Introduction
In the rapidly evolving landscape of artificial intelligence, we’re witnessing fierce competition between leading AI models. Today, I’m sharing a fascinating live demo comparison between DeepSeek R1 and ChatGPT on a complex mathematical problem. The results were not just surprising—they were mind-blowing.
The Challenge
For this test, I selected an exceptionally difficult math question from a blog post titled “The Oddest Math Questions Ever.” I deliberately chose a problem where I already knew the answer (which should be “A”) to effectively evaluate both models’ performance.
The experiment was simple: paste the exact same complex mathematical question into both DeepSeek and ChatGPT and compare their approaches, reasoning processes, and final answers.
ChatGPT’s Approach: Speed Over Accuracy
ChatGPT immediately jumped into action, displaying its characteristic efficiency. Within just 4 seconds, the model had analyzed the problem, shown some basic steps, and delivered its conclusion.
The verdict? ChatGPT determined the answer was “D” and stated that “the value cannot be determined.”
This quick response time is impressive, but there was a critical problem—the answer was incorrect. According to the source, the correct answer should have been “A.”
DeepSeek’s Approach: Deep, Methodical Reasoning
When I entered the same question into DeepSeek, I initially encountered a busy server message—a testament to the platform’s growing popularity. After trying again, I got to witness something remarkable.
DeepSeek took a fundamentally different approach. Instead of rushing to a conclusion, the AI began thinking through the problem in a manner strikingly similar to human reasoning:
- Initial Assessment: It started by carefully analyzing the question structure
- Multiple Solution Paths: Unlike ChatGPT, DeepSeek explored various solution methods
- Error Detection: It questioned whether there might be formatting issues in how I’d copied the problem
- Alternative Interpretations: It considered different ways the expression could be understood
- Self-Correction: When one approach seemed problematic, it pivoted to others
What truly distinguished DeepSeek was its metacognitive abilities. When it noticed potential formatting errors in how the math problem was copied, it didn’t simply give up or provide an incorrect answer. Instead, it tried different interpretations of the formula to determine what the original problem likely meant.
The Astonishing Result
While ChatGPT took only 4 seconds to provide an incorrect answer, DeepSeek thought for an impressive 280 seconds—nearly 5 minutes of deep analysis.
During this extended reasoning process, DeepSeek initially calculated an answer that matched ChatGPT’s conclusion (“D”). However, instead of stopping there, it continued exploring:
“It’s crazy because ChatGPT only thought for 4 seconds, and [DeepSeek] is still going… I’m not going to speed up or cut out anything like that. I want to show how much time it took exactly.”
What happened next was remarkable. DeepSeek recognized that there might have been formatting issues in how I pasted the formula. It systematically worked through alternative interpretations of the problem, considering whether “maybe the person who typed this has typed it wrong.”
After 280 seconds of thorough analysis, DeepSeek provided the correct answer: “A”—matching the expected result from the source material.
Why This Matters: The Power of Deep Reasoning
This demonstration highlights a fundamental difference in how these AI models approach complex problems:
- ChatGPT: Optimized for speed and efficiency, providing quick answers that may miss nuance in complex scenarios
- DeepSeek: Designed for thorough analysis with human-like reasoning, willing to spend significantly more time to arrive at the correct answer
The most impressive aspect wasn’t just that DeepSeek got the right answer—it’s how it got there. The model demonstrated:
- Persistence: Continuing to work long after most AIs would have settled on an answer
- Self-questioning: Challenging its own assumptions multiple times
- Creative problem-solving: Considering that the input itself might have formatting issues
- Multiple solution pathways: Trying different mathematical approaches when one seemed to fail
Conclusion
This comparison reveals something profound about the current state of AI reasoning capabilities. While quick responses are valuable in many scenarios, DeepSeek demonstrates that for complex mathematical problems, the ability to think deeply, question assumptions, and explore multiple solution paths can make the difference between right and wrong answers.
As AI continues to evolve, this balance between speed and depth of reasoning will become increasingly important. DeepSeek’s approach—taking time to thoroughly analyze problems before providing answers—points to an exciting direction for the future of artificial intelligence in tackling complex intellectual challenges.
In this particular math challenge, DeepSeek’s willingness to spend 280 seconds on deep analysis versus ChatGPT’s 4-second response made all the difference. The clear winner in this mathematical showdown? DeepSeek, by a significant margin.
What do you think about these different approaches to AI problem-solving? Would you prefer a quick answer that might be wrong, or are you willing to wait longer for more thorough analysis? Share your thoughts in the comments below!
