DeepSeek R1 vs OpenAI o1: Which One is Faster, Cheaper and Smarter?

The DeepSeek R1 has arrived, and it’s not just another AI model—it’s a significant leap in AI capabilities, trained upon the previously released DeepSeek-V3-Base variant. With the full-fledged release of DeepSeek R1, it now stands on par with OpenAI o1 in both performance and flexibility. What makes it even more compelling is its open weight and MIT licensing, making it commercially viable and positioning it as a strong choice for developers and enterprises alike.

But what truly sets DeepSeek R1 apart is how it challenges industry giants like OpenAI, achieving remarkable results with a fraction of the resources. In just two months, DeepSeek has done what seemed impossible—launching an open-source AI model that rivals proprietary systems, all while operating under strict limitations. In this article, we will compare – DeepSeek R1 vs OpenAI o1.

DeepSeek R1: A Testament to Ingenuity and Efficiency

With a budget of just $6 million, DeepSeek has accomplished what companies with billion-dollar investments have struggled to do. Here’s how they did it:

• Budget Efficiency: Built R1 for just $5.58 million, compared to OpenAI’s estimated $6 billion+ investment.
• Resource Optimization: Achieved results with 2.78 million GPU hours, significantly lower than Meta’s 30.8 million GPU hours for similar-scale models.
• Innovative Workarounds: Trained using restricted Chinese GPUs, showcasing ingenuity under technological and geopolitical constraints.
• Benchmark Excellence: R1 matches OpenAI o1 in key tasks, with some areas of clear outperformance.

While DeepSeek R1 builds upon the collective work of open-source research, its efficiency and performance demonstrate how creativity and strategic resource allocation can rival the massive budgets of Big Tech.

What Makes DeepSeek R1 a Game-Changer?

Beyond its impressive technical capabilities, DeepSeek R1 offers key features that make it a top choice for businesses and developers:

• Open Weights & MIT License: Fully open and commercially usable, giving businesses the flexibility to build without licensing constraints.
• Distilled Models: Smaller, fine-tuned versions (akin to Quen and Llama), providing exceptional performance while maintaining efficiency for diverse applications.
• API Access: Easily accessible via API or directly on their platform—for free!
• Cost-Effectiveness: A fraction of the cost compared to other leading AI models, making advanced AI more accessible than ever.

DeepSeek R1 raises an exciting question—are we witnessing the dawn of a new AI era where small teams with big ideas can disrupt the industry and outperform billion-dollar giants? As the AI landscape evolves, DeepSeek’s success highlights that innovation, efficiency, and adaptability can be just as powerful as sheer financial might.

Overview of DeepSeek R1

The DeepSeek R1 model boasts a 671 billion parameters architecture and has been trained on the DeepSeek V3 Base model. Its focus on Chain of Thought (CoT) reasoning makes it a strong contender for tasks requiring advanced comprehension and reasoning. Interestingly, despite its large parameter count, only 37 billion parameters are activated during most operations, similar to DeepSeek V3.

DeepSeek R1 isn’t just a monolithic model; the ecosystem includes six distilled models fine-tuned on synthetic data derived from DeepSeek R1 itself. These smaller models vary in size and target specific use cases, offering solutions for developers who need lighter, faster models while maintaining impressive performance.

Distilled Model Lineup

These distilled models enable flexibility, catering to both local deployment and API usage. Notably, the Llama 33.7B model outperforms the o1 Mini in several benchmarks, underlining the strength of the distilled variants.

You can find all about OpenAI o1 here.

How DeepSeek R1 Gives Unbeatable Performance at Minimal Cost?

