The latest AI reasoning models from OpenAI and Google are pushing the boundaries of machine intelligence in 2025. These systems don’t just spit out instant answers - they carefully work through problems step-by-step, aiming for more logical and reliable results. But how do they actually stack up against each other on real-world tasks?
We put OpenAI’s o3-Mini-High and Google’s Gemini 2.0 Flash Thinking through a gauntlet of challenging prompts to see which one comes out on top. From tricky logic puzzles to complex math problems, here’s how these cutting-edge AI models performed:
Logic Puzzle Challenge
We started with a classic knights and knaves logic puzzle:
On an island, every inhabitant is either a knight, who always tells the truth, or a knave, who always lies. You meet three inhabitants: A, B, and C.
A says, 'B is a knave.'
B says, 'C is a knight.'
C says, 'A is a knight.'
Who is what?
This puzzle is designed to be unsolvable, testing if the AI can recognize the logical impossibility.
OpenAI o3-Mini-High: Correctly identified that the puzzle has no valid solution in about 15 seconds.
Google Gemini 2.0: Initially recognized the puzzle was unsolvable, but then continued reasoning and arrived at an incorrect answer after about 45 seconds.
Winner: OpenAI o3-Mini-High for speed and accuracy in logical reasoning.
Mathematical Reasoning
Next, we tested their mathematical capabilities with a probability question:
You have a deck of 52 playing cards (standard deck). You draw five cards at random. What is the probability that you have exactly three aces in your hand? Show your work.
OpenAI o3-Mini-High: Provided a correct answer with clear, concise steps in about 10 seconds. The explanation was easy to follow but didn’t delve into deep mathematical concepts.
Google Gemini 2.0: Also arrived at the correct answer in roughly 10 seconds. However, Gemini’s explanation was more thorough, clearly stating the formulas used and providing more context for each step.
Winner: Tie. Both models solved the problem correctly and quickly. Gemini provided more detailed explanations, while OpenAI’s answer was more concise.
Visual Reasoning: Sudoku Challenge
To test visual comprehension, we presented both models with a Sudoku puzzle image. This task proved challenging for both AIs.
OpenAI o3-Mini-High: Misinterpreted the image, claiming there were duplicate numbers in certain columns when there weren’t.
Google Gemini 2.0: Initially created an incorrect 12x12 grid instead of the standard 9x9, leading to errors. On a second attempt, it struggled to generate any output.
When provided with a text-based Sudoku puzzle instead, both models improved but still made mistakes:
OpenAI o3-Mini-High: Recognized its limitations and stated it was having trouble solving the puzzle.
Google Gemini 2.0: Produced a nearly correct solution with only a couple of misplaced numbers.
Winner: Slight edge to Google Gemini 2.0 for coming closer to a correct solution, but both models struggled with this visual reasoning task.
Hypothetical Scenario Analysis
We challenged the AIs with a complex hypothetical scenario:
If the internet had been invented 50 years earlier—in the 1940s instead of the 1990s—explain three major impacts on society today, covering technological, cultural, and geopolitical aspects. Support each impact with reasoning based on historical context.
Both models provided similar predictions, focusing on:
- Accelerated technological development, particularly in communications
- Faster cultural exchange and social movements
- Potential use of the internet as a powerful tool during the Cold War
While the predictions were plausible, both models took a relatively safe approach, avoiding more speculative or nuanced outcomes. Neither model fully explored how specific government policies or societal structures might have radically changed.
Winner: Tie. Both models provided reasonable predictions but didn’t push boundaries in their analysis.
Programming Challenge
Finally, we tested their coding abilities:
Write a Python program that determines whether a given sentence is positive, negative, or neutral. For each classification, provide an explanation for why the sentence was categorized that way. Handle complex sentences, such as those with sarcasm, double negatives, or mixed sentiments. Create a graphical interface where users can input a sentence and see the sentiment analysis results in real-time.
OpenAI o3-Mini-High: Produced a working Python script using third-party libraries. However, it didn’t include explanations for sentiment classifications as requested.
Google Gemini 2.0: Also created a functional script with third-party modules. It included sentiment explanations but didn’t implement real-time analysis in the GUI as specified.
Winner: Tie. Both models created workable code but missed key aspects of the prompt.
The Verdict: Free vs Paid AI Reasoning
After extensive testing, Google’s free Gemini 2.0 Flash Thinking model performed remarkably well against OpenAI’s paid o3-Mini-High offering. Gemini matched or came close to o3-Mini-High’s capabilities in most tasks, with a few notable exceptions:
- OpenAI’s model excelled at pure logical reasoning, as seen in the unsolvable puzzle challenge.
- Gemini provided more detailed explanations for mathematical problems.
- Both models struggled with visual reasoning tasks like Sudoku.
- Programming capabilities were roughly equivalent, with both missing some prompt details.
For most users, the free Gemini 2.0 Flash Thinking model likely provides sufficient reasoning capabilities without the need for a paid subscription. However, if you require top-tier logical analysis or are already a ChatGPT Plus subscriber, the o3-Mini-High model may offer a slight edge in certain scenarios.
As AI reasoning models continue to evolve, the gap between free and paid offerings narrows. While OpenAI’s o3-Mini-High shows some advantages in specific areas, Google’s Gemini 2.0 Flash Thinking proves that powerful AI reasoning capabilities are becoming increasingly accessible to all users.