DeepSeek V3 vs Claude 3.7: Is V3-0324 Better at Coding?

As AI models advance, their programming and software development capabilities have become key benchmarks. Two leading contenders in the coding scene are DeepSeek V3 and Claude 3.7. DeepSeek V3-0324, the latest from DeepSeek AI, comes with promising benchmark results on coding tasks. Meanwhile, Anthropic’s newest model, Claude 3.7, is a stronger generalist AI with superior contextual understanding and coding capabilities. In this DeepSeek V3 vs Claude 3.7 comparison, we’ll be looking into the performance of both these models across standard benchmarks as well as real-life applications, to find the ideal coding assistant.

Table of Contents

• What is DeepSeek V3-0324?
• How to Access DeepSeek V3-0324
• DeepSeek V3-0324 vs Claude 3.7: Performance-based Comparison
• Overall Analysis
• DeepSeek V3-0324 vs Claude 3.7: Benchmark Comparison
• Conclusion
• Frequently Asked Questions

What is DeepSeek V3-0324?

DeepSeek V3-0324 is the latest AI model from DeepSeek AI, designed to deliver high performance in coding and reasoning tasks. This model builds on the success of its predecessors, incorporating advancements in efficiency, and usability. With an emphasis on improving both developer experience and accessibility. DeepSeek V3-0324 is positioned as a powerful tool for AI-driven applications.

Key Features of DeepSeek V3-0324

Here are some of the key features of DeepSeek ’s latest model:

• Streamlined User Interface: The model features an improved interface across DeepSeek’s official platforms, including the website, mobile app, and mini-program.
• Consistent API Integration: Developers benefit from unchanged API interfaces and usage methods, ensuring seamless integration with existing systems.
• Open-Source Accessibility: DeepSeek V3-0324 is available under the MIT license, promoting transparency and collaboration within the AI community. The model weights and technical report can be accessed on platforms like Hugging Face.

How to AccessDeepSeek V3-0324

Below are the two primary methods for accessing DeepSeek V3-0324.

1. Accessing through API

For people looking to incorporate DeepSeek V3-0324 into their applications, the API provides a flexible and efficient solution. The API structure remains unchanged from previous versions, ensuring an easy transition for existing users.

Here’s how to access DeepSeek V3-0324 via API:

1. Sign Up for an API Key

   Visit DeepSeek AI’s official website and create an account. Once registered, navigate to the developer section and request an API key. This key will be required to authenticate requests.
   

   

2. Send Requests to the API

   Once you have the API key, you can start making requests. Use the designated model name, such as model=’deepseek-chat’. Ensure your API request follows DeepSeek’s documentation, including proper authentication headers and request formatting.
   

   

No Modifications Needed for Existing Users: If you have previously integrated DeepSeek’s API, no changes are required in your setup. Simply update the model reference to DeepSeek V3-0324 if there is any change in it, and your application will continue functioning seamlessly.

2. Accessing through DeepSeek’s Official Platforms

If you prefer direct access without API integration, DeepSeek V3-0324 is available through DeepSeek’s official platforms. These platforms provide an interactive interface, allowing users to test the model’s capabilities in real-time.

To access DeepSeek V3-0324 from their official website follow this:

1. Open your browser and go to the official DeepSeek AI website.
2. If you already have an account, log in using your credentials. New users can sign up by providing an email address and setting up a password.
3. You can now enter prompts and interact with DeepSeek V3-0324 directly. Adjust settings such as response length

DeepSeek V3-0324 vs Claude 3.7: Performance-based Comparison

Now let’s start with the DeepSeek V3-0324 vs Claude 3.7 comparison. To evaluate the programming capabilities of DeepSeek V3-0324 and Claude 3.7, we will test them on different coding tasks. These tasks will help us assess how well each model performs across varying levels of complexity.

The comparison will be based on four key metrics:

• Code Quality
• Efficiency
• Error Handling
• Documentation

By analyzing these aspects, we aim to determine which model is a more reliable and effective coding assistant across different programming challenges.

Task 1: Aggressive Cows

Prompt: “You are given an array with unique elements of stalls[ ], which denote the position of a stall. You are also given an integer k which denotes the number of aggressive cows. Your task is to assign stalls to k cows such that the minimum distance between any two of them is the maximum possible.”

