DeepSeek-R1 vs DeepSeek-V3: Detailed Comparison

DeepSeek has made significant strides in AI model development, with the release of DeepSeek-V3 in December 2024, followed by the groundbreaking R1 in January 2025. DeepSeek-V3 is a Mixture-of-Experts (MoE) model that focuses on maximizing efficiency without compromising performance. DeepSeek-R1, on the other hand, incorporates reinforcement learning to enhance reasoning and decision-making. In this DeepSeek-R1 vs DeepSeek-V3 article, we will compare the architecture, features and applications of both these models. We will also see their performance in various tasks involving coding, mathematical reasoning, and webpage creation, to find out which one is more suited for what use case.

DeepSeek-V3 vs DeepSeek-R1: Model Comparison

DeepSeek-V3 is a Mixture-of-Experts model boasting 671B parameters and 37B active per token. Meaning, it dynamically activates only a subset of parameters per token, optimizing computational efficiency. This design choice allows DeepSeek-V3 to handle large-scale NLP tasks with significantly lower operational costs. Moreover, its training dataset, consisting of 14.8 trillion tokens, ensures broad generalization across various domains.

DeepSeek-R1, released a month later, was built on the V3 model, leveraging reinforcement learning (RL) techniques to enhance its logical reasoning capabilities. By incorporating supervised fine-tuning (SFT), it ensures that responses are not only accurate but also well-structured and aligned with human preferences. The model particularly excels in structured reasoning. This makes it suitable for tasks that require deep logical analysis, such as mathematical problem-solving, coding assistance, and scientific research.

Also Read: Is Qwen2.5-Max Better than DeepSeek-R1 and Kimi k1.5?

Pricing Comparison

Let’s have a look at the costs for input and output tokens for DeepSeek-R1 and DeepSeek-V3.

As you can see, DeepSeek-V3 is roughly 6.5x cheaper compared to DeepSeek-R1 for input and output tokens.

DeepSeek-V3 vs DeepSeek-R1 Training: A Step-by-Step Breakdown

DeepSeek has been pushing the boundaries of AI with its cutting-edge models. Both DeepSeek-V3 and DeepSeek-R1 are trained using massive datasets, fine-tuning techniques, and reinforcement learning to improve reasoning and response accuracy. Let’s break down their training processes and learn how they have evolved into these intelligent systems.

DeepSeek-V3: The Powerhouse Model

The DeepSeek-V3 model has been trained in two parts – first, the pre-training phase, followed by the post-training. Let’s understand what happens in each of these phases.

Pre-training: Laying the Foundation

DeepSeek-V3 starts with a Mixture-of-Experts (MoE) model that smartly selects the relevant parts of the network, making computations more efficient. Here’s how the base model was trained.

• Data-Driven Intelligence: Firstly, it was trained on a massive 14.8 trillion tokens, covering multiple languages and domains. This ensures a deep and broad understanding of human knowledge.
• Training Effort: It took 2.788 million GPU hours to train the model, making it one of the most computationally expensive models to date.
• Stability & Reliability: Unlike some large models that struggle with unstable training, DeepSeek-V3 maintains a smooth learning curve without major loss spikes.

Post-training: Making It Smarter

Once the base model is ready, it needs fine-tuning to improve response quality. DeepSeek-V3’s base model was further trained using Supervised Fine-Tuning. In this process, experts refined the model by guiding it with human-annotated data to improve its grammar, coherence, and factual accuracy.

DeepSeek-R1: The Reasoning Specialist

DeepSeek-R1 takes things a step further; it’s designed to think more logically, refine responses, and reason better. Instead of starting from scratch, DeepSeek-R1 inherits the knowledge of DeepSeek-V3 and fine-tunes it for better clarity and reasoning.

Multi-stage Training for Deeper Thinking

Here’s how DeepSeek-R1 was trained on V3.

