DeepSeek R1 vs Claude 3.5: Contrasting Strengths and Use Cases

Anthropic’s Claude 3.5 Sonnet and DeepSeek’s R1 have emerged as key players in the rapidly evolving field of artificial intelligence. Released at different times, both models demonstrate advanced capabilities and have gained significant attention for their unique features and performance attributes.

DeepSeek R1 vs Claude 3.5: Basic Introduction

Deepseek R1

• DeepSeek R1 is purpose-built for tasks that demand advanced reasoning and programming assistance. It leverages a Mixture-of-Experts (MoE) architecture, activating only a subset of its vast parameters for each token, thereby optimizing computational efficiency. Trained through large-scale reinforcement learning (RL) with minimal supervised fine-tuning (SFT), DeepSeek R1 places a strong emphasis on logic and problem-solving capabilities.

Claude 3.5 Sonnet

• Claude 3.5 Sonnet, Anthropic’s most advanced model, combines exceptional performance with enhanced speed. It features a large context window and excels at understanding nuanced and complex instructions. As part of the Claude 3.5 model family, it delivers significant improvements over its predecessors, particularly in areas such as coding and tool utilization.

You can start a free trail on Novita AI for Deepseek R1 series!

• deepseek/deepseek-r1（671b）
• deepseek/deepseek-r1-turbo with 3x throughput and a limited-time 60% discount
• deepseek/deepseek-r1-distill-llama-8b
• deepseek/deepseek-r1-distill-qwen-14b
• deepseek/deepseek-r1-distill-qwen-32b
• deepseek/deepseek-r1-distill-llama-70b

DeepSeek R1 vs Claude 3.5: Benchmark

Deepseek R1

DeepSeek R1 excels in programming, debugging, and advanced mathematical reasoning, making it ideal for technical and logic-heavy tasks. Its strong performance in benchmarks like Codeforces, AIME, and debugging accuracy highlights its capabilities in these areas.

Claude 3.5 Sonnet

Claude 3.5 Sonnet, while weaker in programming and math, performs well in language comprehension and general-purpose knowledge tasks, making it better suited for multilingual and conversational applications.

DeepSeek R1 vs Claude 3.5: Speed and Cost

Speed Comparison of Deepseek R1 and Claude 3.5

Cost Comparison of Deepseek R1 and Claude 3.5

Claude offers superior performance metrics (faster output speed and lower latency) but at a considerably higher price point. DeepSeek R1 is more economical but slower in response and generation. The choice between them would depend on whether speed and responsiveness or cost-efficiency is the higher priority for a specific use case.

However, Novita AI launches a Turbo version with 3x throughput and a limited-time 60% discount!

DeepSeek R1 Vs Claude 3.5: Tasks

Task 1: Logical Reasoning

Prompt: “You walk into a room and see a bed. On the bed there are two dogs, four cats, a giraffe, five cows, and a duck. There are also three chairs and a table. How many legs are on the floor?”

Deepseek R1 Result

Claude 3.5 Result

Review:

• Reasoning depth: DeepSeek R1 demonstrates a deeper, more thorough reasoning process, considering all aspects of the problem.
• Accuracy: DeepSeek R1 ultimately arrives at the correct answer (22), while Claude 3.5 incorrectly concludes 20.
• Self-verification capability: DeepSeek R1 continuously reviews and checks its reasoning, whereas Claude 3.5 lacks this self-verification mechanism.
• Ambiguity handling: DeepSeek R1 is able to address ambiguities in the problem (such as whether the bed has legs), while Claude 3.5 makes simple assumptions without explanation.
• Transparency of thought: DeepSeek R1’s thinking process is more transparent, allowing people to understand its reasoning path.

Task 2: Scientific Reasoning

Prompt: “You have a powerful laser and a perfectly reflective mirror. How can you aim the laser at the mirror in such a way that the reflected beam never comes back to you?”

Deepseek R1 Result

Claude 3.5 Result

Review:

• DeepSeek R1 demonstrates significantly stronger scientific reasoning:
  • Physical Accuracy: DeepSeek R1’s answer is physically correct and follows established optical principles, while Claude 3.5’s answer contains fundamental misconceptions.
  • Logical Consistency: DeepSeek R1 maintains logical consistency throughout its explanation, while Claude 3.5’s response contains internal contradictions.
  • Problem Solving: DeepSeek R1 correctly solves the problem by providing a valid range of solutions, while Claude 3.5 proposes an impossible solution.
  • Scientific Literacy: DeepSeek R1 demonstrates proper understanding of the law of reflection, while Claude 3.5 appears to confuse basic optical concepts.