Response by DeepSeek V3-0324

def aggressive_cows(stalls, k):
   # Sort the stalls to easily calculate distances between them
   stalls.sort()
   n = len(stalls)
   # Initialize the binary search bounds
   left = 0  # Minimum possible distance
   right = stalls[-1] - stalls[0]  # Maximum possible distance
   best = 0  # To store the maximum minimum distance found
  
   # Binary search to find the maximum minimum distance
   while left = mid:
               count  = 1
               last_position = stalls[i]
               # Early exit if all cows are placed
               if count == k:
                   break
       # If we can place all cows with 'mid' distance, try for a larger distance
       if count >= k:
           best = mid
           left = mid   1
       # Otherwise, try for a smaller distance
       else:
           right = mid - 1
  
   return best
# Test Case 1:
stalls1 = [1, 2, 4, 8, 9]
k1 = 3
print("Maximum minimum distance:", aggressive_cows(stalls1, k1))  
print()
# Test Case 2:
stalls2 = [10, 1, 2, 7, 5]
k2 = 3
print("Maximum minimum distance:", aggressive_cows(stalls2, k2)) 
print()
# Test Case 3:
stalls3 = [2, 12, 11, 3, 26, 7]
k3 = 5
print("Maximum minimum distance:", aggressive_cows(stalls3, k3)) 

DeepSeek V3-0324 Output

Response by Claude 3.7

def is_possible_distance(stalls, k, min_distance):
   """
   Check if it's possible to place k cows with at least min_distance between them
  
   Args:
   stalls (list): Sorted list of stall positions
   k (int): Number of cows to place
   min_distance (int): Minimum distance to check between cows
  
   Returns:
   bool: True if possible to place cows, False otherwise
   """
   # Place the first cow at the first stall
   cows_placed = 1
   last_cow_position = stalls[0]
  
   # Try to place remaining cows
   for i in range(1, len(stalls)):
       # If distance between current stall and last cow's position is
       # at least min_distance, place a cow here
       if stalls[i] - last_cow_position >= min_distance:
           cows_placed  = 1
           last_cow_position = stalls[i]
          
           # If we've placed all k cows, return True
           if cows_placed == k:
               return True
  
   # Couldn't place all k cows
   return False


def aggressive_cows(stalls, k):
   """
   Find the maximum possible minimum distance between k cows
  
   Args:
   stalls (list): List of stall positions
   k (int): Number of cows
  
   Returns:
   int: Maximum possible minimum distance between cows
   """
   # Sort the stalls
   stalls.sort()
  
   # Binary search range is from 1 to max possible distance
   left = 1
   right = stalls[-1] - stalls[0]
   result = 0
  
   # Binary search to find maximum minimum distance
   while left 



<p><b>Claude Output 3.7</b></p>



<p><img  src="/static/imghw/default1.png" data-src="https://img.php.cn/upload/article/000/000/000/174546332060339.jpg" class="lazy" alt="DeepSeek V3 vs Claude 3.7: Is V3-0324 Better at Coding?" ></p>



<h4 id="Comparative-Analysis">Comparative Analysis</h4>



<p>Claude 3.7’s code is more modular, readable, and well-documented with clear function docstrings. It avoids unnecessary binary search iterations, improving efficiency. DeepSeek V3-0324 is slightly more optimized with an early exit but lacks error handling. Claude’s structured approach makes adding validation easier, though both lack explicit checks. Overall, Claude 3.7 wins for maintainability, while DeepSeek V3-0324 is marginally faster in execution.</p>



<p><strong>Score:</strong> DeepSeek V3-0324: 0 | Claude 3.7: 1</p>



<p><em>Also Read: Building AI Application with DeepSeek-V3</em></p>



<h3 id="Task-Animation-of-Boat-on-Water">Task 2: Animation of Boat on Water</h3>



<p><strong>Prompt:</strong> <em>“Generate an HTML and CSS code snippet that creates an animated scene of a boat moving smoothly across the water. The water should have realistic ripples around the boat to enhance the visual effect. Use CSS animations for fluid movement and ripple effects. Ensure the design is visually appealing and runs smoothly on modern browsers.”</em></p>



<h4 id="Response-by-DeepSeek-V">Response by DeepSeek V3-0324</h4>



<pre class="brush:php;toolbar:false">


   <meta charset="UTF-8">
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   <title>Animated Boat on Water</title>
   <style>
       body {
           margin: 0;
           overflow: hidden;
           background: linear-gradient(to bottom, #87CEEB 0%, #1E90FF 100%);
           height: 100vh;
           display: flex;
           justify-content: center;
           align-items: center;
       }


