OpenAI O3 and O4-mini Models Explained

Benchmark performance: O3 and O4-mini have set new records across numerous benchmarks, demonstrating exceptional reasoning and problem-solving capabilities. On the prestigious AIME (American Invitational Mathematics Examination) benchmark, O4-mini even outperforms the larger O3. Without tool use, O4-mini achieves 93.4% accuracy on AIME 2024, exceeding O3's 91.6% and far surpassing its predecessor O1's 74.3%. Similar trends appear on AIME 2025, with O4-mini reaching approximately 92.7%, continuing to lead O3's 88.9%. With tool use enabled, both models reach near-perfect scores: when given access to Python code execution, O4-mini achieves 99.5% accuracy on AIME 2025 problems, while O3 improves to 98.4%. This demonstrates that the new models effectively leverage computational tools to boost precision.

On coding and scientific problems, O3 demonstrates clear advantages as the larger model. According to OpenAI's official documentation, O3 achieves state-of-the-art results on programming challenges like Codeforces, software engineering benchmarks like SWE-bench, and multidisciplinary evaluations like MMMU, all without additional manual tool assistance. In an assessment of open-ended software development tasks, O3 autonomously tackled high-difficulty coding assignments, demonstrating strong engineering problem-solving. On complex multilingual code editing tests, O3's consistent high accuracy defeated all competitors, establishing it as the benchmark for AI programming capability. External experts found that O3 reduced critical errors by 20% compared to its predecessor O1 on difficult real-world tasks, particularly excelling in programming, business consulting, and creative thinking. O4-mini, while smaller, likewise performs well across mathematics, coding, and data science, comprehensively outperforming its predecessor O3-mini and showing improvements on non-STEM tasks as well.

Response quality and limitations: Thanks to deeper reasoning chains, the new models deliver more accurate and comprehensive answers on complex problems. On exam-style and logical reasoning tasks, O3 often produces more rigorous derivations and conclusions than its predecessors; early testers praised its ability to propose new hypotheses and self-review on scientific and engineering questions. However, hallucination rates—the tendency to generate false information—have not decreased in this generation but have actually increased. OpenAI's system card reveals that on the PersonQA test specifically designed to elicit hallucinations, O3 exhibits a hallucination rate of approximately 33%, while the smaller O4-mini reaches 48%, significantly higher than the previous-generation O1 (16%) and O3-mini (14.8%). This means O3 and O4-mini can sometimes express incorrect content with greater confidence; reducing hallucinations in high-order reasoning models remains an ongoing challenge.

Technical Features

• Multimodal and visual reasoning: O3 and O4-mini introduce genuinely capable visual reasoning. The models can analyze images as part of their reasoning chain, moving beyond simple image recognition to "think with images" as humans do. For example, O3 can analyze a geographic photograph and infer its location, indicating that the model has developed preliminary visual understanding and reasoning applicable to complex image-based Q&A, chart analysis, and diagnostic scenarios. The models also support audio input processing, making them general-purpose multimodal AI systems.

• Autonomous tool calling: Both models include built-in Agent-style tool use capabilities, automatically invoking external tools such as browser search, code execution, and image generation. They can autonomously decide which tools to call and when, integrating tool results into their final responses. This autonomy stems from specialized reinforcement learning strategies: OpenAI trained the models to understand tool functionality and calling conventions, enabling them to compose multiple tools within their reasoning process to handle complex tasks. With access to tool suites, the models can retrieve current web information, run code for calculations, generate required images, and substantially expand AI's capabilities.

• Deep reasoning chains: O3 and O4-mini are reasoning models emphasizing internal multi-step inference. Unlike general LLMs that generate answers in a single pass, the O series conducts a series of chained reasoning steps before producing final output. The models automatically decompose complex problems into subproblems, solve them progressively, and internally validate each step. O3 in particular employs a carefully designed inference process: from initial problem-solving through strategy optimization to repeated verification before forming the final answer. This multi-stage reasoning produces more reliable and consistent results. During reasoning, the new models also leverage search and citations to verify information sources, enhancing verifiability. Reasoning chains equip the models with stronger logical consistency and problem decomposition, a major step toward general AI.

