What is DeepSeek R1? A Complete Guide to the AI Model

DeepSeek R1 — if you’ve kept up with AI news, or just any news in general, there’s a good chance you’ve been hearing about it the past few days.

The AI app claims to rival the likes of OpenAI and Nvidia — claims that have caught the attention of AI enthusiasts, becoming the most downloaded free app on Apple’s App Store and Google Play Store in the United States.

But what is DeepSeek R1, and why is it causing such a stir in the tech community? DeepSeek R1 is an advanced artificial intelligence model developed by DeepSeek, designed to perform a wide range of language tasks including text generation, question answering, and code completion. In this comprehensive guide, we’ll explore what makes DeepSeek R1 unique, its capabilities, and its potential impact on various industries.

Let’s dive right in.

What is DeepSeek?

DeepSeek is a Chinese artificial intelligence company that was founded in 2023 by Liang Wenfeng. Even though the company is fairly young, it has released a couple version of its AI model in the past year.

Along with companies like Anthropic and Perplexity, DeepSeek has also invested extensively in AI research, trying to compete with giants like OpenAI and Nvidia.

Two of their models, DeepSeek R1 and DeepSeek V3, have brought the company to the limelight for achieving high accuracy parameters at relatively lower costs.

What is DeepSeek R1?

DeepSeek R1 is a family of AI models based on reinforcement learning (RL) that’s designed for logical and reasoning tasks. The model solves complex problems by breaking them down into multiple steps. It’s open-source and has a conversational chat interface like any other AI tool.

DeepSeek R1 in itself, has two versions, DeepSeek R1 and DeepSeek R1 Zero. The former was launched on 20th January 2025 and is accessible on the web, iOS, and Android. It is also available in the model catalogs in Azure AI Foundry and GitHub.

DeepSeek R1 Zero, on the other hand, has shown impressive results in terms of accuracy and performance for mathematical and reasoning use cases. However, it has not yet been released for users.

DeepSeek R1’s quick popularity not just gained the attention of AI enthusiasts, but also of world leaders and tech giants. So much so that, venture capitalist Marc Andreessen called it AI’s Sputnik moment.

“Deepseek R1 is AI’s Sputnik moment.” – Marc Andreessen, General partner of Andreessen Horowitz

How Does DeepSeek R1 Work? Understanding Its Architecture

The DeepSeek R1 architecture utilizes a Mixture of Experts (MoE) framework, allowing for efficient parameter activation during inference. This means, that for each query, DeepSeek R1 only utilizes 37 billion parameters out of the 671 billion total parameters it has. This approach helps it improve efficiency, deliver quicker results, and also save resources.

Let’s understand the architecture in-depth:

Mixture of Experts (MoE) Framework

DeepSeek R1’s MoE architecture combines shared experts with general capabilities and specific experts with narrow capabilities. This design allows the model to:

1. Activate Subset of Parameters: During inference, only a fraction of the total parameters are activated. Specifically, DeepSeek R1 has 671 billion total parameters but uses only 37 billion active parameters during operation.
2. Efficient Resource Utilization: By selectively activating experts, the model achieves high performance while minimizing computational resources. This efficiency is crucial for practical applications and deployment at scale.
3. Dynamic Expert Selection: The architecture includes a gating mechanism that determines which experts to activate based on the input. This dynamic selection process allows the model to adapt to various tasks and domains.
4. Load Balancing: The MoE framework implements a Load Balancing Loss, ensuring that experts are utilized evenly across different inputs. This prevents over-reliance on specific experts and promotes more robust performance across diverse tasks.

Despite being one of the many companies that trained AI models in the past couple of years, DeepSeek is one of the very few that managed to get international attention. But exactly what separates DeepSeek R1 from other AI models? Why is it special? Let’s find out.

Why is Everyone Talking About DeepSeek R1? Unveiling Its Impact

The buzz around DeepSeek R1 isn’t just hype. To understand what DeepSeek R1 is bringing to the table, let’s explore its groundbreaking capabilities that have the AI community excited:

DeepSeek R1 is extremely cost-effective

DeepSeek claims to have trained the AI model, DeepSeek R1, for just $5.6 million — which is extremely low in comparison to the billions other AI giants have been spending over the past few years.

OpenAI, in contrast, spent $5 billion in the past year alone. The training cost of Google Gemini, too, was estimated at $191 million in 2023 and OpenAI’s GPT-4 training costs were estimated at around $78 million.

Why does it matter?

The cost of training DeepSeek R1 may not affect the end user since the model is free to use. However, it means a lot for sustainability and ethics.

The AI industry is extremely expensive in terms of energy and resource consumption. A lower cost of training means lower consumption of resources, which makes DeepSeek’s feat a new hope for sustainable AI. 