       .scene {
           position: relative;
           width: 100%;
           height: 300px;
           overflow: hidden;
       }


       .water {
           position: absolute;
           bottom: 0;
           width: 100%;
           height: 100px;
           background: linear-gradient(to bottom, #1E90FF 0%, #00BFFF 50%, #1E90FF 100%);
       }</style>

DeepSeek V3-0324 Output

   <meta charset="UTF-8">
   <title>Boat on Waves Animation</title>
   <style>
       body, html {
           margin: 0;
           height: 100%;
           overflow: hidden;
           background: linear-gradient(to bottom, #87CEEB, #4682B4);
           display: flex;
           justify-content: center;
           align-items: center;
       }


       .ocean {
           width: 100%;
           height: 40%;
           position: absolute;
           bottom: 0;
           left: 0;
           background: linear-gradient(to bottom, rgba(65,105,225,0.7), rgba(30,144,255,0.9));
           overflow: hidden;
       }</style>

Claude3.7’s Output

Comparative Analysis

DeepSeek V3-0324’s boat floats perfectly with better graphics, creating a visually appealing and realistic scene. Claude’s boat appears upside down, reducing realism. DeepSeek V3-0324’s ripples enhance water dynamics, while Claude’s animation lacks fluidity. Both run smoothly, but DeepSeek V3-0324’s design is more polished and visually engaging. Overall, DeepSeek V3-0324 provides superior aesthetics and accurate movement, making it the better model for animation.

Score: DeepSeek V3-0324: 1 | Claude 3.7: 1

Task 3: Classic Snake Game in Python

Prompt: “Generate a Python script for the classic Snake game using the pygame library.”

Response by DeepSeek V3-0324

def game_loop():
   """Main game loop."""
   game_over = False
   game_close = False


   # Initial snake position
   x1 = dis_width / 2
   y1 = dis_height / 2


   # Snake movement
   x1_change = 0
   y1_change = 0


   # Snake body
   snake_list = []
   length_of_snake = 1


   # Food position
   foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0
   foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0


   while not game_over:


       while game_close:
           # Game over screen
           dis.fill(white)
           message("You Lost! Press Q-Quit or C-Play Again", red)
           your_score(length_of_snake - 1)
           pygame.display.update()


           # Check for player input after game over
           for event in pygame.event.get():
               if event.type == pygame.KEYDOWN:
                   if event.key == pygame.K_q:
                       game_over = True
                       game_close = False
                   if event.key == pygame.K_c:
                       game_loop()


       # Handle keyboard events
       for event in pygame.event.get():
           if event.type == pygame.QUIT:
               game_over = True
           if event.type == pygame.KEYDOWN:
               if event.key == pygame.K_LEFT:
                   x1_change = -snake_block
                   y1_change = 0
               elif event.key == pygame.K_RIGHT:
                   x1_change = snake_block
                   y1_change = 0
               elif event.key == pygame.K_UP:
                   y1_change = -snake_block
                   x1_change = 0
               elif event.key == pygame.K_DOWN:
                   y1_change = snake_block
                   x1_change = 0


       # Check for boundary collision
       if x1 >= dis_width or x1 = dis_height or y1  length_of_snake:
           del snake_list[0]


       # Check for self-collision
       for x in snake_list[:-1]:
           if x == snake_head:
               game_close = True


       # Draw snake and score
       our_snake(snake_block, snake_list)
       your_score(length_of_snake - 1)


       pygame.display.update()


       # Check if snake ate food
       if x1 == foodx and y1 == foody:
           foodx = round(random.randrange(0, dis_width - snake_block) / 10.0) * 10.0
           foody = round(random.randrange(0, dis_height - snake_block) / 10.0) * 10.0
           length_of_snake  = 1


       # Control game speed
       clock.tick(snake_speed)


   # Quit pygame
   pygame.quit()
   quit()


# Start the game
game_loop()

DeepSeek V3-0324 Output

Response by Claude

def main():
   # Initialize game objects
   snake = Snake()
   food = Food(snake)
   score = 0


   # Font for displaying score
   font = pygame.font.Font(None, 36)


   # Game loop
   running = True
   while running:
       # Event handling
       for event in pygame.event.get():
           if event.type == pygame.QUIT:
               running = False
          