• Performance and efficiency optimization: While O3 possesses a large architecture and extremely high inference precision, its reasoning speed and resource overhead are correspondingly substantial. OpenAI therefore optimized O4-mini as an efficient reasoning engine. Through streamlined model parameters and efficient inference algorithms, O4-mini dramatically improves response speed and throughput, handling more requests per unit time. According to OpenAI's data, O4-mini's lower computational cost permits much higher usage frequency than O3: in enterprise ChatGPT, O3 allows only 100 messages per user per week, while O4-mini permits 300 messages per day. This gap directly reflects the enormous difference in computational cost between the two. For O4-mini, an O4-mini-high mode is also available, which spends additional compute time to further improve answer reliability. Regarding model compression, OpenAI has not disclosed details, but industry speculation suggests O4-mini may employ techniques such as knowledge distillation, inheriting knowledge from larger models to approach O3's performance at smaller scale. Both models incorporate OpenAI's latest safety mechanisms and fine-tuning strategies to ensure high-quality outputs while minimizing inappropriate content and bias.

Application Scenarios

• Advanced assistants and complex Q&A: With powerful multi-step reasoning and tool-calling capabilities, O3 is well-suited as an AI assistant for complex tasks. On business analysis, research decisions, legal consultation, and other multifaceted problems, O3 deeply understands problem context, retrieves and computes necessary information, and provides well-structured solutions. It demonstrates strong analytical rigor and creativity in conversation, serving effectively as an intellectual partner to professionals.

• Programming and code generation: O3 is recognized for producing high-quality code directly from requirement descriptions, covering mainstream programming languages and frameworks. In software development, it not only writes code but assists with debugging and multilingual code translation, functioning as an exceptionally powerful AI programming partner. O4-mini, despite its smaller size, performs well on algorithm design and code completion tasks due to specialized optimization training on mathematics and coding. Combined with Python tools for unit testing or data analysis, it serves effectively as a developer's everyday high-efficiency coding assistant.

• Mathematical solving and logical reasoning: Both models demonstrate far greater capability in mathematics and logical reasoning than general models, applicable to automated problem-solving and theorem-proving assistance. O4-mini especially excels at mathematical reasoning, frequently delivering high-accuracy solutions to mathematical competition problems and engineering calculations. This makes it suitable for online education Q&A, scientific computing tools, and other applications demanding rigorous computation. Combined with its fast response times, O4-mini can handle large volumes of mathematical solving requests cost-effectively.

• Multimodal content analysis: Leveraging joint image and text reasoning capabilities, O3 and O4-mini can analyze complex visual scenarios, charts, and multimedia content. In medical contexts, the models can synthesize analysis of medical imaging with patient records; in security applications, they can draw logical inferences from surveillance footage; in business settings, they can read and analyze chart data in reports to draw conclusions. This "visual thinking" ability expands AI's applicability in real-world scenarios.

• General conversational chat and content creation: As the latest models on the ChatGPT platform, O3 and O4-mini support everyday conversation, writing coaching, and content creation. O3 excels at creative writing and complex topic discussion, proposing novel perspectives and engaging in critical thinking. This suits demanding creative tasks such as long-form article writing, screenplay ideation, and academic paper refinement. O4-mini handles general writing and chat tasks effectively, and its lower cost makes it suitable for large-scale content generation such as batch product descriptions and customer service chatbots. For developers seeking to embed powerful AI in their products, O4-mini offers a more feasible option for embedded deployment or mobile applications, as it has relatively modest hardware requirements and can run inference in resource-constrained environments.

• Tool-based Agent systems: Leveraging autonomous tool calling, developers can build various AI Agent systems. For example, an automated research assistant: O3 understands complex research questions, automatically searches literature and data, invokes code tools for analysis, and compiles a research report draft. Or an intelligent home manager: the model analyzes voice commands, calls IoT device interfaces to execute actions, and uses image recognition to confirm execution. In these multi-step, multi-interface scenarios, O3 and O4-mini's autonomous decision-making and tool integration capabilities enable them to serve as the coordinating "brain" across components.

Key Parameters and Feature Comparison: O3 vs. O4-mini

The following table summarizes the similarities and differences between OpenAI O3 and O4-mini across major parameters and features:

References:

https://help.openai.com/en/articles/9855712-openai-o3-and-o4-mini-models-faq-chatgpt-enterprise-edu

https://news.sohu.com/a/885391457_122004016