What is Deepseek AI?

DeepSeek is the name of a free AI-powered chatbot. It looks, feels, and performs very similarly to ChatGPT. That means it’s used for many of the same jobs. But how well it performs in comparison to its competitors is debatable.

It is allegedly as powerful as OpenAI’s o1 model. This was published at the end of last year in activities such as mathematics and coding. Like o1, R1 is a “reasoning” model. These algorithms generate responses gradually. They mimic how humans think through issues or ideas. It utilizes less memory than its competitors, lowering the cost of doing tasks.

DeepSeek, like many other Chinese AI models, like Baidu’s Ernie and ByteDance’s Doubao, has been trained to avoid politically sensitive issues.

When the BBC asked the app regarding the events at Tiananmen Square on June 4, 1989, DeepSeek did not provide any information about the massacre. This is forbidden to discuss in China.

It responded, “I am sorry, but I cannot answer that question. I am an AI assistant designed to deliver useful and harmless responses.” Censorship by the Chinese government poses a significant hurdle to the country’s international AI goals. However, DeepSeek’s underlying model looks to have been trained from reliable sources while adding a layer of censoring. Or withholding certain information through an additional protective layer

Who is behind Deepseek?

Liang Wenfeng created DeepSeek in December 2023. The company launched its first AI big language model the following year. Liang, who holds degrees in electronic information engineering and computer science from Zhejiang University, is relatively unknown. However, he now finds himself in the international limelight.

He was recently observed attending a meeting held by China’s Premier Li Qiang. It demonstrates DeepSeek’s expanding presence in the AI business. Unlike many American AI entrepreneurs from Silicon Valley, Mr. Liang has a financial background.

He is the CEO of High-Flyer. This is a private equity firm that employs artificial intelligence to analyze financial data and make investment decisions. It is a practice known as quantitative trading. In 2019, High-Flyer became the first quant hedge fund in China to raise more than 100 billion yuan ($13 million).

Liang asked in a speech that year, “If the US can develop its quantitative trading sector, why not China?”

In a rare interview last year, he stated that China’s AI sector “cannot remain a follower forever.”. He went on to remark, “We frequently hear that there is a one- or two-year difference between Chinese and American AI, but the true divide is between originality and imitation. “If this does not change, China will remain a follower.”

Coding and technical inquiries

If you are a programmer, this will be the most crucial feature. ChatGPT provides complete code assistance, including clear explanations and code suggestions. This makes it an ideal learning tool for people new to data science.

DeepSeek takes a more direct approach. It generates code faster and in a modular way that is especially effective for quick, efficient answers to specific coding difficulties. Many developers have had success with DeepSeek for quick prototyping and ChatGPT for understanding complex systems.

Writing assistance

Both methods can assist with documentation and content production. But their approaches differ. ChatGPT excels in creating engaging, conversational content with rich context. This is ideal for explaining complicated data ideas to non-technical stakeholders.

DeepSeek, on the other hand, excels at technical writing. It produces exact, formal documentation that is especially useful for data projects and technical specifications.

Brainstorming and creativity

When brainstorming data project techniques or analytical strategies, these tools have varied capabilities. ChatGPT excels at producing numerous different approaches to a topic. It allows you to investigate various analytical possibilities.

DeepSeek often offers fewer but more extensively developed answers. It delves deeply into a single method—ideal for fleshing out a certain data strategy in detail.

Cost and Efficiency

DeepSeek stands out for its low operational expenses. These are achieved through the use of energy-efficient hardware and edge deployments. It’s a wonderful resource for those operating on a tight budget. Because it’s completely free to use.

ChatGPT’s subscription model, while more expensive. It provides constant performance and advanced features. These are useful for professional data work.

Learning and Research

In the context of data science education, ChatGPT offers extensive, tutorial-style explanations that are effective for learning new ideas. It excels at breaking down difficult concepts into manageable chunks.

DeepSeek emphasizes precision and conciseness. This makes it ideal for quick reference and fact-checking throughout data projects. Its technical accuracy is especially useful for researching specific approaches or algorithms.

Privacy and ethical considerations

This is especially critical when dealing with sensitive data. ChatGPT adheres to Western data protection regulations, giving it a safer option for projects requiring stringent data privacy compliance.

DeepSeek’s data storage techniques and content moderation regulations may cause problems for certain types of projects. Particularly those containing sensitive material or requiring open-ended analytical conversations.

Each feature comparison highlights important trade-offs in many data science settings. The goal is to align these skills with your individual needs and requirements. 

DeepSeek’s massive language models

Since its inception in 2023, DeepSeek has developed several generative AI models. With each subsequent generation, the firm has tried to increase the capacity and efficiency of its products.

Deepseek Coder

This is the company’s first open-source model created exclusively for coding jobs. It will be released in November 2023.

Deep Seek LLM

This is the initial iteration of the company’s general-purpose model. And it will be released in December 2023.

DeepSeek-V2

This is the second edition of the company’s LLM, due out in May 2024. And it focuses on high performance and cheap training expenses.

DeepSeek-Coder-V2

This 236 billion-parameter model. This will be released in July 2024. It includes a context window of 128,000 tokens and is intended for complex coding tasks.

DeepSeek-V3

DeepSeek-V3, which will be released in December 2024. This employs a mixture-of-experts architecture. It can handle a wide range of jobs. The model contains 671 billion parameters and a context length of 128,000.

DeepSeek-R1

This model, which was released in January 2025, is based on DeepSeek-V3. It is aimed at sophisticated reasoning tasks that directly compete with OpenAI’s o1 model in terms of performance. But also keeping a substantially lower cost structure. Like DeepSeek-V3, the model has 671 billion parameters and a context length of 128,000.

Janus-Pro-7B

Janus-Pro-7B, which was released in January 2025. It has a vision model capable of understanding and generating images.

Training advances in DeepSeek

DeepSeek’s strategy for training R1 models differs from OpenAI’s. The training took less time, required fewer AI accelerators, and cost less to create. DeepSeek’s goal is to attain artificial general intelligence. And the company’s advances in reasoning capabilities are key steps forward in AI development.

Distillation

DeepSeek researchers used efficient knowledge transfer approaches to efficiently compress capabilities into models with as little as 1.5 billion parameters.

Emergent behavior network

DeepSeek’s natural behavioral breakthrough is the revelation that complicated reasoning patterns can emerge organically via reinforcement learning.

Reinforcement learning

DeepSeek used a large-scale reinforcement learning method. This is aimed at reasoning problems.

Reward engineering

The researchers designed a rule-based reward system for the model. It outperformed more commonly used neural reward models. Reward engineering is the process of developing an incentive structure to guide an AI model’s learning during training.