DeepSeek AI vs OpenAI- Which Is Better and Why?

Hi, I’m Aqsa Zafar, the founder of MLTut and a Ph.D. scholar in Machine Learning. I’m always on the lookout for exciting AI developments, and today I want to share something incredible—DeepSeek R1, an AI model that’s powerful, affordable, and open to everyone. Most AI models you’ve heard of, like the ones from OpenAI or Meta, cost billions of dollars to create. But DeepSeek R1? It was built with just $5.58 million—a tiny fraction of that—and it still competes with the big players. In this blog, I will share DeepSeek AI vs OpenAI.

Now, without further ado, let’s get started and see DeepSeek AI vs OpenAI- Which Is Better and Why?

DeepSeek AI vs OpenAI- Which Is Better and Why?

What Is DeepSeek R1?

DeepSeek R1 is an advanced AI model with 671 billion parameters (think of these as the brain cells of AI). It’s designed to be really good at solving tough problems, like understanding complex ideas and answering tricky questions.

But here’s what makes it stand out:

• Customizable: You can even use smaller, pre-trained versions of the model if you don’t need the full power of the big one.
• Open-source: Anyone can use it for free, whether it’s for learning, research, or business.
• Efficient: Even though it has 671 billion parameters, it only activates a small portion at a time (37 billion), making it faster and cheaper to use.

How Was DeepSeek R1 Built So Cheaply?

DeepSeek R1’s creators, a startup called DeepSeek AI, used some really clever ideas to keep costs low. Here’s how they did it:

1. Smarter Training:
   • Instead of using massive amounts of computing power, they trained the model with just 2.78 million GPU hours (compare that to Meta’s 30.8 million GPU hours!).
   • They also used self-evolution reinforcement learning, which helps the model get smarter on its own without needing a ton of extra data.
2. Focus on Quality:
   • They started with a small, high-quality dataset to make sure the model was fluent in language and good at reasoning.
3. Optimized Parameters:
   • By activating only the parameters needed for a specific task, the model stays efficient and doesn’t waste resources.

This means they spent a fraction of what others spend, without sacrificing performance.

How Does DeepSeek R1 Compare to OpenAI o1?

Image Credit- DeepSeek-R1

Let’s look at some benchmarks (tests that measure AI performance) to see how DeepSeek R1 stacks up against OpenAI o1:

Task	DeepSeek R1 Score	OpenAI o1 Score	Winner
Math Problems	97.3%	96.7%	DeepSeek R1
Logical Reasoning	79.8%	79.2%	DeepSeek R1
General Knowledge	90.8%	91.2%	OpenAI o1
Coding Challenges	96.3%	96.6%	OpenAI o1

Key Takeaways:

• DeepSeek R1 is amazing at reasoning and solving math problems.
• OpenAI o1 is slightly better for general knowledge and coding.
• Both are strong competitors, but DeepSeek R1 does all this for a fraction of the cost.

Why DeepSeek R1 Is Affordable for Everyone

One of the coolest things about DeepSeek R1 is how much money you can save using it. Let’s compare its API pricing (what you pay to use it) with OpenAI:

Cost (per million tokens)	DeepSeek R1	OpenAI o1
Input	$0.55	$15.00
Output	$2.19	$60.00

That’s 96.4% cheaper than OpenAI! If you’re a student, a startup, or anyone on a budget, DeepSeek R1 gives you access to world-class AI without breaking the bank.

Smaller Models for Specific Needs

Not everyone needs the full power of a giant AI model. That’s why DeepSeek R1 also offers distilled models—smaller versions optimized for specific tasks. Here’s a quick look:

Model	Size	Where to Get It
Qwen-1.5B	1.5 billion	Hugging Face
Qwen-7B	7 billion	Hugging Face
Llama-8B	8 billion	Hugging Face

These smaller models are:

• Faster to run: Perfect for laptops or smaller servers.
• Cost-effective: Great for projects with tight budgets.
• Flexible: You can use them for anything from chatbots to personalized tutoring systems.

Real-Life Uses of DeepSeek R1

DeepSeek R1 isn’t just theoretical—it’s already making a difference in many areas:

• Education: Helping students solve tough math problems.
• Customer Support: Giving smarter, faster answers to user queries.
• Healthcare: Assisting doctors in diagnosing and decision-making.
• Programming: Debugging code and solving programming challenges.
• Content Creation: Generating articles, scripts, and other creative content.

No matter your field, DeepSeek R1 can make your work easier and more efficient.

Why I’m Excited About DeepSeek R1

What I love most about DeepSeek R1 is how it makes advanced AI accessible to everyone. You don’t need a billion-dollar budget or a giant data center to use it. Whether you’re a beginner just getting into AI or a startup looking to innovate, this model offers powerful tools at an unbeatable price.

Step-by-Step Guide to Start Using DeepSeek R1

If you’re new to using AI models like DeepSeek R1, don’t worry! Here’s a simple guide to help you get started. Follow these steps, and you’ll have the model running in no time.

Step 1: Access the Model

• Visit Hugging Face or GitHub to find DeepSeek R1. These platforms make it easy to download or try AI models online.
• Search for “DeepSeek R1” in the search bar.
• Click on the model’s page to access details, documentation, and setup files.

Step 2: Set Up Your Environment

Before using the model, you’ll need to prepare your computer. Don’t worry; it’s simple!