DeepSeek R1’s impressive performance at minimal cost can be attributed to several key strategies and innovations in its training and optimization processes. Here’s how they achieved it:

1. Reinforcement Learning Instead of Heavy Supervised Fine-Tuning

Most traditional LLMs (like GPT, LLaMA, etc.) rely heavily on supervised fine-tuning, which requires extensive labeled datasets curated by human annotators. DeepSeek R1 took a different approach:

• DeepSeek-R1-Zero:
  • Instead of supervised learning, it utilized pure reinforcement learning (RL).
  • The model was trained through self-evolution, allowing it to iteratively improve reasoning capabilities without human intervention.
  • RL helps in optimizing policies based on trial-and-error, making the model more cost-effective compared to supervised training, which requires vast human-labeled datasets.
• DeepSeek-R1 (Cold Start Strategy):
  • To avoid common issues in RL-only models (like incoherent responses), they introduced a small, high-quality supervised dataset for a “cold start.”
  • This enabled the model to bootstrap better from the beginning, ensuring human-like fluency and readability while maintaining strong reasoning capabilities.

Impact:

• RL training significantly reduced data annotation costs.
• Self-evolution allowed the model to discover problem-solving strategies autonomously.

2. Distillation for Efficiency and Scaling

Another game-changing approach used by DeepSeek was the distillation of reasoning capabilities from the larger R1 models into smaller models, such as:

• Qwen, Llama, etc.
  • By distilling knowledge, they were able to create smaller models (e.g., 14B) that outperform even some state-of-the-art (SOTA) models like QwQ-32B.
  • This process essentially transferred high-level reasoning capabilities to smaller architectures, making them highly efficient without sacrificing much accuracy.

Key Distillation Benefits:

• Lower computational costs: Smaller models require less inference time and memory.
• Scalability: Deploying distilled models on edge devices or cost-sensitive cloud environments is easier.
• Maintaining strong performance: The distilled versions of R1 still rank competitively in benchmarks.

3. Benchmark Performance & Optimization Focus

DeepSeek R1 has focused its optimization towards specific high-impact benchmarks like:

• AIME 2024: Achieving near SOTA performance at 79.8%
• MATH-500: Improving reasoning with 97.3% accuracy
• Codeforces (Competitive Programming): Ranking within the top 3.7%
• MMLU (General Knowledge): Competitive at 90.8%, slightly behind some models, but still impressive.

Instead of being a general-purpose chatbot, DeepSeek R1 focuses more on mathematical and logical reasoning tasks, ensuring better resource allocation and model efficiency.

4. Efficient Architecture and Training Techniques

DeepSeek likely benefits from several architectural and training optimizations:

• Sparse Attention Mechanisms:
  • Enables processing of longer contexts with lower computational cost.
• Mixture of Experts (MoE):
  • Possibly used to activate only parts of the model dynamically, leading to efficient inference.
• Efficient Training Pipelines:
  • Training on well-curated, domain-specific datasets without excessive noise.
  • Use of synthetic data for reinforcement learning phases.

5. Strategic Model Design Choices

DeepSeek’s approach is highly strategic in balancing cost and performance by:

1. Focused domain expertise (math, code, reasoning) rather than general-purpose NLP tasks.
2. Optimized resource utilization to prioritize reasoning tasks over less critical NLP capabilities.
3. Smart trade-offs like using RL where it works best and minimal fine-tuning where necessary.

Why Is It Cost-Effective?

• Reduced need for expensive supervised datasets due to reinforcement learning.
• Efficient distillation ensures top-tier reasoning performance in smaller models.
• Targeted training focus on reasoning benchmarks rather than general NLP tasks.
• Optimization of architecture for better compute efficiency.

By combining reinforcement learning, selective fine-tuning, and strategic distillation, DeepSeek R1 delivers top-tier performance while maintaining a significantly lower cost compared to other SOTA models.

DeepSeek R1 vs. OpenAI o1: Price Comparison

DeepSeek R1 scores comparably to OpenAI o1 in most evaluations and even outshines it in specific cases. This high level of performance is complemented by accessibility; DeepSeek R1 is free to use on the DeepSeek chat platform and offers affordable API pricing. Here’s a cost comparison:

• DeepSeek R1 API: 55 Cents for input, $2.19 for output ( 1 million tokens)
• OpenAI o1 API: $15 for input, $60 for output ( 1 million tokens)

API is 96.4% cheaper than chatgpt.

