DeepSeek, a relatively unknown Chinese startup, has recently captured global attention with the surprisingly impressive unveiling of its advanced artificial intelligence models. By offering models developed with extraordinary efficiency and made available as open-source, the company has presented a significant challenge to major players in the AI arena, including OpenAI, Meta, and Google.
DeepSeek: A Rising Star
DeepSeek AI was founded in May 2023 by Liang Wen-feng, a well-known figure in the quantitative investment fund and AI industries. The company operates independently and is financed by High-Flyer, a quantitative investment fund also founded by Wen-feng. This unique funding model has allowed DeepSeek to pursue ambitious AI projects without the pressures of external investors, prioritizing long-term research and development.
The DeepSeek team is primarily composed of talented young graduates from top Chinese universities who foster a culture of innovation and possess a deep understanding of the Chinese language and culture. The company’s recruitment practices are noteworthy, favoring technical skills over traditional work experience. This has enabled DeepSeek to assemble a team of highly skilled individuals with fresh ideas in AI development.
DeepSeek’s Journey: From Inception to Transforming the AI Market
DeepSeek’s journey began with the release of DeepSeek Coder in November 2023, an open-source model designed for coding tasks. Subsequently, the company unveiled DeepSeek LLM with 67 billion parameters, which it developed to compete with other large language models.
In May 2024, DeepSeek once again garnered attention with the introduction of DeepSeek-V2. This model was notable not only for its strong performance but also for its low development and usage costs. This move sparked a price war in the Chinese AI model market.
DeepSeek’s disruptive pricing strategy put pressure on major Chinese technology companies such as ByteDance, Tencent, Baidu, and Alibaba to lower the prices of their AI models to remain competitive. This shift demonstrates DeepSeek’s innovative power in the market and its impact on the Chinese technology ecosystem.
The DeepSeek-Coder-V2 model, which succeeded DeepSeek-V2, is an advanced model with 236 billion parameters. It is designed for complex coding challenges and can process text in the longest context length sizes, reaching 128,000 tokens. One of the key features of this model is its affordable API, which allows developers to harness its power at a low cost. The cost of using this API is as follows:
- $0.14 per million input tokens
- $0.28 per million output tokens
With its massive scale and ability to process large volumes of information, this model can effectively assist in solving complex coding problems and large projects. On the other hand, its low API costs enable companies and startups to leverage the model’s capabilities without incurring huge costs, thereby increasing their competitiveness.
The company’s new models, DeepSeek-V3 and DeepSeek-R1, have further solidified its position as a disruptive and transformative force in the world of AI. DeepSeek-V3, designed with 671 billion parameters, has demonstrated amazing performance in various tests while requiring significantly fewer resources than its competitors. This makes DeepSeek-V3 a more cost-effective and efficient choice compared to similar models.
The DeepSeek-R1 model, introduced in January 2025, is specifically focused on reasoning tasks, and with its advanced capabilities, it has become a serious challenge and competitor to OpenAI’s o1 model, especially in the field of complex analyses. This model offers innovative capabilities, especially in solving reasoning problems and making intelligent decisions, which sets it apart from other competitors.
With these features, these two new models have not only increased DeepSeek’s competitive power, but they can also have profound impacts on the world of technology and language models. From DeepSeek-V3 with its high processing power to DeepSeek-R1 with its focus on logical reasoning, these models represent significant advancements and innovative capacities that are driving the AI market toward a new future.
DeepSeek AI also offers a collection of distilled models called DeepSeek-R1-Distill, which are based on well-known and open-access models like Llama and Qwen. These models were created after fine-tuning synthetic data generated by R1. These distilled models offer various levels of performance and efficiency that meet different computing needs and hardware configurations.
While DeepSeek has achieved remarkable success in a short period, it is worth noting that the company is primarily focused on research and currently has no specific plans for widespread commercialization soon.
DeepSeek’s Strategic Partnerships
DeepSeek’s success is not limited to the company’s internal efforts. By establishing strategic collaborations, it has been able to enhance its technological capabilities and market access. One of the company’s notable collaborations is its partnership with AMD, a leading provider of high-performance computing solutions. DeepSeek uses AMD Instinct GPUs and ROCM software in key stages of its model development, especially for DeepSeek-V3. This partnership provides DeepSeek with access to advanced hardware and an open software suite that enables performance optimization and scalability.
DeepSeek’s Innovative Techniques
DeepSeek’s success is attributed to several key innovations, which are described below:
Reinforcement Learning
Unlike traditional methods that rely primarily on supervised fine-tuning, DeepSeek uses pure reinforcement learning, which allows models to learn through trial and error and improve by receiving algorithmic rewards. This approach has been particularly effective in developing DeepSeek-R1’s reasoning capabilities. In fact, DeepSeek’s models learn by interacting with their environment and receiving feedback on their performance; that is, something similar to how humans learn through experience. This process enables models to develop more complex reasoning abilities and adapt more effectively to new situations.