           # Handle keyboard input for snake direction
           if event.type == pygame.KEYDOWN:
               if event.key == pygame.K_UP and snake.direction != (0, 1):
                   snake.direction = (0, -1)
               elif event.key == pygame.K_DOWN and snake.direction != (0, -1):
                   snake.direction = (0, 1)
               elif event.key == pygame.K_LEFT and snake.direction != (1, 0):
                   snake.direction = (-1, 0)
               elif event.key == pygame.K_RIGHT and snake.direction != (-1, 0):
                   snake.direction = (1, 0)


       # Move the snake
       snake.move()


       # Check for food collision
       if snake.body[0] == food.position:
           snake.grow = True
           food.position = food.generate_position(snake)
           score  = 1


       # Check for self-collision
       if snake.check_collision():
           running = False


       # Clear the screen
       screen.fill(BLACK)


       # Draw game objects
       snake.draw(screen)
       food.draw(screen)


       # Render score
       score_text = font.render(f'Score: {score}', True, WHITE)
       screen.blit(score_text, (10, 10))


       # Update display
       pygame.display.flip()


       # Control game speed
       clock.tick(10)  # 10 frames per second


   # Game over screen
   screen.fill(BLACK)
   game_over_text = font.render('Game Over', True, WHITE)
   final_score_text = font.render(f'Final Score: {score}', True, WHITE)
   screen.blit(game_over_text, (WIDTH//2 - game_over_text.get_width()//2, HEIGHT//2 - 50))
   screen.blit(final_score_text, (WIDTH//2 - final_score_text.get_width()//2, HEIGHT//2   50))
   pygame.display.flip()


   # Wait for a moment before closing
   pygame.time.wait(2000)


   # Quit Pygame
   pygame.quit()


# Run the game
if __name__ == '__main__':
   main()

Claude Output

Comparative Analysis

DeepSeek V3-0324’s Snake game correctly ends on border collision but requires pressing a key to start, which adds minor friction. Claude’s version fails to detect border collisions, reducing the accuracy of gameplay. DeepSeek V3-0324’s game auto-ends on simultaneously pressing the left and right keys, which can be frustrating. Overall, Claude 3.7 ensures better game logic, while DeepSeekV3-0324 requires fixes for a complete experience.

Score: DeepSeek V3-0324: 1 | Claude 3.7: 2

Final Score: DeepSeek V3-0324: 1 | Claude 3.7: 2

Code Files: For the complete version of the code files please click here.

Overall Analysis

Metric	Task 1: Aggressive Cows	Task 2: Boat Animation	Task 3: Snake Game	Overall Verdict
Code Quality	Claude 3.7 is more modular and well-documented.	DeepSeek V3-0324 has better visuals and structure.	DeepSeek-V3 has better collision handling.	DeepSeek V3-0324 excels in execution, and Claude 3.7 in readability.
Efficiency	DeepSeek V3-0324 is slightly faster with an early exit.	Both run smoothly, but DeepSeek V3-0324’s animation is more polished.	DeepSeek V3-0324 handles border collisions well.	DeepSeek V3-0324 offers better optimization.
Error Handling	Claude 3.7 lacks explicit checks but is easier to validate.	Claude’s boat is upside down, reducing realism.	Claude 3.7 fails to detect border collisions.	Claude 3.7 requires fixes for execution issues.
Visual Appeal	Not applicable.	DeepSeek V3-0324 has better graphics and ripple effects.	Not applicable.	DeepSeek V3-0324 leads in aesthetics.
Execution Accuracy	Both work correctly, but DeepSeek V3-0324 lacks validation.	DeepSeek V3-0324’s boat floats perfectly, while Claude 3.7’s is misaligned.	DeepSeek’s game ends correctly; Claude 3.7 does not detect border collisions.	DeepSeek V3-0324 provides better functional correctness.
Final Verdict	Claude 3.7 is better for maintainability, and DeepSeek V3-0324 for speed.	DeepSeek’s output is visually superior and well-executed.	DeepSeek V3-0324 ensures better game logic, while Claude 3.7 has major flaws.	DeepSeek-V3 wins overall with better execution and visuals.

Also Read: DeepSeek V3 vs GPT-4o: Which is Better?