1. Cold Start Fine-tuning: Instead of throwing massive amounts of data at the model immediately, it starts with a small, high-quality dataset to fine-tune its responses early on.
2. Reinforcement Learning Without Human Labels: Unlike V3, DeepSeek-R1 relies entirely on RL, meaning it learns to reason independently instead of just mimicking training data.
3. Rejection Sampling for Synthetic Data: The model generates multiple responses, and only the best-quality answers are selected to train itself further.
4. Blending Supervised & Synthetic Data: The training data merges the best AI-generated responses with the supervised fine-tuned data from DeepSeek-V3.
5. Final RL Process: A final round of reinforcement learning ensures the model generalizes well to a wide variety of prompts and can reason effectively across topics.

Key Differences in Training Approach

DeepSeek-V3 vs DeepSeek-R1: Performance Comparison

Now we’ll compare DeepSeek-V3 and DeepSeek-R1, based on their performance in certain tasks. For this, we will give the same prompt to both the models and compare their responses to find out which model is better for what application. In this comparison, we will be testing their skills in mathematical reasoning,

Task 1: Advanced Number Theory

In the first task we will ask both the models to do the prime factorization of a large number. Let’s see how accurately they can do this.

Prompt: “Perform the prime factorization of large composite numbers, such as: 987654321987654321987654321987654321987654321987654321”

Response from DeepSeek-V3:

Response from DeepSeek-R1:

Comparative Analysis:

DeepSeek-R1 demonstrated significant improvements over DeepSeek-V3, not only in speed but also in accuracy. R1 was able to generate responses faster while maintaining a higher level of precision, making it more efficient for complex queries. Unlike V3, which directly produced responses, R1 first engaged in a reasoning phase before formulating its answers, leading to more structured and well-thought-out outputs. This enhancement highlights R1’s superior decision-making capabilities, optimized through reinforcement learning, making it a more reliable model for tasks requiring logical progression and deep understanding

Task 2: Webpage Creation

In this task, we will test the performance of both the models in creating a webpage.

Prompt: “Create a basic HTML webpage for beginners that includes the following elements:
A header with the title ‘Welcome to My First Webpage’.
A navigation bar with links to ‘Home’, ‘About’, and ‘Contact’ sections.
A main content area with a paragraph introducing the webpage.
An image with a placeholder (e.g., ‘image.jpg’) inside the content section.
A footer with your name and the year.
Basic styling using inline CSS to set the background color of the page, the text color, and the font for the content.”

Response from DeepSeek-V3:

Response from DeepSeek-R1:

Comparative Analysis:

Given the same prompt, DeepSeek-R1 outperformed DeepSeek-V3 in structuring the webpage template. R1’s output was more organized, visually appealing, and aligned with modern design principles. Unlike V3, which generated a functional but basic layout, R1 incorporated better formatting and responsiveness. This shows R1’s improved ability to understand design requirements and produce more refined outputs.

Task 3: Coding

Now, let’s test the models on how well they can solve this complex LeetCode problem.

Prompt: “You have a list of tasks and the order they need to be done in. Your job is to arrange these tasks so that each task is done before the ones that depend on it. Understanding Topological Sort
It’s like making a to-do list for a project.

Important points:
– You have tasks (nodes) and dependencies (edges).
– Start with tasks that don’t depend on anything else.
– Keep going until all tasks are on your list.
– You’ll end up with a list that makes sure you do everything in the right order.

Steps
– Use a list to show what tasks depend on each other.
– Make an empty list for your final order of tasks.

Create a helper function to visit each task:
– Mark it as in process.
– Visit all the tasks that need to be done before this one.
– Add this task to your final list.

Mark it as done.
Start with tasks that don’t have any prerequisites.”

