DeepSeek-V3 vs GPT-4o vs Llama 3.3 70B – Let the Best AI Model Win

The evolution of AI language models has set new standards, especially in the coding and programming landscape. Leading the charge are DeepSeek-V3, GPT-4o, and Llama 3.3 70B, each offering unique advantages. In this blog, we will do an AI language model comparison, focusing on the architectures, parameters, coding capabilities, and practical use cases of GPT-4o and its two alternatives. Through a detailed analysis of DeepSeek-V3 vs GPT-4o vs Llama 3.3 70B, I will uncover which model is best suited for programming tasks and how these advancements are shaping the future of AI in 2025.

Model Architectures and Design

DeepSeek-V3 is an open-source AI model that excels in large language model benchmarks with its efficient Mixture-of-Experts (MoE) architecture. Llama 3.3 70B is impressive with its scalability and adaptability, making it a strong contender in AI model parameter comparison. Meanwhile, GPT-4o stands out with its extensive resources, giving its competitors a run for their money.

Now, let’s begin our comparison by understanding the design and architectures of the three models.

DeepSeek-V3

DeepSeek-V3 is an open-source Mixture-of-Experts (MoE) model with 671 billion parameters, activating 37 billion parameters per token. It leverages cutting-edge load balancing and multi-token prediction methods, trained on 14.8 trillion tokens. Achieving top-tier performance across multiple benchmarks, the model maintains training efficiency with a cost of only 2.788 million H800 GPU hours.

DeepSeek-V3 incorporates reasoning abilities from DeepSeek-R1 Lite and offers a 128K context window. Moreover, it can process a variety of input types, including text, structured data, and complex multimodal inputs, making it versatile for diverse use cases.

Also Read: Building AI Application with DeepSeek-V3

GPT-4o

GPT-4o is an advanced language model developed by OpenAI, featuring state-of-the-art architectural improvements. It is trained over a vast dataset of input tokens, making it highly capable across various tasks with impressive accuracy.

The model supports multimodal inputs and has enhanced reasoning abilities, providing versatility for numerous applications. With a context window of 128K tokens, it can generate up to 16,384 tokens per request and processes around 77.4 tokens per second. Released in August 2024, its knowledge extends up to October 2023, making it one of the most powerful and adaptable models on the market.

Llama 3.3 70B

The Meta Llama 3.3 70 B multilingual large language model (LLM) is an open-source, pre-trained, instruction-tuned generative model with 70 billion parameters. It is designed to be optimized for efficiency and scalability. It employs cutting-edge techniques to handle a broad range of tasks, trained on over 15 trillion tokens.

Llama 3.3 70B is an auto-regressive language model that uses an optimized transformer architecture. The model achieves remarkable performance on several benchmarks, keeping training costs minimal with optimized resource allocation.

Llama 3.3 70B supports a wide context window and incorporates advanced reasoning capabilities for nuanced and precise task handling. It is designed to process text-based inputs but can also handle structured data, offering flexibility in various applications.

DeepSeek-V3 vs GPT-4o vs Llama 3.3 70B: Model Evaluation

1. Model Overview

2. Pricing Comparison

3. Benchmark Comparison

You can find the results of their individual benchmark tests here:

• GPT-4o: https://github.com/openai/simple-evals?tab=readme-ov-file#benchmark-result
• Llama 3.3 70B: https://build.nvidia.com/meta/llama-3_3-70b-instruct/modelcard
• DeepSeek-V3: https://github.com/deepseek-ai/DeepSeek-V3

Comparison Insights

Speaking of pricing, GPT-4o is roughly 30x more expensive compared to DeepSeek-V3 for input and output tokens. Meanwhile, Llama 3.3 70B Instruct is roughly 1.5x more expensive compared to DeepSeek-V3 for input and output tokens.

DeepSeek-V3 excels in benchmarks that require structured task completion, such as MMLU (Massive Multitask Language Understanding), and HumanEval (Code Generation). However, it faces challenges in benchmarks like MATH (Mathematical Problem-Solving), where its performance is not as competitive. It also leads in GPQA (Generalized Passage Question Answering), outperforming the other models in this domain.

GPT-4o performs particularly well in HumanEval and MMLU, where it stands out with its ability to generate high-quality responses across various topics. While it shines in reasoning-based tasks, it struggles in specialized areas such as MATH and GPQA, where other models perform better.

Llama 3.3 70B shows its strengths in benchmarks like MATH and IFEval (Instruction Following Evaluation). It demonstrates superior problem-solving abilities and follows complex instructions more effectively than the other models. Its performance in other benchmarks like HumanEval and GPQA is less impressive than DeepSeek-V3 and GPT-4o.

DeepSeek-V3 vs GPT-4o vs Llama 3.3 70B: Coding Capabilities

Now we will compare the code writing capabilities of DeepSeek-V3, GPT-4o, and Llama 3.3 70B. For that, we are going to give the same prompt to all three models and evaluate their responses on the below metrics:

1. Efficiency
2. Readability
3. Comment and Documentation
4. Error Handling

Task 1: Finding the Factorial of a Large Number

Prompt: “Hi, you are an excellent code instructor so help me with this question.
Q. Write Python code for finding the Factorial of a Large Number.”