DeepSeek V3-0324 vs Claude 3.7: Benchmark Comparison

Now, let’s get into the benchmark results of both these models.

1. Aider Polyglot Benchmark

This benchmark measures the LLM’s coding ability in popular languages, and whether it can write new code that integrates into existing code.

DeepSeek V3-0324 achieved around 55% accuracy in “Diff-like format”, at a moderate cost. Meanwhile, Claude 3.7 (32K thinking tokens) achieved around 65-67% accuracy, which is the highest among the models tested. Even Claude 3.7 (No thinking) achieved around 60% accuracy. However, these models come at a higher cost compared to DeepSeek V3-0324.

2. Chatbot Arena Leaderboard

Model	Arena Score	Rank (UB)	Rank (StyleCtrl)	Votes	Organization	License
DeepSeek V3-0324	1318	12	12	22,848	DeepSeek	DeepSeek
Claude 3.7 (Thinking-32K)	1302	14	3	4,487	Anthropic	Proprietary

Benchmark Insights:

• DeepSeek V3-0324 seems to be a stronger general-purpose model based on Arena scores and ranking.
• Claude 3.7 (Thinking-32K) might be better in structured or style-based outputs, given its high StyleCtrl ranking.
• DeepSeek V3-0324 has broader adoption, given its much higher vote count.

3. Other Benchmarks

Here’s a breakdown of the benchmark results for DeepSeek-V3 0324 and Claude-Sonnet 3.7:

MMLU-Pro (Exact Match – EM):

DeepSeek V3 0324: 81.2% | Claude 3.7 Sonnet: 75.9%

Analysis: DeepSeek V3 0324 shows a stronger ability to handle multitask language understanding and complex reasoning, with a higher score than Claude 3.7 Sonnet.

GPQA Diamond (Pass@1):

DeepSeek V3 0324: 86.1% | Claude 3.7 Sonnet: 80.7%

Analysis: DeepSeek V3 0324 again outperforms Claude 3.7 Sonnet in answering complex general knowledge and reasoning questions, showcasing better question-answering capabilities.

MATH-500 (Pass@1):

DeepSeek V3 0324: 68.4% | Claude 3.7 Sonnet: 60.1%

Analysis: DeepSeek V3 0324 has a higher score in mathematical problem-solving, indicating stronger proficiency in solving a wide range of math-related tasks compared to Claude 3.7 Sonnet.

AIME 2024 (Pass@1):

DeepSeek V3 0324: 94.0% | Claude 3.7 Sonnet: 82.2%

Analysis: DeepSeek V3 0324 excels in advanced reasoning tasks, significantly outperforming Claude 3.7 Sonnet in more complex, multi-step tasks.

LiveCodeBench (Pass@1):

DeepSeek V3 0324: 90.2% | Claude 3.7 Sonnet: 82.6%

Analysis: DeepSeek V3 0324 scores higher in coding and software development tasks, reflecting its better understanding of real-time programming, debugging, and coding challenges.

Conclusion

DeepSeek V3-0324 stands out with its accuracy, efficiency, and strong execution across multiple tasks. It ensures correct functionality in coding challenges, animations, and gameplay logic, making it reliable for real-world applications. Claude 3.7, while well-structured and readable, struggles with execution flaws that impact usability. DeepSeek V3-0324’s superior optimization and polished output make it a strong choice for developers who prioritize performance and correctness. Meanwhile Claude 3.7 remains useful for those who value clean and maintainable code.

Frequently Asked Questions

Q1. Which model performs better for coding tasks, DeepSeek V3-0324 or Claude 3.7?

A. DeepSeek V3-0324 generally delivers more accurate and efficient code execution, while Claude 3.7 focuses on better structure and documentation.

Q2. Which model is better for animations and graphics-based tasks?

A. DeepSeek V3-0324 produces more polished and visually appealing animations, as seen in the boat animation task.

Q3. Which model is better for beginners in programming?

A. Claude 3.7 is better for beginners due to its well-structured code and clear documentation, making it easier to understand and modify.

Q4. Which model is better suited for real-world projects?

A. DeepSeek V3-0324 is better for real-world applications due to its superior execution accuracy, but Claude 3.7 is valuable for structured development.

Q5. How do these models compare in terms of efficiency and speed?

A. DeepSeek V3-0324 generally executes tasks faster and with fewer iterations, while Claude 3.7 sometimes introduces unnecessary steps that slow down performance.

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