What is DeepSeek?, Is DeepSeek free?, Will DeepSeek be banned like TikTok?

DeepSeek is a Chinese AI company that has experienced rapid growth in 2024-2025 by developing powerful, relatively low-cost large language models (LLMs). It excels at complex reasoning, math, coding, and structured problem solving, often matching or surpassing other frontier models in benchmarks.

DeepSeek

DeepSeek is a Chinese artificial intelligence company (founded in 2023, based in Hangzhou) that develops large language models (LLMs) and AI systems. It is best known for its open-weight models like DeepSeek-V3 and DeepSeek-R1, which are designed for reasoning, coding, mathematics, and long-context tasks. Unlike many closed AI providers, DeepSeek emphasizes cost-efficient training, open accessibility, and transparency in model releases.

DeepSeek = an AI company + its family of advanced language models, focused on high reasoning ability, long input handling, and lower-cost training compared to rivals like OpenAI’s ChatGPT or Anthropic’s Claude. It is best for cost-effective reasoning, coding, research, and long-context use cases. Watch out for: reliability, censorship, and data governance issues.

What is DeepSeek?

• Full name: Hangzhou DeepSeek Artificial Intelligence Basic Technology Research Co., Ltd.
• Founded in July 2023 by Liang Wenfeng, who also co-founded High-Flyer, the hedge fund that funds DeepSeek.
• Headquartered in Hangzhou, Zhejiang, China.
• Its mission is to develop cutting-edge open-weight / open-source language models and AI tools.

What models & technology has DeepSeek built?

Some of DeepSeek’s key models and technical features:

• DeepSeek-R1: A reasoning-focused model released in early 2025. It’s made open-source (somewhat akin to open source), and demonstrates strong reasoning/problem-solving capabilities.
• DeepSeek-V3: Another of its more advanced models. It uses architectures such as Mixture-of-Experts (MoE), long context windows, etc. They also have the “DeepSeek Coder” series of models specialized for code and programming tasks.

What makes DeepSeek notable

DeepSeek has drawn attention for several reasons:

• Cost-efficiency: Their models are reportedly trained for far less money than many Western models. For example, they claim DeepSeek-V3 or its predecessors cost on the order of US$6 million to train; by contrast, GPT-4 and some other major models are believed to cost tens or hundreds of millions.
• Open/“open-weight” behavior: They publish model weights or parts thereof, use fairly permissive licensing, and emphasize transparency. This is in contrast to many proprietary models.
• Strong performance: DeepSeek’s models have been evaluated well on reasoning, coding, mathematics, etc., achieving results competitive with many of the top models from other AI labs.
• Rapid adoption & controversy: The app/chatbot version of DeepSeek became very popular (for example, on the iOS App Store) soon after release. But there have also been concerns raised about data privacy, security, and how user data may be handled. Some countries or regulators have raised flags.

Challenges & Criticisms

DeepSeek isn’t without challenges. Some of the issues reported:

• Privacy/data protection: In the EU, especially, data authorities have expressed concerns about how DeepSeek transfers or processes user data, and whether it meets GDPR and other regulations.
• Censorship/content filtering: There’s evidence that a version of DeepSeek (or DeepSeek-R1) has been modified (“DeepSeek-R1-Safe”) to enforce political content filtering, in alignment with Chinese regulations.
• Stability/scalability: Some users have reported that DeepSeek servers are often busy or overloaded.

Features of DeepSeek & DeepSeek-R1

• Mixture-of-Experts (MoE) architecture: DeepSeek-R1 has ~671 billion parameters total, but only around 37B are activated per token during inference. This sparsity helps reduce compute cost while retaining large model capacity. Earlier version DeepSeek-V2 also uses MoE, with fewer activated experts (≈ 21B) but still a large parameter count. 
• Large / Very Long Context Window: The model supports extremely long input contexts. For example, DeepSeek-R1 can handle up to 128,000 tokens in a single request. This allows processing of long documents, long conversations, codebases, etc.
• Strong performance on reasoning, math, code etc.: It has been benchmarked well on reasoning tasks, code generation, and math word problems. DeepSeek-Coder series specializes in code: Pretrained and instruction-fine-tuned models for coding tasks, with large context (16K in many models) and good performance. 
• Cost Efficiency: Because only part of the model activates per inference (MoE), DeepSeek claims a lower operational cost compared to densely-activated large models. Also, training appears more efficient: DeepSeek-R1 reportedly costs ~$6 million for base model training, plus lower relative costs for enhancements. 
• Open or “Open-weight” Model & Accessibility: DeepSeek’s models are made available under more open licensing (“open weight”), meaning users can access model weights, do distillation, fine-tuning, etc. There are distilled variants (smaller models) to allow usage on less powerful hardware. 
• Explainability & Reasoning Outputs: The R1 model includes chain-of-thought style reasoning — i.e. it can show more of “how it thought” through responses. This aids interpretability. Also, “DeepThink” mode, which toggles enhanced reasoning behavior. 
• Integration & Deployment: The model is supported via APIs, and is usable through web and mobile apps. It is available on Amazon Bedrock (customers can import their own fine-tuned versions), which facilitates production deployment. 
• Enhanced Benchmarks & Updates: There are upgraded versions (e.g. R1-0528) with improved factuality, reduced hallucinations, support for tool calling, structured outputs (JSON), etc. 

