DeepSeek: the new model competing with ChatGPT - review and capabilities

Founded in China in 2023, DeepSeek quickly attracted the attention of the global AI community. The DeepSeek-R1 model and its later versions gained popularity for their technical performance and open-weight strategy, making them direct competitors to ChatGPT and other large language models. This article explores DeepSeek’s architecture, compares it with ChatGPT, and highlights its strengths, limitations, and applications.

Architecture and Key Features

DeepSeek-R1 and the V3 series use the mixture-of-experts (MoE) approach, hybrid architectures, and advanced training methods. For example, DeepSeek-V3 is described as an MoE model with 671 billion parameters, of which about 37 billion are activated per token. The V3.1 version introduces a “hybrid inference: Think & Non-Think” mode, allowing either fast or deep reasoning. Moreover, DeepSeek models are designed for efficiency — fewer GPUs, lower cost, and open access.

Comparison with ChatGPT

One of the most notable developments was that DeepSeek R1 achieved results close to OpenAI o1 in mathematics, coding, and logic benchmarks. DeepSeek accomplished this with significantly lower costs and computing power, sparking major discussion in the tech community.
However, ChatGPT still maintains a more mature ecosystem, greater stability, and broader integrations.

Main Advantages of DeepSeek

• Open weights and transparency — developers can study, modify, and deploy DeepSeek models freely.

• Efficiency — lower training costs and fewer computing resources compared to massive closed models.

• Strong logic and coding skills — DeepSeek-R1 performed well in reasoning and math tasks.

• Competitive pricing — challenges the dominance of commercial AI systems.

Limitations and Risks

• Security and data privacy concerns — some governments are cautious about data flowing into Chinese platforms.

• Content filtering — reports indicate that DeepSeek restricts political or sensitive topics under Chinese regulations.

• Regulatory barriers — the model has faced restrictions in several countries due to security issues.

• New and less tested — it lacks the maturity and reliability of ChatGPT.

Practical Use Cases

DeepSeek is already applied in education, healthcare, code generation, and analytics. For example, DeepSeek-R1 has passed several medical benchmarks and assists in clinical decision-making. In business, it competes with automation and content-generation tools.
End users can access it through a web interface or mobile apps.

Which to Choose — DeepSeek or ChatGPT?

If openness, cost efficiency, or local deployment are your priorities, DeepSeek can be a compelling option. If you value stability, integrations, and ecosystem support, ChatGPT remains the more dependable choice.
Businesses should assess security, compliance, and regulatory factors before adopting DeepSeek for critical operations.

Core Insight

DeepSeek represents a major challenge to the dominance of large language models, emphasizing openness and efficiency.
It proves that powerful LLMs can be built with fewer resources and open data, though it still faces regulatory scrutiny and ecosystem trust issues.
The choice between DeepSeek and ChatGPT depends not just on technology — but on your priorities for safety, scalability, and control.