DeepSeek vs. ChatGPT Part 2: A Comparative Analysis in the "Manhattan Project 2.0" Era

Welcome back to our exploration of advanced AI systems. In this segment, I’ll compare the reasoning and computational abilities of DeepSeek R1 and OpenAI’s ChatGPT (o1 Advanced Reasoning). This guide will highlight their differences through quantitative problem-solving and Python coding tasks. As always, I’ve included hyperlinks to relevant resources for further learning.

Introduction

In this analysis, we’ll dive deeper into the capabilities of two leading AI systems. DeepSeek, developed by a Chinese AI company, recently released its advanced reasoning model, DeepSeek R1. On the other hand, ChatGPT o1 represents OpenAI’s competitive offering in the reasoning domain. To benchmark their abilities, I used a quantitative reasoning question—similar to those found on the SAT or GMAT—and assessed their outputs.

If you’re unfamiliar with concepts like large language models (LLMs) or quantitative reasoning, these resources might be helpful:

• Large Language Models and Linguistic Capital

• Introduction to LLMs

• SAT Quantitative Section Overview

• GMAT Quantitative Review

Reasoning and Problem-Solving

Task: Solve a Quantitative Problem

The prompt given to both models was: "Please solve this quantitative reasoning question and show your work."

1. Processing Time:

   • ChatGPT: 4 seconds

   • DeepSeek: Slightly longer, but displayed a more detailed chain of thought.

2. Output Comparison:

   • ChatGPT: 1,768 characters

   • DeepSeek: 7,397 characters

Observations:

• Chain of Thought Reasoning: DeepSeek’s output included a detailed explanation, making its reasoning process more transparent.

• Accuracy: Both models produced correct answers, but their presentation styles differed significantly.

For those curious about how AI models implement chain of thought reasoning, check out this article.

Python Code Generation

Next, I tested their ability to generate Python code based on a problem involving two trains traveling toward each other from Boston to San Francisco. The models were prompted to include colors representing the respective cities’ sports teams:

Prompt:

"Please write Python code to illustrate this problem, using colors that represent Boston and San Francisco sports teams."

• DeepSeek R1 Output:

  • Boston: Navy blue (Celtics, Red Sox, Bruins colors)

  • San Francisco: Orange (Giants, 49ers colors)

  • Code included Matplotlib for animations, showing the trains’ progress and a vertical dashed line indicating the meeting point.

• ChatGPT o1 Output:

  • Similar implementation but slightly slower completion time.

  • Provided concise yet functional code that adhered to the prompt.

Execution Results:

Both codes were tested in a development environment, and each accurately visualized the problem. DeepSeek’s output leaned toward more verbose explanations, while ChatGPT provided a cleaner, more streamlined script.

For those unfamiliar with Matplotlib or Python, these links can help:

• Matplotlib Documentation

• Python Programming Basics

Conclusion and Next Steps

This comparison illustrates the nuances of reasoning and computational tasks performed by large language models. DeepSeek R1’s transparency and affordability make it an attractive option for certain use cases, while ChatGPT o1 continues to excel in accessibility and efficiency.

Future Exploration:

In the next segment, I’ll challenge these models to create a lean model canvas and design a front-end landing page. Stay tuned for more insights into how these tools can streamline complex tasks.

Additional Resources and Contact Information

Check out the RUDI prompt library, 

Hoff

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