Response by GPT-4o

To compute the factorial of a large number using an array (to manually handle multiplication and carry operations), here’s a Python implementation:

def multiply(arr, x):
   """
   Multiplies an array representation of a number with an integer x.


   : param arr: List of integers representing the digits of the number.
   : param x: Integer to multiply with.
   :return: Updated array with the result.
   """
   carry = 0  # Initialize carry
   for i in range(len(arr)):
       prod = arr[i] * x + carry
       arr[i] = prod % 10  # Store last digit of the product in the array
       carry = prod // 10  # Update the carry
   # Process carry, add digits to the array
   while carry > 0:
       arr.append(carry % 10)
       carry //= 10
   return arr


def factorial_large_number_array(n):
   """
   Calculate the factorial of a large number using array representation.


   : param n: Integer for which the factorial is to be computed.
   :return: Factorial of n as a string.
   """
   if n < 0:
       return "Factorial is not defined for negative numbers."
   if n == 0 or n == 1:
       return "1"
  
   arr = [1]  # Initialize the array with the first factorial value (1)
   for x in range(2, n + 1):
       arr = multiply(arr, x)
  
   # The result is stored in reverse order in the array
   return ''.join(map(str, reversed(arr)))
# Input a large number
try:
   number = int(input("Enter a large number to calculate its factorial: "))
   result = factorial_large_number_array(number)
   print(f"The factorial of {number} is:\n{result}")
except ValueError:
   print("Invalid input! Please enter a valid integer.")

Output of the Generated Code

Response by Llama 3.3 70B

Here’s a Python code snippet that calculates the factorial of a large number using an array to store the digits.

def multiply(x, res, res_size):
   """Multiplies x with the number represented by res[]"""
   carry = 0
   for i in range(res_size):
       prod = res[i] * x + carry
       res[i] = prod % 10
       carry = prod // 10
   while carry:
       res.append(carry % 10)
       carry = carry // 10


def factorial(n):
   """ Calculates the factorial of a number n"""
   res = [1]
   res_size = 1
   for i in range(2, n + 1):
       multiply(i, res, res_size)
       res_size = len(res)
   return res
def print_factorial(n):
   """ Prints the factorial of a number n"""
   res = factorial(n)
   print("Factorial of", n, "is:")
   for digit in reversed(res):
       print(digit, end="")
# Example usage:
n = 55
print_factorial(n)

Output of the Generated Code

Response by DeepSeek-V3

To calculate the factorial of a large number using an array in Python, we can store the result in an array (or list) to handle very large numbers that exceed the limit of standard data types. Here’s how you can implement it:

def factorial(n):
   # Initialize the result as a list with 1
   result = [1]


   # Multiply the result list with numbers from 2 to n
   for x in range(2, n + 1):
       carry = 0
       # Multiply each digit in the result list with x
       for i in range(len(result)):
           product = result[i] * x + carry
           result[i] = product % 10  # Store the last digit
           carry = product // 10     # Carry over the remaining digits
       # Add the remaining carry to the result list
       while carry > 0:
           result.append(carry % 10)
           carry = carry // 10


   # Reverse the result list to get the correct order
   result.reverse()
   return result


# Input: Number to find factorial of
n = int(input("Enter a number: "))
# Compute factorial
factorial_result = factorial(n)
# Convert the list of digits to a string for display
factorial_str = ''.join(map(str, factorial_result))
# Output the result
print(f"Factorial of {n} is: {factorial_str}")

Output of the Generated Code

Comparison Results

GPT-4o’s response is the most complete, balancing efficiency, readability, and robust error handling. It includes detailed comments and clear variable names, and handles invalid input gracefully, making it well-suited for practical use or collaborative development.

Llama 3.3 70B’s response is functional, but it lacks proper error handling and detailed documentation. Its structure is less intuitive, and the use of redundant variables impacts the efficiency. This makes it the weakest of the three.

DeepSeek-V3’s response is efficient and cleanly structured but lacks robust error handling and sufficient documentation. It is a strong contender but falls short in clarity for people to follow or adapt easily.

Recommendations:

• For practical use: GPT-4o is the best choice due to its robust error handling, clear documentation, and strong readability.
• For educational purposes: GPT-4o would again be the preferred option due to the detailed docstrings and comments in its response, which can help learners understand things logically.

Task 2: Checking if a String is a Palindrome

Prompt: ‘‘Hi, you are an excellent code instructor so help me with this question.
Q. Given a string s, return true if the s can be palindrome after deleting at most one character from it.’’

Response by GPT-4o

To solve this problem, we can use a two-pointer approach to check if the string can be a palindrome after removing at most one character. Here’s the Python implementation

def valid_palindrome(s):
   """
   Check if the string can become a palindrome by removing at most one character.