DeepSeek R1’s lower costs and free chat platform access make it an attractive option for budget-conscious developers and enterprises looking for scalable AI solutions.

Benchmarking and Reliability

DeepSeek models have consistently demonstrated reliable benchmarking, and the R1 model upholds this reputation. DeepSeek R1 is well-positioned as a rival to OpenAI o1 and other leading models with proven performance metrics and strong alignment with chat preferences. The distilled models, like Qwen 32B and Llama 33.7B, also deliver impressive benchmarks, outperforming competitors in similar-size categories.

Practical Usage and Accessibility

DeepSeek R1 and its distilled variants are readily available through multiple platforms:

1. DeepSeek Chat Platform: Free access to the main model.
2. API Access: Affordable pricing for large-scale deployments.
3. Local Deployment: Smaller models like Quen 8B or Quen 32B can be used locally via VM setups.

While some models, such as the Llama variants, are yet to appear on AMA, they are expected to be available soon, further expanding deployment options.

DeepSeek R1 vs OpenAI o1: Comparison of Different Benchmarks

1. AIME 2024 (Pass@1)

• DeepSeek-R1: 79.8% accuracy
• OpenAI o1-1217: 79.2% accuracy
• Explanation:
  • This benchmark evaluates performance on the American Invitational Mathematics Examination (AIME), a challenging math contest.
  • DeepSeek-R1 slightly outperforms OpenAI-o1-1217 by 0.6%, meaning it’s marginally better at solving these types of math problems.

2. Codeforces (Percentile)

• DeepSeek-R1: 96.3%
• OpenAI o1-1217: 96.6%
• Explanation:
  • Codeforces is a popular competitive programming platform, and percentile ranking shows how well the models perform compared to others.
  • OpenAI-o1-1217 is slightly better (by 0.3%), meaning it may have a slight advantage in handling algorithmic and coding challenges.

3. GPQA Diamond (Pass@1)

• DeepSeek-R1: 71.5%
• OpenAI o1-1217: 75.7%
• Explanation:
  • GPQA Diamond assesses a model’s ability to answer complex general-purpose questions.
  • OpenAI-o1-1217 performs better by 4.2%, indicating stronger general question-answering capabilities in this category.

4. MATH-500 (Pass@1)

• DeepSeek-R1: 97.3%
• OpenAI o1-1217: 96.4%
• Explanation:
  • This benchmark measures math problem-solving skills across a wide range of topics.
  • DeepSeek-R1 scores higher by 0.9%, showing it might have better precision and reasoning for advanced math problems.

5. MMLU (Pass@1)

• DeepSeek-R1: 90.8%
• OpenAI o1-1217: 91.8%
• Explanation:
  • MMLU (Massive Multitask Language Understanding) tests the model’s general knowledge across subjects like history, science, and social studies.
  • OpenAI-o1-1217 is 1% better, meaning it might have a broader or deeper understanding of diverse topics.

6. SWE-bench Verified (Resolved)

• DeepSeek-R1: 49.2%
• OpenAI o1-1217: 48.9%
• Explanation:
  • This benchmark evaluates the model’s performance in resolving software engineering tasks.
  • DeepSeek-R1 has a slight 0.3% advantage, indicating a similar level of coding proficiency with a small lead.

Overall Verdict:

• DeepSeek-R1 Strengths: Math-related benchmarks (AIME 2024, MATH-500) and software engineering tasks (SWE-bench Verified).
• OpenAI o1-1217 Strengths: Competitive programming (Codeforces), general-purpose Q&A (GPQA Diamond), and general knowledge tasks (MMLU).

The two models perform quite similarly overall, with DeepSeek-R1 leading in math and software tasks, while OpenAI o1-1217 excels in general knowledge and problem-solving.