Limitations / Trade-offs (What to Watch Out For)

To balance the picture, these are issues that have been reported or are known:

• Hallucinations & reliability: Even though performance is good, some users find factual errors or overconfident but wrong statements. 
• Default model in UI isn’t R1: By default, users often use V3; R1 / “DeepThink” mode must be enabled explicitly. 
• Server load/availability: Many users report that the R1 model endpoint is overloaded and hard to access at times. 
• Safety, alignment concerns: As with many LLMs, concerns over bias, misuse, content filtering, and sensitive content. Also, specialized “Safe” versions are being developed (e.g. DeepSeek-R1-Safe) to improve in these areas. 

Advantages of DeepSeek

• High Reasoning Ability: DeepSeek-R1 excels at complex reasoning, math, coding, and structured problem solving, often matching or surpassing other frontier models in benchmarks.
• Efficient Architecture (MoE): It uses a Mixture-of-Experts approach — only a fraction of parameters activate per inference. This reduces compute cost and makes it more scalable.
• Long Context Window: It handles up to 128K tokens, allowing it to process long documents, conversations, or entire codebases in one go.
• Cost-Effective Training & Inference: Trained with significantly less money than Western rivals (tens of millions vs. hundreds of millions), while maintaining strong performance.
• Open-Weight Accessibility: Unlike fully closed models, DeepSeek releases weights or provides permissive licensing, enabling community fine-tuning, distillation, and research.
• Specialized Models (Coder, Reasoning, etc.): Variants like DeepSeek-Coder are optimized for programming tasks, making them very competitive in software development workflows.
• Rapid Adoption & API Support: It is available via API, mobile apps, and platforms like Amazon Bedrock, making it easier for businesses and developers to deploy.
• Explainability Features: It supports chain-of-thought reasoning and “DeepThink mode,” which provides more transparent reasoning traces.

Disadvantages of DeepSeek

• Reliability & Hallucinations: Like other LLMs, it can produce incorrect or fabricated answers, sometimes with high confidence.
• Server Load & Availability Issues: High demand means the R1 model is often slow or unavailable on public endpoints.
• Censorship / Alignment Constraints: Some versions (e.g., DeepSeek-R1-Safe) are aligned with strict content filtering rules, especially for political or sensitive topics, which may limit open discussion.
• Privacy & Data Concerns: Regulatory agencies in places like the EU have raised concerns about data protection and compliance with GDPR.
• Rapid Updates = Fragmentation: Multiple versions (V2, V3, R1, R1-0528, etc.) can confuse users about which is best or most stable for their use case.
• Limited Ecosystem vs. Western Rivals: While growing fast, its ecosystem (plugins, integrations, developer community) is still smaller compared to OpenAI, Anthropic, or Google.
• Bias & Safety Risks: As with all large AI models, there are risks of biased outputs, misuse (e.g., misinformation, cyber use), and security vulnerabilities.

Why DeepSeek is being banned

1. Data protection/privacy concerns: Officials worry that DeepSeek collects a lot of user data (chat histories, files, etc.) and stores it on servers in China. There are concerns that DeepSeek has not demonstrated sufficient safeguards to ensure that the data of users (especially outside China) is protected to the standards required by laws such as the European Union’s General Data Protection Regulation (GDPR).
2. Unlawful cross-border data transfers: Authorities say that DeepSeek is transferring user data to China without having put in place legal or technical safeguards to ensure equivalent protection as mandated by data protection laws in many countries.
3. National security/government risk: Because DeepSeek is a Chinese company, there are worries that Chinese laws (e.g. intelligence, cybersecurity, and national intelligence laws) might require it to share data with authorities in China. That raises the risk that sensitive or private data could be accessed by state bodies without adequate oversight. Government agencies in several places have banned the use of DeepSeek on government devices over fears that it could be used for espionage or unauthorized data access.
4. Lack of transparency: Regulators argue that DeepSeek has not clearly explained how user data is processed, how long it is stored, whether third parties have access, or whether users have robust rights concerning their data.
5. Potential for censorship or biased / manipulated content: Some academic audits and investigations have shown that DeepSeek may suppress certain politically sensitive information or content in its outputs, especially relating to government accountability, transparency, etc. There are concerns that the model’s responses could be influenced or aligned with state policies from where it is hosted, which might limit freedom of expression or provide biased information.

Examples of where and how it’s being banned/restricted

• Germany: Its data protection authority has asked Apple and Google to remove DeepSeek from their app stores because it transfers German users’ data in ways that violate GDPR, and the company hasn’t shown equivalent protections.
• South Korea: Removed DeepSeek from app stores while the company works to fix privacy issues.
• Australia: Banned DeepSeek from government devices, citing national security risks.
• Italy: The Data privacy regulator ordered a limitation on processing Italian users’ data and app removal from stores.
• Taiwan: Government departments banned DeepSeek due to data security/information leak concerns.

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