Mixture of Experts Architecture
DeepSeek models use a mixture of experts’ architecture that activates only a small portion of its parameters for each task. This selective activation significantly reduces computational costs and increases efficiency. Imagine a team of experts, each specializing in a specific field. When faced with a task, only the relevant experts are called upon, which leads to optimal use of resources and expertise. DeepSeek’s MoE architecture works in the same way, saving significant costs and improving performance by activating (only) the necessary parameters for each task.
Multi-Head Attention
The DeepSeek-V3 model uses multi-head attention, which improves the model’s ability to process data by identifying complex relationships and managing different aspects of the input simultaneously. Imagine that there are multiple “attention heads,” each of which can focus on different parts of the input data so that the model can gain a more comprehensive understanding of the information. This advanced mechanism has contributed to DeepSeek-V3’s impressive performance in conducting assessments and comparisons.
Knowledge Distillation
DeepSeek utilizes knowledge distillation techniques to transfer the capabilities and knowledge of larger models to smaller, more efficient models. This process makes powerful AI accessible to a wider range of users and devices.
This process can be likened to an experienced teacher passing on their knowledge to their students. In this way, students can perform tasks with a similar level of skill and accuracy with fewer resources and limited experience. The distillation process at DeepSeek allows smaller models to gain advanced capabilities in reasoning and language processing, similar to their larger counterparts while being lighter, faster, and more optimized.
The combination of these innovative techniques with DeepSeek’s focus on optimization and open-source collaboration has made the company a transformative option in the world of AI.
Affordable Approach
DeepSeek’s commitment to cost-effectiveness is evident in its development and pricing strategies.
Reducing Training Costs
By leveraging reinforcement learning and efficient architectures such as MoE, DeepSeek significantly reduces the computational resources required to train its models, resulting in substantial cost savings. For example, the DeepSeek-V3 model was trained at a much lower cost than similar models from Meta. Although the reported figure of $5.5 million only includes a portion of the total training costs, this demonstrates DeepSeek’s ability to achieve high performance with less financial investment.
This approach helps DeepSeek compete with other major brands in the field of AI at a more reasonable cost, and it also finds the ability to achieve optimal results while saving on computing resources and costs.
Affordable API Pricing
DeepSeek’s API pricing is significantly lower than its competitors. This makes its models more accessible to small businesses and developers who have limited financial resources and cannot invest in expensive, proprietary solutions. For example, the cost of the DeepSeek-R1 model’s API is only 0.55 per million input tokens and 2.19 per million output tokens, while for the APIs of large companies like OpenAI, the costs are 15and15and 60, respectively.
DeepSeek Open-Source Model
DeepSeek’s open-source approach significantly contributes to its cost-effectiveness by eliminating licensing fees and encouraging community-driven development. This allows developers to freely access, modify, and use DeepSeek’s models. As a result, financial barriers to entry into this field are reduced and the adoption of advanced AI technologies is broadened.
With this strategy, DeepSeek helps promote collaboration and knowledge exchange within the developer community while providing an opportunity for better access to complex technologies. This method not only reduces costs but also expands the use of AI in various industries.
DeepSeek’s Impact on the AI Landscape
Competitive Impact
DeepSeek’s entry into the AI market has placed significant competitive pressure on established giants such as OpenAI, Google, and Meta. By offering low-cost and open-source models, the company is forcing these major players to either lower their prices or enhance their offerings to remain competitive. This increased competition will likely lead to cheaper and more accessible AI solutions for businesses and consumers.
In addition, DeepSeek’s pricing strategy has triggered a price war in the Chinese AI model market, which is forcing other Chinese technology giants to re-evaluate and adjust their pricing structures. This action demonstrates DeepSeek’s ability to disrupt stable markets and influence pricing dynamics.
DeepSeek’s Impact on the AI Community
DeepSeek’s commitment to open-source models has democratized access to advanced AI technologies, enabling a wide range of users, including small businesses, researchers, and developers, to use innovative AI tools. This widespread access promotes innovation and helps shape a more diverse and dynamic AI ecosystem. By promoting collaboration and knowledge sharing, DeepSeek enables a wider community to participate in AI development, thereby accelerating progress in this field. Furthermore, DeepSeek’s open-source approach creates greater transparency and accountability in AI development.
By making its models and training data publicly available, the company improves thorough scrutiny and allows the community to identify and address potential biases and ethical issues. This transparency will lead to more responsible and ethical advancements in the field of AI.
Shift in Priorities
DeepSeek’s success places a great emphasis on the increasing importance of algorithmic efficiency and resource optimization in AI development. Instead of relying solely on bulky scaling, DeepSeek shows that high performance can be achieved with far fewer resources, challenging the traditional belief that larger models and data are inherently superior. This shift in approach encourages the AI community to seek more innovative and sustainable solutions in AI development.
DeepSeek’s focus on efficiency has positive environmental impacts. Given the growing concerns about the carbon footprint of AI, DeepSeek’s methods contribute to more sustainable practices in AI by reducing energy consumption and minimizing the use of computing resources.