And even though experts estimate that DeepSeek might have spent more than the $5.6 million that they claim, the cost will still be nowhere close to what global AI giants are currently spending.

DeepSeek R1 matches other AI models in accuracy and performance

Despite being developed with a significantly lower budget, DeepSeek R1 has proven itself capable of competing with the most advanced AI models available today in terms of accuracy and performance.

Check this detailed comparison released by DeepSeek:

Image Source

According to these benchmark tests, DeepSeek R1 performs at par with OpenAI’s GPT-4 and Google’s Gemini when evaluated on tasks such as logical inference, multilingual comprehension, and real-world reasoning.

Why does it matter?

Accuracy is a critical factor in determining the reliability of AI models. 

Consider this. You ask an AI model “What is 2+2?” and it says 5. No matter how quick the model is or how much it costs to train it, if the end result is inaccurate, your purpose of using the AI model fails. 

Many industry experts believed that DeepSeek’s lower training costs would compromise its effectiveness, but the model’s results tell a different story.

This balance between accuracy and resource efficiency positions DeepSeek as a game-changing alternative to costly models, proving that impactful AI doesn’t always require billions in investment.

DeepSeek is transparent with its training data

Along with the release of R1, the parent company also released research papers related to the training of the AI model. Apart from the usual training methods and evaluation criteria, this paper also highlighted the failures of their training methods.

This is quite rare in the AI industry, where competitors try keeping their training data and development strategies closely guarded. DeepSeek, unlike others, has been quite open about the challenges and limitations they faced, including biases and failure cases observed during testing.

Why does it matter?

DeepSeek’s transparency allows researchers, developers, and even competitors to understand both the strengths and limitations of the R1 model and also the usual training approaches. 

This training data can be key to speedy AI developments in various fields. Plus, it has also earned DeepSeek a reputation for building an environment of trust and collaboration.

These three factors have made DeepSeek stand out among the rest. But let’s be practical. As an end user, you’d rarely focus on the research data and training costs. What matters more is DeepSeek R1’s features and drawbacks, which we’ll discuss now.

DeepSeek R1 Key Features: What Makes DeepSeek R1 Stand Out?

What is DeepSeek R1 capable of? Let’s break down its key features to understand why it’s considered a leap forward in AI technology:

Conversational intelligence

DeepSeek R1 is an AI model powered by machine learning and natural language processing (NLP). That means, it understands, accepts commands, and gives outputs in human language, like many other AI apps (think ChatGPT and ChatSonic). 

That also means it has many of the basic features, like answering queries, scanning documents, providing multilingual support, and so on.

Math, Logic, and Problem-Solving Skills

One of R1’s most impressive features is that it’s specially trained to perform complex logical reasoning tasks. The benchmarks we discussed earlier alongside leading AI models also demonstrate its strengths in problem-solving and analytical reasoning.

Check how DeepSeek answered one of our queries:

This makes it ideal for industries like legal tech, data analysis, and financial advisory services. It is quite effective in interpreting complex queries where step-by-step reasoning is critical for accurate answers.

Open-Source Availability

DeepSeek R1 is one of the LLM’s that are open-source. That means developers are free to use this LLM to power their own AI apps and tools. 

How does that help? Here’s how:

1. Customization: Developers can fine-tune R1 for specific applications, potentially enhancing its performance in niche areas, like education or scientific research.
2. Transparency: The ability to examine the model’s inner workings fosters trust and allows for a better understanding of its decision-making processes.
3. Community-driven improvement: With many minds working on the model, bugs can be identified and fixed more quickly, giving you access to new and safe features.

The open-source approach also aligns with growing calls for ethical AI development, as it allows for greater scrutiny and accountability in how AI models are built and deployed.

High Accuracy for Complex Tasks

One of the main reasons to use DeepSeek R1 is its accuracy. As explained by DeepSeek, several studies have placed R1 on par with OpenAI’s o-1 and o-1 mini. This high accuracy combined with its use case of solving complex problems means you get a high-performance AI model for specialized applications.

It even answers the famous “STRAWBERRY” query correctly:

While DeepSeek R1 is all the buzz currently, it’s not without drawbacks and errors. Let’s discuss some of them here.

DeepSeek R1 Limitations: What Are DeepSeek R1’s Current Challenges?

While understanding what DeepSeek R1 is capable of is crucial, it’s equally important to recognize its current limitations. Like any other AI platform, DeepSeek R1 faces certain challenges:

Privacy Concerns

As DeepSeek is a newer company, people are skeptical about trusting the AI model with their data. Many users and experts are citing data privacy concerns, with larger companies and enterprises still wary of using the LLM.

