including structural and sustainability notes.
• Accessibility and inclusion: For blind users, these models describe the images in detail. For deaf users, they convert the chart into visual sequences or subtitle text. Their translation of words and visual effects helps bridge language and cultural gaps.
• Going towards autonomous agents: Since models can browse the network, run code, and process images in a workflow, they form the basis of autonomous agents. The developer describes a feature; the model is written, tested, and deployed. Knowledge workers can delegate data collection, analysis, visualization, and report writing to a single AI assistant.

Limitations and the next step

Despite these advances, O3 and O4-Mini still have an August 2023 knowledge cutoff, which limits their ability to respond to the latest events or technologies unless additional web browsing is provided. Future iterations may address this gap by improving real-time data ingestion.

We can also expect further progress in autonomous AI agents – systems that can plan, reason, act and learn, and constantly learn with minimal supervision. OpenAI integrates tools, inference models and real-time data access signals that we are approaching such systems.

Bottom line

Openai’s new models O3 and O4-Mini provide improvements in inference, multi-modal understanding and tool integration. They are more accurate, general and useful in a variety of tasks, from analyzing complex data and generating code to interpreting images. These advances have the potential to significantly increase productivity and accelerate innovation across industries.