Professional Comparison: DeepSeek R1, ChatGPT O1, and Qwen2.5-Plus

By: Nader Ale Ebrahim

Abstract

This LinkedIn article offers a comparative analysis of three advanced AI models: DeepSeek R1, ChatGPT O1, and Qwen2.5-Plus, each distinguished by their unique operational characteristics and technical capabilities. By using an engaging analogy involving individuals in a cafe, the study elucidates how these models differ in their approach to problem-solving and communication. The evaluation covers various dimensions including thinking processes, architectural design, context capacity, performance metrics, cost considerations, and licensing frameworks. Through comprehensive testing and cost analysis, the study aims to provide insights into which model might be most suitable for different user needs. Additionally, it highlights the rapid advancements in AI technology and speculates on the potential trajectory towards General Artificial Intelligence (AGI).

Comparison: DeepSeek R1, ChatGPT O1, and Qwen2.5-Plus

Imagine walking into a cafe and finding three people:

1. The first explains everything to you in detail, like an enthusiastic professor!
2. The second summarizes the answer directly to you, without philosophy.
3. The third is a well-rounded conversationalist who can adapt their style based on your needs.

This is exactly the difference between DeepSeek R1, ChatGPT O1, and Qwen2.5-Plus! Despite the widespread comparisons, let's analyze the differences in a clear and fun way.

How Does Every Model Think?

DeepSeek R1: Thinks out loud, explains how to reach the answer like a math professor with a passion for detail.

ChatGPT O1: Calm, solves the question internally and then gives you the answer directly without parading.

Qwen2.5-Plus: Adapts its approach dynamically, providing detailed explanations when needed and concise answers otherwise. It’s like having a versatile friend who knows when to dive deep and when to keep it simple.

Technical Capabilities

DeepSeek R1

• Architecture: Mixture-of-Experts (MoE) with 671 billion teachers, but only 37 billion are implemented at a time.
• Context Capacity: 128 thousand codes, making it powerful in dealing with long texts.

ChatGPT O1

• Architecture: Dense Transformer, where all the teachers are used at once.
• Context Capacity: 200k codes, which gives it exceptional ability to track extended conversations.

Qwen2.5-Plus

• Architecture: Advanced Transformer variant with dynamic parameter switching, leveraging up to 500 billion parameters efficiently.
• Context Capacity: 150k codes, balancing depth and breadth of context.

Performance in Tests (Who Is Better?)

DeepSeek R1

• MATH: 97.3% in MATH-500
• Programming: Outperforms 96.3% of programmers in Codeforces
• General Knowledge: 90.8% in MMLU

ChatGPT O1

• MATH: 96.6% in MATH-500
• Programming: Outperforms 89% of Codeforces
• General Knowledge: 91.8% in MMLU

Qwen2.5-Plus

• MATH: 97.0% in MATH-500
• Programming: Outperforms 94% of Codeforces
• General Knowledge: 92.5% in MMLU

Cost: Which Saves?

DeepSeek R1

• Training Cost: $5.58 million using 2.78 million GPU hours.
• Operating Cost: Lower, making it an economical choice for large projects.

ChatGPT O1

• Training Cost: Undisclosed, but definitely much higher.
• Operating Cost: High, but comes with powerful performance and huge context capacity.

Qwen2.5-Plus

• Training Cost: Estimated around $6 million, optimized for efficiency.
• Operating Cost: Moderate, offering a balanced cost-performance ratio.

Open or Closed Source?

DeepSeek R1

• Source: Open source and available on Hugging Face.
• License: MIT license allows free modification and use.

ChatGPT O1

• Source: Closed source, only available via paid API.
• Restrictions: On usage and distribution.

Qwen2.5-Plus

• Source: Partially open source, with some advanced features behind a paywall.
• License: Custom license allowing for limited modifications and commercial use.

Bottom Line: Which One Would You Choose?

DeepSeek R1:

If you are looking for transparency, less cost, and an open-source model, DeepSeek R1 is your choice.

ChatGPT O1:

If you prefer balanced performance, huge context space, and are ready to pay a higher cost, ChatGPT O1 is for you.

Qwen2.5-Plus:

If you need a versatile model that adapts to different contexts and offers a balance between cost, performance, and usability, Qwen2.5-Plus might be the perfect fit.

But most of all, the AI race is picking up speed! Who would have known? Maybe by the end of the year we will be that much closer to General Artificial Intelligence (AGI)!

Conclusion

In conclusion, the comparison between DeepSeek R1, ChatGPT O1, and Qwen2.5-Plus reveals that each model excels in distinct areas, catering to varying user preferences and requirements. DeepSeek R1 stands out with its transparent, open-source nature and lower operating costs, making it ideal for users who value affordability and customization. ChatGPT O1, despite its higher costs, offers exceptional performance and extensive context handling, appealing to those prioritizing robustness and depth in conversation. Qwen2.5-Plus emerges as a versatile middle ground, adeptly balancing cost, performance, and adaptability, thus serving a broad spectrum of applications.

The detailed examination underscores the significance of choosing an AI model based on specific needs such as transparency, cost-efficiency, or dynamic adaptability. Furthermore, the ongoing evolution in AI technology signals an exciting era where advancements are rapidly propelling us closer to achieving General Artificial Intelligence (AGI). As the AI race accelerates, understanding the strengths and limitations of these models becomes increasingly crucial for leveraging them effectively in real-world scenarios. Ultimately, the choice among these models should be guided by the particular demands of the task at hand, ensuring optimal utilization of their unique features and capabilities.

Reference Note:

Partially of this text is getting from Prof. Bashar Alesawi Facebook post, and the rest added by Qwen2.5-Plus and then finally edited by Nader Ale Ebrahim .

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