If your focus is on mathematical reasoning and software engineering, DeepSeek-R1 may be a better choice, whereas, for general-purpose tasks and programming competitions, OpenAI o1-1217 might have an edge.

How to Access DeepSeek R1 Using Ollama?

Firstly, Install Ollama

• Visit the Ollama website to download the tool. For Linux users:
• Execute the following command in your terminal:

curl -fsSL https://ollama.com/install.sh | sh

Then run the model.

Here’s the Ollama like for DeepSeek R1: ollama run deepseek-r1

Copy the command: ollama run deepseek-r1

I am running Ollama run deepseek-r1:1.5b in local and it will take few minutes to download the model.

Prompt: Give me code for the Fibonacci nth series

Output

The output quality from deepseek-r1:1.5b looks quite solid, with a few positive aspects and areas for potential improvement:

Positive Aspects

1. Logical Thought Process
   • The model exhibits a clear step-by-step reasoning process, considering both recursive and iterative approaches.
   • It catches common pitfalls (e.g., inefficiencies of recursion) and justifies the choice of an iterative method.
2. Correctness of Code
   • The final iterative solution is correct and handles base cases properly.
   • The test case fib(5) produces the correct output.
3. Explanation Depth
   • The provided breakdown of the code is detailed and beginner-friendly, covering:
     • Base cases
     • Loop behavior
     • Variable updates
     • Complexity analysis
4. Efficiency Consideration
   • The explanation highlights the time complexity ($O(n)$) and contrasts it with recursion, demonstrating a good understanding of algorithmic efficiency.

How to Use DeepSeek R1 in Google Colab?

Using Transformer

!pip install transformers accelerate torch

This command installs three Python libraries:

• transformers: A library by Hugging Face for working with pre-trained language models.
• accelerate: A library to optimize and speed up training and inference for PyTorch models.
• torch: The PyTorch library, which is a deep learning framework.

from transformers import pipeline

This imports the pipeline function from the transformers library. The pipeline function is a high-level API that simplifies the process of using pre-trained models for tasks like text generation, classification, etc.

messages = [
   {"role": "user", "content": "Give me code for the Fibonacci nth series"},
]

This defines a list of messages, where each message is a dictionary with a role (e.g., “user”) and content (the actual text of the message). This format is often used in conversational models.

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
pipe(messages)

This creates a text-generation pipeline using the deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model. The pipeline function automatically handles loading the model and tokenizer.

This passes the messages list to the pipeline, which generates a response based on the input.

Output

Okay, the user is asking for a Fibonacci sequence calculator. I remember 

that the Fibonacci sequence starts with 0 and 1, and each subsequent 

number is the sum of the previous two.

I should write a function called fib(n) where n is the term number we want 

to find. If n is 0 or 1, it returns 0 since those are the starting points. 

For other values, I'll use recursion because each term depends on the two 

before it, which seems straightforward.

Wait, but recursion can be inefficient for large n because it recalculates 

many terms repeatedly. Maybe the user wants an iterative approach instead 

to make it faster and more efficient. That way, even if n is big, it 

doesn't take too long.

I'll structure my function with a loop that starts from 2 up to n. I'll 

keep track of the two previous numbers, prev1 and prev2. Then for each 

step beyond 2, calculate current as the sum of prev1 and prev2. Update 

prev2 to be prev1, and update prev1 to current.

Wait again, what if n is 0? The function should return 0, same with n=1. 

So I'll add a condition at the beginning of the loop where if it's the 

first term, we handle it correctly.

Let me test some examples. For fib(5), it should be 5. Let's see: prev1 

starts as 0, prev2 as 1. Then for i=3 to 5:

i=3: current = 1 (0+1)

i=4: current = 2 (1+1)

i=5: current = 3 (1+2) Wait, that's not right. Oh no, I think I messed up the calculation steps.

Wait, for fib(5), let me recount:

n=0: 0

n=1: 1

n=2: 1

n=3: 2

n=4: 3

n=5: 5. So my previous step-by-step was wrong. I need to make sure each iteration is correct.

