DeepSeek vs ChatGPT: A Technical Deep Dive into Modern LLM Architectures

The large language model (LLM) landscape is rapidly evolving, and two powerful contenders—DeepSeek and ChatGPT—are emerging as core engines in generative AI applications. While they both excel at generating human-like text, answering questions, and powering chatbots, they differ significantly in architecture, training objectives, inference capabilities, and deployment paradigms.

Not long ago, I had my first experience with ChatGPT version 3.5, and I was instantly amazed. It wasn’t just the speed with which it tackled problems but also how naturally it mimicked human conversation. That moment was like the start of a big AI chatbot competition, with ChatGPT leading the charge.

Now, there’s a new player DeepSeek R1. It’s a powerful AI language model that’s surprisingly affordable, making it a serious rival to ChatGPT. In this article, we’ll explore what DeepSeek R1 can do, how well it performs, and whether it’s worth the price. We’ll even compare it to ChatGPT in everyday tasks so you can decide which one is best for you.

In this blog, we unpack the technical differences between DeepSeek and ChatGPT to help you choose the right model for your use case or research.

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.

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.

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).

Model Foundations

Architectural Differences

• DeepSeek-LLM appears to use a modified transformer architecture with enhancements similar to LLaMA or MPT—supporting longer context windows (up to 32K) and efficient attention mechanisms.

• ChatGPT (GPT-4) uses a Mixture of Experts (MoE) model with sparse activation, making it efficient at scale—but details are mostly proprietary.

• GPT-4o, the latest variant, integrates native multimodal inputs, handling image, audio, and text in the same pass through a single model.

Training Strategy

DeepSeek is transparent about training scale and methodology, including open checkpoints. GPT-4 remains a black box in this regard.

Note: GPT-4 tends to edge out DeepSeek on general reasoning, but DeepSeek offers top-tier code and math performance for an open model.

️ Inference & Deployment

DeepSeek can be fine-tuned, quantized (GGUF, GPTQ), and deployed on consumer GPUs—unlike ChatGPT, which is usage-constrained to OpenAI’s ecosystem.

Openness & Community

✅ Use Case Recommendations

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.

Content Creation Task

As a writer, I’m not a big fan of AI-based writing, but I do think it can be useful for brainstorming ideas, coming up with talking points, and spotting any gaps. Now, to test this, 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”

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.”

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.

Academics Question Analysis

In our next test of DeepSeek vs ChatGPT, we were given a basic question from Physics (Laws of Motion) to check which one gave me the best answer and details answer. The question we asked with DeepSeek and ChatGPT is “Calculate the momentum of a ball thrown at a speed of 10 m/s and weighing 800 g“

DeepSeek Response:

Okay, let’s see. I need to calculate the momentum of a ball that’s thrown at 10 meters per second and weighs 800 grams. Hmm, momentum… I remember that momentum is related to mass and velocity. The formula, if I recall correctly, is momentum equals mass multiplied by velocity. So, p = m * v. Yeah, that seems right. But wait, the mass here is given in grams, right? The standard unit for mass in physics is kilograms, so I should probably convert grams to kilograms first. Let me check that.

DeepSeek recalls and analyzes the points that we have asked from it. But in the calculation process, DeepSeek missed many things like in the formula of momentum DeepSeek only wrote the formula.

There is no explanation of what “p” stands for, what m stands and so on. If we see the answers then it is right, there is no issue with the calculation process.

ChatGPT Response:

On the other hand, ChatGPT provided a details explanation of the formula and GPT also provided the same answers which are given by DeepSeek.

Coding Task

In the next process of DeepSeek vs ChatGPT comparison our next task is to check the coding skill. In the test, we were given a task to write code for a simple calculator using HTML, JS, and CSS. We know that both of the AI chatbots are not capable of full-fledged coating, hence we have given the easy task so we can check the coding skills of both of the AI titans.

DeepSeek Response:

As we have said previously DeepSeek recalled all the points and then DeepSeek started writing the code. Now, if says true then I need to correct DeepSeek two times and after that, DeepSeek provided me the right code for the calculator. The interface of the calculator is more simple and engaging.

Key Technical Differences Explained:

1. Architecture & Training

• DeepSeek-R1:

  • Trained on 2 trillion tokens with heavy focus on STEM, code, and Chinese data.

  • Uses a custom tokenizer optimized for Chinese-English alignment.

  • Not a Mixture of Experts (MoE) – single dense model for consistency.

• ChatGPT (GPT-4):

  • Uses Mixture of Experts (MoE) architecture (1.76T parameters total, 280B active per query).

  • Trained on broader web data but less specialized for non-English tasks.

2. Language & Reasoning

• DeepSeek-R1:

  • Beats GPT-4 in Chinese benchmarks (e.g., C-Eval, GAOKAO).

  • Outperforms in math/reasoning tasks (e.g., MATH: 51.7

• ChatGPT:

  • Better at creative writing and multilingual versatility.

3. Efficiency & Accessibility

• DeepSeek-R1:

  • Open weights released for research (e.g., DeepSeek-Coder).

  • Free API with 128K context (no paywall).

• ChatGPT:

  • Closed ecosystem – users pay for API access.

  • Requires subscriptions for advanced features (e.g., GPT-4 Turbo).

4. Use Cases

• DeepSeek-R1:
  Ideal for:

  • Chinese NLP tasks

  • Math/problem-solving

  • Code generation (especially Python/C++)

• ChatGPT:
  Ideal for:

  • Multimodal tasks (text + images)

  • Content creation / copywriting

  • Plugin integrations (e.g., web browsing)

DeepSeek vs ChatGPT: Which One Should You Pick

Well after testing both of the AI chatbots, ChaGPT vs DeepSeek, DeepSeek stands out as the strong ChatGPT competitor and there is not just one reason. While I noticed Deepseek often delivers better responses (both in grasping context and explaining its logic), ChatGPT can catch up with some adjustments. But what makes Deepseek shine are its unique advantages.

Key Advantage of DeepSeek

• Cost-Effectiveness – More affordable, with efficient resource usage.
• Logical Structuring – Provides well-structured and task-oriented responses.
• Domain-Specific Tasks – Optimized for technical and specialized queries.
• Ethical Awareness – Focuses on bias, fairness, and transparency in responses.
• Speed and Performance – Faster processing for task-specific solutions.
• Ease of Use – Offers flexibility for professional and targeted use cases.
• Customizability – Can be fine-tuned for specific tasks or industries.
• Language Fluency – Excels in creating structured and formal outputs.
• Real-World Applications – Ideal for research, technical problem-solving, and analysis.

Key Advantage of ChatGPT

• Cost-Effectiveness – Freemium model available for general use.
• Logical Structuring – Delivers conversational and easy-to-understand replies.
• Domain-Specific Tasks –.Great for a wide range of general knowledge and creative tasks.
• Ethical Awareness – General responses with minimal built-in ethical filtering.
• Speed and Performance – Reliable performance across diverse topics.
• Ease of Use – Simple and intuitive for day-to-day questions and interactions.
• Customizability – Pre-trained for broad applications without extra tuning.
• Language Fluency – Natural, casual, and relatable tone in communication.
• Real-World Applications – Perfect for casual learning, creative writing, and general inquiries.