Strategic Timing
The recent releases of DeepSeek products, especially the release of the DeepSeek-R1 model, appear to be timed to coincide with major geopolitical events, namely Donald Trump’s inauguration, which is completely strategic timing. This timing represents a deliberate effort to challenge the prevailing view of U.S. dominance in the field of AI and to showcase China’s growing capabilities in this area. By releasing products in conjunction with such events, DeepSeek intends to present itself as a strong competitor on the global stage and to showcase the rapid advancements and strategic initiatives undertaken by Chinese AI developers.
DeepSeek’s Functional Challenges
Computational Power
Despite its impressive achievements, DeepSeek faces a major challenge in the field of computing power and is in a weaker position in this regard compared to its American competitors. This shortage is particularly exacerbated by U.S. export restrictions on advanced chips, which limit DeepSeek’s access to the new hardware needed to develop and run more powerful AI models.
Despite the remarkable efficiency that DeepSeek has demonstrated in its operations, access to more advanced computing resources could accelerate the company’s progress and strengthen its competitiveness against companies that have more computing resources. Closing this computational gap is essential for DeepSeek to scale its innovations and compete more effectively globally.
Market Understanding
DeepSeek may face challenges in establishing the same level of trust and recognition as well-established and leading companies like OpenAI and Google. Building a strong brand reputation and overcoming skepticism about its affordable solutions is of particular importance to DeepSeek’s long-term success. To gain acceptance and attract more users, DeepSeek must demonstrate a consistent track record of reliability and high performance. Strengthening market understanding through effective branding and proven results will play a key role in differentiating the company from its competitors and attracting a loyal customer base.
Competitive Landscape
The AI market is highly competitive, and major players are constantly innovating and releasing new models. To stay ahead, DeepSeek must maintain a high pace of development and constantly differentiate its offerings. This requires continuous innovation and a focus on unique capabilities that can set the company apart from others in the field. By prioritizing the development of distinctive features and being agile in responding to market trends, DeepSeek can maintain its competitive edge and overcome the challenges of a rapidly evolving industry.
Censorship
DeepSeek’s models are subject to censorship due to the prevention of criticism of the Chinese Communist Party, which is a major challenge to its global adoption. In countries where freedom of expression is highly valued, this censorship can limit DeepSeek’s appeal and acceptance. Addressing these concerns is critical to DeepSeek’s international development. Balancing censorship requirements with the need to develop open and unbiased AI solutions will be crucial. Finding ways to overcome these limitations while maintaining the integrity and performance of the models will help DeepSeek achieve broader adoption and success in various markets.
Weakness in Search and Responsiveness
DeepSeek, as a tool that uses the word “deep” in its name, creates expectations such as providing accurate, documented, and reliable searches and responses. But, our experience has shown that this tool still has weaknesses in providing valid and factual results. This became more apparent to us when we decided to evaluate DeepSeek’s ability compared to more advanced tools like ChatGPT and Copilot.
For this test, we asked all three tools a specific and simple question with the same prompt and received accurate, documented responses from ChatGPT and Copilot with references to credible sources. These two tools were able to provide information that was well aligned with our knowledge of the subject matter, further increasing our confidence in the ability of these tools to analyze and provide documentation.
But DeepSeek’s response was completely different. Contrary to expectations, the tool provided responses that were largely far from our knowledge and often unrealistic. In addition, DeepSeek did not even make the slightest mention of the sources or data on which it had based its conclusion.
This weakness in DeepSeek’s performance, especially compared to tools like ChatGPT and Copilot, shows that the technology has not yet reached sufficient maturity in searching, analyzing information, and providing verifiable responses. A tool that users are expected to trust must present its results not only based on credible databases but also with complete transparency regarding sources and foundations.
However, it should be noted that DeepSeek, like many emerging technologies, is at the beginning of its journey, and this type of weakness can be part of its growth and evolution process. It is expected that with improved model performance, the use of more credible data sources, and the strengthening of their analytical capabilities, we will see significant improvements in DeepSeek’s performance. In the future, this tool may become a powerful and reliable tool in analyzing and providing accurate information instead of providing unrealistic and undocumented answers.
Finally, the comparison of the three AI tools showed us that choosing the right tool for searching and analyzing information can have a significant impact on obtaining correct and usable results. While ChatGPT and Copilot were able to gain our trust through their strong performance, DeepSeek still needs fundamental changes to reach such a level of accuracy and credibility.
The Future of DeepSeek
The emergence of DeepSeek as a transformative force in the AI landscape is undeniable. Its innovative techniques, affordable solutions, and optimization strategies have challenged the status quo and forced established players to reconsider their approaches. While DeepSeek faces challenges, its commitment to open-source collaborations and efficient AI development has the potential to change the future of the industry. As competition intensifies in the field of AI, DeepSeek’s journey will be closely followed.