Task 3: Problem-Solving Skills

Prompt: “Write a program that can solve a Sudoku puzzle”

Deepseek R1 Result

def find_empty(grid):
    for row in range(9):
        for col in range(9):
            if grid[row][col] == 0:
                return (row, col)  # Return empty cell position
    return None  # No empty cells left

def is_valid(grid, num, pos):
    row, col = pos
    
    # Check row
    for i in range(9):
        if grid[row][i] == num and i != col:
            return False
    
    # Check column
    for i in range(9):
        if grid[i][col] == num and i != row:
            return False
    
    # Check 3x3 subgrid
    start_row = (row // 3) * 3
    start_col = (col // 3) * 3
    for i in range(start_row, start_row + 3):
        for j in range(start_col, start_col + 3):
            if grid[i][j] == num and (i, j) != pos:
                return False
    return True

def solve(grid):
    empty = find_empty(grid)
    if not empty:
        return True  # Puzzle solved
    
    row, col = empty
    for num in range(1, 10):
        if is_valid(grid, num, (row, col)):
            grid[row][col] = num  # Place number
            
            if solve(grid):
                return True  # Continue solving
            
            grid[row][col] = 0  # Backtrack if invalid
    
    return False  # Trigger backtracking

def print_grid(grid):
    for row in grid:
        print(row)

# Example Sudoku puzzle (0 represents empty cells)
grid = [
    [5,3,0,0,7,0,0,0,0],
    [6,0,0,1,9,5,0,0,0],
    [0,9,8,0,0,0,0,6,0],
    [8,0,0,0,6,0,0,0,3],
    [4,0,0,8,0,3,0,0,1],
    [7,0,0,0,2,0,0,0,6],
    [0,6,0,0,0,0,2,8,0],
    [0,0,0,4,1,9,0,0,5],
    [0,0,0,0,8,0,0,7,9]
]

if solve(grid):
    print("Solved Sudoku:")
    print_grid(grid)
else:
    print("No solution exists")

Claude 3.5 Result

def print_board(board):
    for i in range(9):
        if i % 3 == 0 and i != 0:
            print("- - - - - - - - - - - -")
        for j in range(9):
            if j % 3 == 0 and j != 0:
                print("|", end=" ")
            if j == 8:
                print(board[i][j])
            else:
                print(str(board[i][j]) + " ", end="")

def find_empty(board):
    for i in range(9):
        for j in range(9):
            if board[i][j] == 0:
                return (i, j)
    return None

def valid(board, num, pos):
    # Check row
    for j in range(9):
        if board[pos[0]][j] == num and pos[1] != j:
            return False

    # Check column
    for i in range(9):
        if board[i][pos[1]] == num and pos[0] != i:
            return False

    # Check box
    box_x = pos[1] // 3
    box_y = pos[0] // 3

    for i in range(box_y * 3, box_y * 3 + 3):
        for j in range(box_x * 3, box_x * 3 + 3):
            if board[i][j] == num and (i, j) != pos:
                return False

    return True

def solve(board):
    empty = find_empty(board)
    if not empty:
        return True

    row, col = empty

    for num in range(1, 10):
        if valid(board, num, (row, col)):
            board[row][col] = num

            if solve(board):
                return True

            board[row][col] = 0

    return False

# Example puzzle (0 represents empty cells)
board = [
    [5, 3, 0, 0, 7, 0, 0, 0, 0],
    [6, 0, 0, 1, 9, 5, 0, 0, 0],
    [0, 9, 8, 0, 0, 0, 0, 6, 0],
    [8, 0, 0, 0, 6, 0, 0, 0, 3],
    [4, 0, 0, 8, 0, 3, 0, 0, 1],
    [7, 0, 0, 0, 2, 0, 0, 0, 6],
    [0, 6, 0, 0, 0, 0, 2, 8, 0],
    [0, 0, 0, 4, 1, 9, 0, 0, 5],
    [0, 0, 0, 0, 8, 0, 0, 7, 9]
]

print("Original Sudoku:")
print_board(board)
print("\nSolving...\n")

if solve(board):
    print("Solved Sudoku:")
    print_board(board)
else:
    print("No solution exists")

Review:

• Algorithm Implementation: Both are on par, correctly implementing the Sudoku solving algorithm
• Code Readability: Claude 3.5’s generated code is slightly better, particularly with more user-friendly printing functionality
• User Experience: Claude 3.5 provides a more complete user experience, including feedback on the processing stages
• Code Style: Both maintain good and consistent Python coding style
• Practicality: Claude 3.5’s generated code may have a slight edge in practical use due to its clearer output format

How to access DeepSeek R1 via API?

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="<YOUR Novita AI API Key>",
)

model = "deepseek/deepseek_r1"
stream = True # or False
max_tokens = 2048
system_content = """Be a helpful assistant"""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
  
  

DeepSeek R1 and Claude 3.5 Sonnet each have unique strengths. DeepSeek R1 excels in math, coding, and logical problem-solving, offering cost-efficiency and customization as an open-source model—ideal for developers, researchers, or budget-conscious organizations.

Claude 3.5 Sonnet shines in multilingual tasks, code generation, visual reasoning, and handling large context windows. Its seamless integration via APIs makes it versatile for research, content creation, and advanced chatbots.

The choice depends on task requirements and user priorities, such as cost, domain expertise, or ease of use.

Frequently Asked Question

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