On April 16, 2025, Openai released an upgraded version of its advanced inference model. These new models, named O3 and O4-MINI, offer improvements over their predecessors, O1 and O3-MINI, respectively. The latest models offer enhanced performance, new features and greater accessibility. In this article, we will explore the main benefits of O3 and O4-MINI, outline the main features, and explain how they will affect the future of AI applications. However, before diving into something that clarifies the O3 and O4-Mini, it is important to understand how Openai’s models have evolved over time. Let’s start with a brief overview of Openai’s journey in developing increasingly powerful languages and inference systems.
Evolution of Openai’s large-scale language model
The development of Openai’s large-scale language model began with GPT-2 and GPT-3, and ChatGpt became the mainstream use due to its ability to create contextually accurate text in fluent. These models were widely adopted for tasks such as summarizing, translation, and answering questions. However, when users apply them to more complex scenarios, their drawbacks became apparent. These models often struggled with tasks that required deep inference, logical consistency, and multi-step problem solving. To address these challenges, Openai introduced GPT-4 and shifted its focus to strengthening the model’s inference capabilities. This shift led to the development of O1 and O3-MINI. Both models used a method called the Chain of Obsert Prompt, which allowed us to generate a more logical and accurate response by inference in stages. While the O1 is designed for advanced problem-solving needs, the O3-MINI is built to provide similar features in a more efficient and cost-effective way. Based on this foundation, Openai currently introduces O3 and O4-Mini, further enhancing the inference capabilities of LLM. These models are designed to generate more accurate and well thought out answers, particularly in technical fields such as programming, mathematics, and scientific analysis. In the next section, we will explore how O3 and O4-MINI improve their predecessors.
Important advances in O3 and O4-Mini
Enhanced inference features
One important improvement to O3 and O4-mini is to enhance the inference ability of complex tasks. Unlike previous models that provided rapid responses, the O3 and O4-MINI models take time to process each prompt. This additional process allows for more thorough inference and generation of more accurate answers, improving benchmark results. For example, O3 outperforms O1 by 9% on LiveBench.ai. This is a benchmark that evaluates performance across multiple complex tasks such as logic, mathematics, and code. On the SWE bench, which tests inference for software engineering tasks, O3 is better than even competitive models like the Gemini 2.5 Pro, which scored 69.1% and scored 63.8%. Meanwhile, O4-Mini scored 68.1% on the same benchmark, offering roughly the same depth of reasoning at a much lower cost.
Multimodal integration: Thinking with images
One of the most innovative features of the O3 and O4-Mini is its ability to “think with images.” This means that not only processing textual information, but visual data can be integrated directly into the inference process. You can understand and analyze images even when they are of low quality, such as handwritten notes, sketches, and diagrams. For example, users can upload diagrams of complex systems, and models can analyze them, identify potential problems, and suggest improvements. This feature creates a gap between text and visual data, allowing for a more intuitive and comprehensive interaction with AI. Both models can be better understood by performing actions such as zooming in on the details and rotating the image. This multimodal reasoning is a greater advance than its predecessors, primarily text-based O1. This opens up new possibilities for applications in fields such as education where visual aids are important, and research where diagrams and charts are often the center of understanding.
Using advanced tools
O3 and O4-MINI are the first OpenAI models to use all the tools available simultaneously in CHATGPT. These tools include:
- Web browsing: Allows models to get the latest updates on time-sensitive queries.
- Running Python code: Allows you to perform complex calculations or data analysis.
- Image Processing and Generation: Improved ability to manipulate visual data.
By using these tools, O3 and O4-MINI can solve complex, multi-step problems more effectively. For example, if a user asks a question that requires current data, the model can perform a web search to get the latest information. Similarly, for tasks that involve data analysis, you can run Python code to process the data. This integration is a critical step towards a more autonomous AI agent that can handle a wide range of tasks without human intervention. The introduction of the Codex CLI, a lightweight open source coding agent that runs on O3 and O4-MINI, further improves developer utilities.
Meaning and new possibilities
The releases of O3 and O4-MINI have a widespread impact across the industry.
- education: These models can assist students and teachers by providing detailed explanations and visual aids, making learning more interactive and effective. For example, students can upload sketches of mathematics problems, and models can provide step-by-step solutions.
- the study: Analyze complex datasets, generate hypotheses, and interpret visual data such as charts and diagrams that are invaluable in fields such as physics and biology, to accelerate discovery.
- industry: Enhance customer interaction by optimizing processes, improving decision-making, and handling both textual and visual queries, such as product design analysis and troubleshooting technical issues.
- Creativity and Media: These models allow authors to turn chapter outlines into simple storyboards. The musician matches the visual to the melody. Film editors will receive pacing suggestions. The architect converts handheld floor plans into a detailed 3‑dl blueprint with structural and sustainability notes.
- Accessibility and Inclusion: For blind users, the model will explain the image in detail. For deaf people, convert the diagram into visual sequence or caption text. Translations of both words and visuals help bridge the linguistic and cultural gaps.
- Towards an autonomous agent: Models can browse the web, execute code, and process images in one workflow, forming the basis for an autonomous agent. Developers explain the features. The model writes, tests, and deploys code. Knowledge workers can collect, analyze, visualize data, and report writing to one AI assistant.
What are the limits and what are the next?
Despite these advances, O3 and O4-MINI have an August 2023 knowledge cutoff. This limits your ability to respond to the latest events or technologies, unless supplemented with web browsing. Future iterations may address this gap by improving real-time data intake.
We can also expect further advances in autonomous AI agents, systems that can learn continuously and continuously with minimal supervision. OpenAI integrates tools, inference models, and real-time data access signals to approach such systems.
Conclusion
Openai’s new models, O3 and O4-Mini, offer improved inference, multimodal understanding and tool integration. They are more accurate, versatile and useful across a wide range of tasks, from analyzing complex data and generating code to interpreting images. These advancements can dramatically increase productivity and accelerate innovation in a variety of industries.
