> For the complete documentation index, see [llms.txt](https://ai.gadgetsxray.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai.gadgetsxray.com/blog/gpt-o3-and-o4-mini.md).

# GPT o3 & o4-mini

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OpenAI O3 & O4-mini vs Claude 3.7 Sonnet
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## 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.*