How DeepSeek is Reshaping the AI Landscape

What is DeepSeek?

An AI development firm based in Hangzhou, China, DeepSeek, has sent major shockwaves through the global tech industry with the release of its open-source reasoning model, R1 [1][2]. Since its introduction in January this year, the AI model has gained massive popularity and media attention, captivating both tech enthusiasts and industry professionals alike. Within just days of its release, DeepSeek’s AI assistant (A mobile app that provides a chatbot interface for the R1 model) soared to the top of Apple's App Store chart, surpassing OpenAI's ChatGPT app in popularity [1]. The company uses a different approach to train its R1 models compared to its US-based competitor OpenAI, which requires less time, fewer AI accelerators, and lower development costs [1]. Similar to OpenAI-o1 models, DeepSeek's R1 is a "reasoning" model that produces responses incrementally, simulating a process similar to how humans reason through problems or ideas [3]. One of the defining characteristics of DeepSeek R1 is its open-source nature [4]. Developers can fully access the model’s architecture, allowing them to tweak its functionalities and fine-tune it for specific industry needs [4]. Similar to other Chinese AI models, DeepSeek is also trained to avoid politically sensitive questions or topics [3]. In this article, we’ll explore the key features of DeepSeek's R1 model and see how it compares to OpenAI’s latest models.

DeepSeek’s Innovative Training Techniques

In a research paper, DeepSeek outlines several key innovations developed as part of the R1 model [1]. One significant advancement is the application of large-scale reinforcement learning, specifically designed to tackle complex reasoning tasks. Unlike typical neural reward models, DeepSeek's rule-based reward system is also designed to provide more effective incentives that guide the model's learning process, ensuring better performance in reasoning tasks [1]. Reward engineering is a critical aspect of AI training, as it helps shape the learning dynamics by specifying what behaviors the model should prioritize.

DeepSeek also employs distillation techniques, transferring the capabilities of larger models into smaller, more efficient ones. This makes advanced AI more accessible and adaptable for a wider range of devices and users [1][5]. Through effective knowledge transfer, the researchers managed to compress the model's capabilities into a smaller, more manageable size, with models as small as 1.5 billion parameters [1]. Furthermore, DeepSeek made a breakthrough with its emergent behavior network, which revealed that complex reasoning patterns can develop naturally through reinforcement learning without explicitly programming them [1].

DeepSeek Vs. OpenAI Comparison

DeepSeek and OpenAI have adopted different strategies in terms of model development and associated costs. DeepSeek’s flagship model, DeepSeek-R1, was developed with a strong emphasis on efficiency and affordability, requiring less than $6 million in computing resources for training [2]. This was achieved through the use of innovative training techniques and optimized algorithms that allowed the model to deliver high performance without incurring substantial expenses. On the other hand, OpenAI’s models, including the widely recognized GPT-4, are known for their much higher development costs, estimated to be in the hundreds of millions of dollars. This high expenditure reflects the vast computational power and extensive data sets required to train these models, positioning OpenAI as a more resource-intensive player in the AI space [2].

In terms of performance, DeepSeek-R1 excels in specific areas such as mathematical reasoning and coding tasks, where it has outperformed OpenAI's models in certain benchmarks [2]. For instance, on the American Invitational Mathematics Examination (AIME) 2024, DeepSeek-R1 achieved an accuracy of 79.8%, slightly surpassing OpenAI's GPT-4 at 79.2%. However, OpenAI’s models, such as GPT-4, are highly regarded for their versatility and general-purpose capabilities, performing well across a broad range of applications, including natural language processing, translation, and creative writing. In the Massive Multitask Language Understanding (MMLU) benchmark, OpenAI’s models have demonstrated higher accuracy rates, indicating their strength in handling diverse subject areas and complex tasks. As a result, while DeepSeek-R1 shows impressive performance in targeted domains, OpenAI’s models offer a wider breadth of knowledge and functionality [2].

One of the key differences between DeepSeek and OpenAI lies in their approach to model accessibility. DeepSeek has adopted an open-source strategy, fully releasing DeepSeek-R1 to the public and allowing developers to access, modify, and deploy the model freely [2]. This open-source approach fosters collaboration and accelerates innovation, contributing to the broader AI community. In contrast, OpenAI has moved towards a more proprietary model, restricting access to its GPT-4 and similar models to commercial partnerships and paid API access [2]. This controlled approach enables OpenAI to maintain oversight over the deployment and use of its models, prioritizing safety and ethical concerns. The contrasting business models have also influenced the market, with DeepSeek’s cost-effective, open-source model prompting reactions from investors and leading to significant shifts in the tech industry, while OpenAI remains a dominant force with its wide adoption across various sectors, including collaborations with major companies like Microsoft [2]. In conclusion, DeepSeek and OpenAI represent different strategies in AI development. DeepSeek emphasizes cost-efficient, open-source models with a focus on specialized tasks, especially in reasoning and coding [2]. On the other hand, OpenAI develops powerful, flexible models suitable for a wide range of applications, operating under a more proprietary approach [2].

Conclusion

DeepSeek has undeniably emerged as a game-changer in the AI sector. With its groundbreaking training methods, cost-efficient solutions, and commitment to open-source development, the company has challenged traditional approaches and prompted established players to rethink their strategies [5]. DeepSeek’s focus on efficiency and specialized capabilities is reshaping the landscape, pushing the boundaries of what’s possible in AI development.

Notes and References

1. Kerner, S. M. (2025, January 30). DeepSeek Explained: Everything You Need to Know - TechTarget. https://www.techtarget.com/whatis/feature/DeepSeek-explained-Everything-you-need-to-know
2. DeepSeek Vs OpenAI Explained. (2025, January 27) - PlayHT. https://play.ht/blog/deepseek-vs-openai/
3. Ng, K., Drenon, B., Gerken, T., & Cieslak, M. (2025, January 27). What is DeepSeek - and Why is Everyone Talking About It? - BBC. https://www.bbc.com/news/articles/c5yv5976z9po
4. Key Features of DeepSeek and How DeepSeek Works? (2025) - DeepSeek. https://opdeepseek.com/key-features-of-deepseek/
5. MSV, J. (2025, January 26). All About DeepSeek - The Chinese AI Startup Challenging The US Big Tech - Forbes. https://www.forbes.com/sites/janakirammsv/2025/01/26/all-about-deepseekthe-chinese-ai-startup-challenging-the-us-big-tech/