   : param s: The input string.
   :return: True if the string can become a palindrome, otherwise False.
   """
   def is_palindrome_range(left, right):
       # Helper function to check if a substring is a palindrome
       while left < right:
           if s[left] != s[right]:
               return False
           left += 1
           right -= 1
       return True
   left, right = 0, len(s) - 1
   while left < right:
       if s[left] != s[right]:
           # If a mismatch occurs, check by skipping one character
           return is_palindrome_range(left + 1, right) or is_palindrome_range(left, right - 1)
       left += 1
       right -= 1
   return True
# Example Usage
s1 = "abca"
print(f"Input: {s1}\nOutput: {valid_palindrome(s1)}") 
s2 = "defgd"
print(f"Input: {s2}\nOutput: {valid_palindrome(s2)}") 

Output of the Generated Code

Response by Llama 3.3 70B

Here’s a Python solution that checks if a string can be a palindrome after deleting at most one character.

def valid_palindrome(s: str) -> bool:
   """
   Returns True if the string can be a palindrome after deleting at most one character.
  
   :param s: The input string.
   :return: True if the string can be a palindrome, False otherwise.
   """
   def is_palindrome(i, j):
       """
       Checks if the substring from index i to j is a palindrome.
       :param i: The start index.
       :param j: The end index.
       :return: True if the substring is a palindrome, False otherwise."""
       while i < j:
           if s[i] != s[j]:
               return False
           i += 1
           j -= 1
       return True


   i, j = 0, len(s) - 1
   while i < j:
       if s[i] != s[j]:
           # If a mismatch is found, try deleting one character
           return is_palindrome(i + 1, j) or is_palindrome(i, j - 1)
       i += 1
       j -= 1
   return True
print(valid_palindrome("abca"))
print(valid_palindrome("defgd"))  

Output of the Generated Code

Response by DeepSeek-V3

To solve this problem, we need to check if a string can become a palindrome by deleting at most one character. A palindrome reads the same forwards and backward. Here’s how we can approach this:

def validPalindrome(s: str) -> bool:
   def is_palindrome(s, left, right):       # Helper function to check if a substring is a palindrome
       while left < right:
           if s[left] != s[right]:
               return False
           left += 1
           right -= 1
       return True
   left = 0
   right = len(s) - 1
   while left < right:
       if s[left] != s[right]:
           # Try deleting either the left or the right character
           return is_palindrome(s, left + 1, right) or is_palindrome(s, left, right - 1)
       left += 1
       right -= 1
   return True
# Example usage:
s1 = "abca"
print(validPalindrome(s1)) 
s2 = "defgd"
print(validPalindrome(s2)) 

Output of the Generated Code

Comparison Insights

GPT-4o’s response is the most complete and well-documented. It handles the core functionality with clarity, making it easy for future developers to modify or extend the code. Its combination of efficiency and clear documentation makes it ideal for production environments.

Llama 3.3 70B’s response is a functional solution but lacks the clear variable naming and in-depth documentation found in GPT-4o. The lack of comments within the main logic makes it harder to follow, and there is room for improvement in terms of readability. However, it is efficient enough for small tasks where quick implementation is the priority.

DeepSeek-V3’s response strikes a good balance between efficiency and simplicity but falls short in documentation. It’s concise and quick but lacks enough detail for others to follow the code easily. Its approach can be beneficial in scenarios where time and resources are limited, but it would need more thorough explanations and error handling to make the code production-ready.

Recommendations:

• For practical use: GPT-4o  response is the best due to its thorough documentation, clear structure, and readability. 
• For educational purposes: GPT-4o  is the most suitable, providing comprehensive insights into each step of the process.

Conclusion

GPT-4o outperforms both Llama 3.3 70B and DeepSeek-V3 in terms of efficiency, clarity, error management, and comprehensive documentation. This makes it the top choice for both practical applications and educational purposes. While Llama 3.3 70B and DeepSeek-V3 are functional, they fall short due to the lack of robust error handling and clear documentation. Adding proper error management, improving variable naming, and including detailed comments would elevate their usability to match GPT-4o’s standard.

Also Read:

• DeepSeek R1 vs OpenAI o1: Which One is Better?
• DeepSeek R1 vs OpenAI o1 vs Sonnet 3.5
• Chinese Giants Faceoff: DeepSeek-V3 vs Qwen2.5
• DeepSeek V3 vs Claude Sonnet 3.5
• DeepSeek V3 vs GPT-4o

Frequently Asked Questions

Vipin Vashisth

Hello! I'm Vipin, a passionate data science and machine learning enthusiast with a strong foundation in data analysis, machine learning algorithms, and programming. I have hands-on experience in building models, managing messy data, and solving real-world problems. My goal is to apply data-driven insights to create practical solutions that drive results. I'm eager to contribute my skills in a collaborative environment while continuing to learn and grow in the fields of Data Science, Machine Learning, and NLP.