I tested ChatGPT o3-mini vs Gemini 2.0 Flash with 7 prompts ...
OpenAI's o3-mini and Google Gemini 2.0 are both advanced AI language models that are built for speed while maintaining accuracy. Best of all, they are available for free to users. Gemini just announced it has made Gemini 2.0 accessible for general availability while OpenAI recently made o3-mini available to all ChatGPT users for free.
AI Models Capabilities
Both AI models have the ability to “remember,” which makes the chatbots respond with clear, more human-like responses the more they are used. Both models are designed to enhance reasoning capabilities, particularly in complex problem-solving tasks such as advanced mathematics and coding.
Yet, they differ in several areas. For instance, o3-mini supports an input context window of up to 200,000 tokens while Gemini 2.0 Flash offers a significantly larger context window that supports 1 million tokens, making it suitable for high-volume, high-frequency tasks. Google's model can generate relevant one-line captions for approximately 40,000 unique photos at a cost of less than a dollar in Google AI Studio’s paid tier. OpenAI’s o3-mini primarily focuses on text-based processing while Gemini 2.0 Flash extends support to voice and video processing, enabling multimodal interactions.
Comparison of o3-mini and Gemini 2.0 Flash
While both o3-mini and Gemini 2.0 are designed to enhance reasoning and problem-solving capabilities, they differ largely enough that I had to compare them to see these distinctions first-hand. Here’s what happened when I put the two free tier chatbots to seven different prompts that test their suitability for specific applications and use cases, ranging from reasoning and coding to mathematics and more.
Prompt: "Prove that the sum of the squares of any two sides of a right-angled triangle equals the square of the hypotenuse."
o3-mini delivered a succinct response that directly walks through the classic rearrangement argument. It identifies the right triangle, constructs the square of side a+ba+ba+b, places four triangles inside it, and equates the total area calculated in two ways.
Gemini 2.0 Flash offered a very detailed, step-by-step explanation, that not only describes the geometric construction (placing four copies of the right‐angled triangle inside a larger square) but also explains why the inner quadrilateral must be a square. Each step is carefully justified, with attention paid to why the areas add up as they do.
Winner: Gemini 2.0 Flash wins for clarity, depth and the step-by-step explanation that not only shows the “how” but also the “why” behind each part of the proof.
Prompt: "Design an algorithm to sort a list of integers using the merge sort technique and explain its time complexity."
o3-mini delivered a response that followed a clear and logical flow while breaking down merge sort into three main steps. The response is easy to read and avoids unnecessary repetition yet presents the information in a way that is easy to grasp and apply in practice.
Gemini 2.0 Flash spent too much time discussing how to structure the answer, making the response far too detailed and wordy. It also repeats concepts and offers too many unnecessary details before actually explaining the algorithm.
Winner: o3-mini wins for a well-organized, practical, and easy-to-follow response making it more useful for someone trying to understand merge sort and implement it.
Prompt: "You have two ropes of uneven thickness that each take exactly one hour to burn. How can you measure 45 minutes using these ropes?"
o3-mini provided a correct and clear answer but it's more concise and lacks deeper reasoning. It explains the steps well but doesn't go as much into why this trick works, which can be useful for someone unfamiliar with these types of logic puzzles. Gemini 2.0 Flash clearly walks through the response and includes why the method works, breaking down the problem logically. It debunks common misconceptions (like assuming you can measure by length), explains the concept of burning from both ends to halve the time, and lays out the sequence clearly.
Winner: Gemini 2.0 Flash wins for a more thorough explanation with reasoning behind each step.
Prompt: "Implement a binary search tree in Python and include methods for insertion, deletion, and search operations."
o3-mini delivered a concise and well-structured response. The implementation is compact but still covers all necessary operations. Gemini 2.0 Flash also delivered an accurate response with clear structure and detailed explanation. It includes docstrings explaining each class and method, making it easier to understand.
Winner: Gemini 2.0 Flash offered a more robust, well-documented and user-friendly BST implementation. It wins for both an educational and well-explained implementation.