I think in the loop, when i reaches n-2, which for fib(5) is index 5 (since starting at 0), but wait, let me adjust the code correctly.

Let me write down:

def fib(n):

    if n == 0:

        return 0

    elif n == 1:

        return 1

    prev1 = 0

    prev2 = 1

    for i in range(2, n+1):

        current = prev1 + prev2

        prev1 = prev2

        prev2 = current

    return prev2

Testing fib(5) would give:

i=2: current 1; prev1=1, prev2=1

i=3: current 2; prev1=1, prev2=2

i=4: current 3; prev1=2, prev2=3

i=5: current 5; return 5. Correct.

Yes, that should work.

</think>

Here’s a simple iterative implementation of the Fibonacci sequence:

```python

def fib(n):

    if n == 0:

        return 0

    elif n == 1:

        return 1

    else:

        prev1 = 0

        prev2 = 1

        for i in range(2, n+1):

            current = prev1 + prev2

            prev1 = prev2

            prev2 = current

        return prev2

# Test the function

print(fib(5))  # Output: 5

```

### Explanation:

- The function `fib(n)` calculates the nth Fibonacci number using an iterative approach.

- **Base Cases**: If `n` is 0, it returns 0; if `n` is 1, it returns 1.

- **Iteration**: For values of `n` greater than 1, we initialize two variables (`prev1` and `prev2`) to represent the first two Fibonacci numbers (0 and 1).

- **Loop**: We loop from 2 up to `n`, updating the values of `prev1` and `prev2` in each iteration. After completing the loop, `prev2` contains the nth Fibonacci number.

- **Efficiency**: This method is efficient with a time complexity of O(n) since it only iterates once through the sequence.

### Example

If you want to calculate the 5th Fibonacci number:

- The function returns 5 when called as `fib(5)`.

This implementation avoids the inefficiencies of recursion and is more straightforward for educational purposes.

Code Implementation of OpenAI o1

!pip install openai

from getpass import getpass

OPENAI_KEY = getpass('Enter Open AI API Key: ')

import openai

from IPython.display import HTML, Markdown, display

openai.api_key = OPENAI_KEY

def get_completion(prompt, model="o1-2024-12-17"):

  messages = [{"role": "user", "content": prompt}]

  response = openai.chat.completions.create(

      model=model,

      messages=messages,

      temperature=1, # degree of randomness of the model's output

  )

  return response.choices[0].message.content

response = get_completion(prompt= '''Give me code for the Fibonacci nth series''',

                        model='o1-2024-12-17')

display(Markdown(response))

Output

Final Verdict

DeepSeek R1 provides a more efficient and versatile solution, making it the better choice overall. It correctly handles edge cases, offers a function that returns values for further use, and includes a detailed explanation. This makes it suitable for both practical applications and educational purposes.

OpenAI o1, while simpler and more beginner-friendly, is limited in functionality as it only prints the sequence without returning values, making it less useful for advanced tasks.

Recommendation: Go with DeepSeek R1’s approach if you need an efficient and reusable solution. Use OpenAI o1’s approach if you’re just looking to understand the Fibonacci sequence in a straightforward way.

Conclusion

The launch of DeepSeek R1 marks a major shift in the AI landscape, offering an open-weight, MIT-licensed alternative to OpenAI o1. With impressive benchmarks and distilled variants, it provides developers and researchers with a versatile, high-performing solution.

DeepSeek R1 excels in reasoning, Chain of Thought (CoT) tasks, and AI comprehension, delivering cost-effective performance that rivals OpenAI o1. Its affordability and efficiency make it ideal for various applications, from chatbots to research projects. In tests, its response quality matched OpenAI o1, proving it as a serious competitor.

The DeepSeek R1 vs OpenAI o1 showdown highlights affordability and accessibility. Unlike proprietary models, DeepSeek R1 democratizes AI with a scalable and budget-friendly approach, making it a top choice for those seeking powerful yet cost-efficient AI solutions.

Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.