1. Install Python: Download Python from python.org (version 3.8 or later is recommended).
2. Install Dependencies:
   • Open your terminal or command prompt.
   • Type the following command to install the required libraries:

pip install transformers torch

These libraries allow you to use and run AI models.

Step 3: Choose a Platform

You can run the model on:

• Google Colab (Free and cloud-based): Ideal if you don’t have a powerful computer. Just log in to Google Colab and use it like a notebook.
• Your Local Machine: If your computer has enough power (RAM and GPU), you can run the model directly on your device.

Step 4: Load the Model

Once your environment is ready, you can load the model in a Python script. Don’t worry if you’re not a coding expert. I’ll show you an easy example below.

Step 5: Test the Model

Run a simple task like summarizing a text or answering a question to see how it works. This gives you a hands-on feel of its capabilities.

Step 6: Explore and Experiment

Now that you’ve set up the model, try experimenting with different inputs. Whether it’s solving math problems or generating summaries, DeepSeek R1 can handle it.

Python Script to Use DeepSeek R1 for a Simple Task

Here’s a step-by-step Python example to help you use DeepSeek R1. In this case, we’ll ask the model to explain a simple topic.

1. Import Required Libraries

# Import the necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer

2. Load the Model and Tokenizer

The tokenizer breaks text into pieces the model can understand, and the model generates responses.

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DeepSeek/R1")
model = AutoModelForCausalLM.from_pretrained("DeepSeek/R1")

3. Provide Input Text

Let’s give the model a topic to explain:

# Input text for the model
input_text = "Explain what artificial intelligence is in simple words."

4. Tokenize the Input

Convert your input into a format the model can understand:

# Prepare the input for the model
inputs = tokenizer(input_text, return_tensors="pt")

5. Generate a Response

Ask the model to process your input and generate an output:

# Generate a response from the model
outputs = model.generate(**inputs, max_length=100, num_return_sequences=1)

6. Decode and Print the Response

Finally, convert the model’s output into readable text:

# Decode the output and print the result
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Complete Code

Here’s the full script for reference:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("DeepSeek/R1")
model = AutoModelForCausalLM.from_pretrained("DeepSeek/R1")

# Input text
input_text = "Explain what artificial intelligence is in simple words."

# Prepare the input
inputs = tokenizer(input_text, return_tensors="pt")

# Generate a response
outputs = model.generate(**inputs, max_length=100, num_return_sequences=1)

# Decode and print the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

DeepSeek AI vs. OpenAI: Beginner-Friendly Comparison Table

Feature	DeepSeek AI	OpenAI
When It Started	Created in 2023 by Liang Wenfeng	Started in 2015 by Elon Musk, Sam Altman, and others
Main Goal	Make AI open and accessible to everyone for free	Ensure Artificial General Intelligence (AGI) benefits all of humanity
Famous Model	DeepSeek-R1	GPT-4
Development Cost	Less than $6 million (very cost-effective)	Costs hundreds of millions of dollars to develop
How It’s Built	Fully open-source (free to use, anyone can contribute)	Proprietary (code and models are private and require paid access)
Performance in Math	79.8% on the AIME math benchmark (best for math tasks)	79.2% on the AIME math benchmark
Performance in General	Best for specific tasks like math and coding	Great for multiple tasks (writing, translation, coding, etc.)
Speed	Very fast, optimized for quick responses	Fast but uses more resources to run
Best For	Solving math problems, coding tasks, and focused problem-solving	Writing, translating, and general creative or language-based tasks
How to Access It	Free and open to everyone	Paid access through APIs or commercial partnerships
Impact on AI Market	Challenged traditional AI by being open and cost-effective	A leader in AI with strong industry partnerships, like Microsoft
Ethics & Safety	Promotes transparency and encourages shared responsibility in AI development	Focuses on safety and controlled AI deployment
Who Should Use It	Great for developers, researchers, and startups	Best for large businesses and enterprises
Who They Work With	Supported by the open-source community	Partners with companies like Microsoft and uses Azure services
Innovation	Proves that AI can be high-quality yet low-cost	Creates cutting-edge, large-scale AI models

Best Large Language Models Courses

• Introduction to Large Language Models– Coursera
• Generative AI with Large Language Models– Coursera
• Large Language Models (LLMs) Concepts– DataCamp
• Prompt Engineering for ChatGPT– Vanderbilt University
• Introduction to LLMs in Python– DataCamp
• ChatGPT Teach-Out– University of Michigan
• Large Language Models for Business– DataCamp
• Introduction to Large Language Models with Google Cloud– Udacity FREE Course
• Finetuning Large Language Models– Coursera
• LangChain with Python Bootcamp– Udemy

Conclusion

When comparing DeepSeek AI vs. OpenAI, both have their strengths, but they serve different purposes. DeepSeek AI is a great choice if you’re looking for an open-source, free AI that works well for math and coding. It’s perfect for developers, students, or anyone who needs fast and accessible tools without any cost.

On the other hand, OpenAI is more flexible and can handle many different tasks, from writing to translation. However, it comes with a price. If you need an AI that can do a lot of different things, OpenAI is the way to go.

So, when it comes to DeepSeek AI vs. OpenAI, it all comes down to what you need. If you’re looking for something free and focused, DeepSeek AI is a great choice. But if you need a more all-around AI, OpenAI is worth considering.

Thank YOU!

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden