# GPT o3 & o4-mini {% embed url="" fullWidth="true" %} OpenAI O3 & O4-mini vs Claude 3.7 Sonnet {% endembed %} ## OpenAI O3 and O4 Mini: Redefining the Landscape of AI Reasoning ### 1. Executive Summary * **OpenAI o3 and o4-mini** are the latest additions to the "o-series" of models, announced on April 16, 2025. * These models are the most capable released by OpenAI, excelling in reasoning and complex task processing. * **Agentic tool use**: They can autonomously access and combine all tools available within ChatGPT (web search, file/data analysis, visual reasoning, image generation). * Emphasis on enhanced problem-solving, intelligent tool selection, and deeper reasoning. * Both models can "think with images," integrating visual inputs directly into their reasoning process for multimodal understanding. * **O3** is the powerhouse for maximum reasoning; **O4-mini** is optimized for efficiency and cost. ### 2. OpenAI O3: Unleashing Unprecedented Reasoning Power * **Most powerful reasoning model** to date from OpenAI. * Excels in coding, mathematics, science, and visual perception. * Achievements: * Codeforces ELO: 2706 (with terminal access) * SWE-bench: 69.1% * MMMU benchmark: new records * Handles complex, multi-faceted queries requiring deep analysis. * Strong visual reasoning: can interpret and manipulate images (rotation, zoom, transformation), even with low quality. * 20% fewer major errors than o1 in real-world tasks (notably in programming, consulting, creative ideation). * Large context window: **200,000 tokens**; output up to **100,000 tokens**. * Pricing: **$10/million input tokens**, **$40/million output tokens** (75% discount on cached input tokens). ### 3. OpenAI O4 Mini: Optimized for Speed and Efficiency * **Smaller, faster, and more cost-efficient** model. * Strong performance in mathematics, coding, and visual tasks. * Top scores: * AIME 2024: 93.4% * AIME 2025: 92.7% (99.5% with Python interpreter) * Outperforms o3-mini in non-STEM and data science tasks. * Supports higher usage limits, ideal for high-volume applications (e.g., chatbots, real-time analytics). * SWE-bench: 68.1% (close to o3) * Pricing: **$1.10/million input tokens**, **$4.40/million output tokens** (75% input caching discount). * Same context window and output limit as o3. * Available to free users via the "think" button in ChatGPT. ### 4. Agentic Tool Use: A Paradigm Shift * Both models can autonomously use and combine all ChatGPT tools. * Capabilities include: * Web search for real-time info * File/data analysis with Python * Deep visual reasoning * Image generation * Models reason about which tools to use and chain multiple tool calls for complex tasks. * Example: For "How will summer energy usage in California compare to last year?", the model can search, analyze, generate graphs, and explain—all autonomously. * Supports custom tools via API function calling. ### 5. Thinking with Images: Integrating Visual Intelligence * Models can "think with images," not just recognize them. * Integrate visual info into reasoning chains for multimodal problem-solving. * Handle various image types (whiteboards, diagrams, sketches), even if blurry or reversed. * Can manipulate images (rotate, zoom, transform) as part of reasoning. * Achieve best-in-class accuracy on visual perception tasks (e.g., MMMU, MathVista). * Example: O3 analyzed a research poster by zooming into elements to extract details for conclusions. ### 6. Comparative Analysis: O3 vs. O4 Mini | Feature | O3 (Powerhouse) | O4 Mini (Efficiency) | | --------------- | ------------------------------------------------- | --------------------------------------------------------- | | Reasoning Power | Highest | Strong | | Speed | Slower, more thorough | Very fast | | Cost (Input) | \~$10/million tokens | \~$1.10/million tokens | | Cost (Output) | \~$40/million tokens | \~$4.40/million tokens | | Use Cases | Deep analysis, research, ideation, complex coding | Customer support, dev tools, analytics, high-volume tasks | * O3: For users needing the highest reasoning, willing to pay premium. * O4-mini: Balanced performance and cost, suitable for broader applications. * Both show significant improvements over predecessors (o1, o3-mini). * O3: 200,000 token context window (vs. GPT-4's 128,000); 100,000 output tokens (vs. GPT-4's 4,096). * O3: 96.7% on AIME 2024 (vs. GPT-4's 64.5% on MATH). * GPT-4.1: Even larger context window (1M tokens), improved coding/instruction following, lower cost/latency. ### 7. Competitive Landscape * O4-mini outperforms Google's Gemini 2.5 Pro in mathematical reasoning (AIME 2024/2025). * O3 scores higher than Gemini 2.5 Pro on SWE-bench (coding). * Both models set new standards for reasoning, coding, and multimodal tasks. *** *This document summarizes the key features, differences, and competitive positioning of OpenAI's O3 and O4 Mini models, providing a clear, structured overview for easy reference.* --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ai.gadgetsxray.com/blog/gpt-o3-and-o4-mini.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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