DeepSeek vs ChatGPT the ultimate ai battle.pptx

DeepSeek vs ChatGPT

1. DeepSeek vs ChatGPT: Architectural Comparison In this section, we will discuss the key architectural differences between DeepSeek-R1 and ChatGPT 40. By exploring how these models are designed, we can better understand their strengths, weaknesses, and suitability for different tasks. This comparison will highlight DeepSeek-R1’s resource-efficient Mixture-of-Experts ( MoE ) framework and ChatGPT’s versatile transformer-based approach, offering valuable insights into their unique capabilities.

2. DeepSeek R1: Mixture-of-Experts ( MoE ) Architecture: Uses 671 billion parameters but activates only 37 billion per query, optimizing computational efficiency. Reinforcement Learning (RL) Post-Training: Enhances reasoning without heavy reliance on supervised datasets, achieving human-like "chain-of-thought" problem-solving. Cost-Effective Training: Trained in 55 days on 2,048 Nvidia H800 GPUs at a cost of $5.5 million—less than 1/10th of ChatGPT’s expenses

3. ChatGPT 4: Dense Model Architecture: A monolithic 1.8 trillion-parameter design optimized for versatility in language generation and creative tasks. Advanced Chain-of-Thought Processing: Excels in multi-step reasoning, particularly in STEM fields like mathematics and coding. Proprietary Training: Built on OpenAI’s GPT-4o framework, requiring massive computational resources (estimated $100 million+ training cost). Key Difference: DeepSeek prioritizes efficiency and specialization, while ChatGPT emphasizes versatility and scale.

4. Performance Benchmark Testing Metric DeepSeek R1 ChatGPT Mathematics 90% accuracy (surpasses GPT-4o) 83% accuracy on advanced benchmarks Coding 97% success rate in logic puzzles  Top-tier debugging (89th percentile on Codeforces)  Reasoning RL-driven step-by-step explanations Superior multi-step problem-solving Multimodal Tasks Text-only focus Supports text and image inputs In this section, we will look at how DeepSeek-R1 and ChatGPT perform different tasks like solving math problems, coding, and answering general knowledge questions. By comparing their test results, we’ll show the strengths and weaknesses of each model, making it easier for you to decide which one works best for your needs.

5. DeepSeek vs ChatGPT: Real World Testing After performing the benchmark testing of DeepSeek R1 and ChatGPT let's see the real-world task experience. Here In this section, we will explore how DeepSeek and ChatGPT perform in real-world scenarios, such as content creation, reasoning, and technical problem-solving. By examining their practical applications, we’ll help you understand which model delivers better results in everyday tasks and business use cases.

6. Content Creation Task I asked both DeepSeek and ChatGPT to create an outline for an article on  What is LLM  and How it Works. I asked, “I’m writing a detailed article on What is LLM and How it Works, so provide me the points which I include in the article that help users to understand the LLM models. Help me craft an outline”

7. DeepSeek Response: The results were impressive. Both AI chatbot models covered all the main points that I can add into the article, but DeepSeek went a step further by organizing the information in a way that matched how I would approach the topic. It also included important points What is an LLM, its Definition, Evolution and milestones, Examples (GPT, BERT, etc.), and LLM vs Traditional NLP, which ChatGPT missed completely. DeepSeek even showed the thought process it used to come to its conclusion, and honestly, the first time I saw this, I was amazed. While we’re still a long way from true  artificial general intelligence , seeing a machine think in this way shows how much progress has been made. The thought process was so interesting that I’m sharing a short transcript below. Quoting "Okay, I need to help the user create an outline for an article explaining what LLMs are and how they work. Let me start by recalling what I know about LLMs. They're large language models, right? Like GPT-3, BERT, etc. The user probably wants a comprehensive outline that breaks down the topic into digestible sections. Let me think about the key points that should be covered."

8. ChatGPT Response: On the other hand, ChatGPT also provides me the same structure with all the mean headings, like Introduction, Understanding LLMs, How LLMs Work, and Key Components of LLMs. Additionally, ChatGPT also provides you with the points that you have to discuss in the Heading. 1. Introduction Briefly explain what LLM stands for (Large Language Model). Mention their growing importance in various fields like content creation, customer service, and technical support. 2. Understanding LLMs Define LLM and explain its purpose. How LLMs are designed to understand and generate human-like text.

9. Deepseek Response

10. Chatgpt Response