Response from DeepSeek-V3:

from collections import defaultdict, deque

def topological_sort(tasks, dependencies):
    # Step 1: Build the graph and in-degree count
    graph = defaultdict(list)
    in_degree = {task: 0 for task in tasks}

    for u, v in dependencies:
        graph[u].append(v)  # u must be done before v
        in_degree[v] += 1

    # Step 2: Initialize the queue with tasks having no prerequisites
    queue = deque([task for task in tasks if in_degree[task] == 0])

    # Step 3: Perform topological sort
    result = []
    while queue:
        task = queue.popleft()
        result.append(task)

        # Reduce in-degree of dependent tasks
        for dependent in graph[task]:
            in_degree[dependent] -= 1
            if in_degree[dependent] == 0:
                queue.append(dependent)

    # Step 4: Check for cycles
    if len(result) != len(tasks):
        return []  # Cycle detected, no valid order

    return result

# Example usage
tasks = ['A', 'B', 'C', 'D', 'E']
dependencies = [('A', 'B'), ('A', 'C'), ('B', 'D'), ('C', 'D'), ('D', 'E')]

sorted_tasks = topological_sort(tasks, dependencies)
print("Topological Order:", sorted_tasks)

Response from DeepSeek-R1:

def topological_sort(tasks):
    visited = {}  # 0: unvisited, 1: visiting, 2: visited
    result = []
    
    # Initialize all tasks as unvisited
    for task in tasks:
        visited[task] = 0
    
    def dfs(node):
        if visited[node] == 1:
            # Detected a cycle, cannot sort
            raise ValueError("Cycle detected, no valid topological order exists.")
        if visited[node] == 2:
            return
        # Mark as visiting
        visited[node] = 1
        # Visit all prerequisites first
        for prerequisite in tasks[node]:
            dfs(prerequisite)
        # Mark as visited and add to result
        visited[node] = 2
        result.append(node)
    
    # Visit each unvisited node
    for node in tasks:
        if visited[node] == 0:
            dfs(node)
    
    return result

Comparative Analysis:

DeepSeek-R1 is better suited for large graphs, using a BFS approach that avoids stack overflow and ensures scalability. DeepSeek-V3 relies on DFS with explicit cycle detection, which is intuitive but prone to recursion limits on large inputs. R1’s BFS method simplifies cycle handling, making it more robust and efficient for most applications. Unless deep exploration is needed, R1’s approach is generally more practical and easier to implement.

Performance Comparison Table

Now let’s see comparison of DeepSeek-R1 and DeepSeek-V3 across the given tasks in table format

So from the table we can clearly see that DeepSeek-R1 consistently outperforms DeepSeek-V3 in reasoning, structure, and scalability across different tasks.

Choosing the Right Model

Understanding the strengths of DeepSeek-R1 and DeepSeek-V3 helps users select the best model for their needs:

• Choose DeepSeek-R1 if your application requires advanced reasoning and structured decision-making, such as mathematical problem-solving, research, or AI-assisted logic-based tasks.
• Choose DeepSeek-V3 if you need cost-effective, scalable processing, such as content generation, multilingual translation, or real-time chatbot responses.

As AI models continue to evolve, these innovations highlight the growing specialization of NLP models—whether optimizing for reasoning depth or processing efficiency. Users should assess their requirements carefully to leverage the most suitable AI model for their domain.

Also Read: Kimi k1.5 vs DeepSeek R1: Battle of the Best Chinese LLMs

Conclusion

While DeepSeek-V3 and DeepSeek-R1 share the same foundation model, their training paths differ significantly. DeepSeek-V3 follows a traditional supervised fine-tuning and RL pipeline, while DeepSeek-R1 uses a more experimental RL-first approach that leads to superior reasoning and structured thought generation.

This comparison of DeepSeek-V3 vs R1 highlights how different training methodologies can lead to distinct improvements in model performance, with DeepSeek-R1 emerging as the stronger model for complex reasoning tasks. Future iterations will likely combine the best aspects of both approaches to push AI capabilities even further.

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Janvi Kumari

Hi, I am Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.