Despite DeepSeek’s claims of robust data security measures, users may still be concerned about how their data is stored, used, and potentially shared. The absence of clear and comprehensive data handling policies could lead to trust issues, particularly in regions with strict data privacy regulations, such as the European Union’s GDPR.

In fact, it’s already under scrutiny in the EU and is restricted by several companies and government agencies.

Lack of Integrated Web Search

DeepSeek R1 doesn’t have web search integrated but has a separate option for it. While most AI models search the web on their own, DeepSeek R1 relies on the user to choose the web search option.

Without the web search option switched on, the AI model can only access its dated knowledge base. Here’s an example:

For updates on real-time data, you need to click on the “Search” option in the chatbox:

How Does DeepSeek R1 Compare to ChatGPT?

When comparing DeepSeek R1 to ChatGPT, it’s important to note that we’re looking at a snapshot in time. AI models are constantly evolving, and both systems have their strengths.

R1 shares some similarities with early versions of ChatGPT, particularly in terms of general language understanding and generation capabilities. However, R1 boasts a larger context window and higher maximum output, potentially giving it an edge in handling longer, more complex tasks.

ChatGPT’s current version, on the other hand, has better features than the brand new DeepSeek R1. It has integrated web search and content generation capabilities — areas where DeepSeek R1 falls behind.

However, both tools have their own strengths. While ChatGPT is great as a general-purpose AI chatbot, DeepSeek R1 is better for solving logic and math problems.

Practical Usage Tips: Integrating DeepSeek R1 into Your Workflow

DeepSeek R1 represents a significant leap in AI technology, combining advanced architecture with open-source accessibility. To help you make the most of this powerful model, here are some practical tips for integrating DeepSeek R1 into your workflow:

1. Optimize for Efficiency: When deploying DeepSeek R1, set the temperature between 0.5-0.7 for a balance between creativity and coherence. This range allows for diverse outputs while maintaining reliability in task performance.
2. Leverage the Extended Context: Take advantage of DeepSeek R1’s 128K token context length for tasks requiring extensive background information or long-form content generation. This feature is particularly useful for document analysis, research assistance, and complex problem-solving scenarios.
3. Utilize Serving Frameworks: Implement DeepSeek R1 using recommended serving frameworks like vLLM or SGLang. These frameworks are optimized for the model’s architecture and can significantly improve inference speed and resource utilization.
4. Integrate with Development Environments: For developers, consider integrating DeepSeek R1 into your IDE through plugins or custom scripts. This integration can enhance code completion, and documentation generation, and even assist in code review processes.
5. Automate Repetitive Tasks: Identify repetitive tasks in your workflow that could benefit from AI assistance. DeepSeek R1’s strong performance in areas like code generation and mathematical computations makes it ideal for automating routine development and data analysis tasks.

By following these tips, you can effectively harness the power of DeepSeek R1 to enhance productivity and innovation in your projects.

Final Thoughts: Is DeepSeek R1 Worth a Try?

The question of whether DeepSeek R1 is worth trying depends largely on your specific needs and concerns. 

DeepSeek R1 is excellent at solving complex queries which require multiple steps of “thinking.” It can solve math problems, answer logic puzzles, and also answer general queries from its database — always returning highly accurate answers.

Apart from the data privacy concerns, DeepSeek R1 is worth a try if you’re looking for an AI tool for problem-solving or academic use cases at present.

However, if you’re looking for an AI platform for other use cases like content creation, real-time web search, or marketing research, consider other tools built for those use cases, like Chatsonic.

Want to know more about DeepSeek R1? Stay tuned to Writesonic’s blog for more updates. 

Meanwhile, discover how AI can transform your marketing process. Try Chatsonic today!

What is DeepSeek R1: Frequently Asked Questions

Q: What is DeepSeek R1’s primary use case?
A: DeepSeek R1 is designed to enhance decision-making through advanced data analysis, pattern recognition, and predictive insights. It is particularly suited for applications involving large datasets where extracting actionable intelligence is essential.

Q: How does DeepSeek R1 compare to other AI models like GPT-4?
A: Unlike general-purpose language models such as GPT-4, which focus on natural language generation and conversation, DeepSeek R1 is optimized for data analytics and domain-specific predictions. While GPT-4 excels at creative content generation, DeepSeek R1 specializes in delivering actionable insights based on structured and unstructured data.

Q: What industries can benefit most from DeepSeek R1?
A: Industries such as finance, healthcare, retail, and logistics can derive significant value from DeepSeek R1. Its ability to analyze complex datasets, identify trends, and forecast outcomes makes it particularly useful for businesses that rely on data-driven strategies.

Niyati Mahale

Niyati Mahale is a Content Writer @Writesonic. She specializes in artificial intelligence and B2B, with a flair for combining effective storytelling and SEO best practices to create impactful content.