What Is DeepSeek R1?

DeepSeek R1 is a language model developed by the Chinese company High-Flyer AI as a competitor to ChatGPT, Claude, and O1. It uses a Mixture of Experts (MoE) architecture, which improves performance and optimizes computing resource usage. This makes it effective for content generation, data analysis, and supporting developers.

Its development aligns with China’s strategy for technological independence in AI. The model is available as open-source, allowing users to experiment and adapt it to their needs. It offers functionalities comparable to commercial solutions – but without the cost.

Who Owns DeepSeek R1?

DeepSeek R1 was created by High-Flyer AI, a Chinese company founded in 2023 in Hangzhou by Liang Wenfeng, an expert in language models. The company is funded by both private investors and government institutions, allowing it to develop competitive AI solutions.

High-Flyer AI’s strategy focuses on building advanced models available to a broad audience – from individual users to companies. Making the model open-source accelerates its adoption and increases brand visibility in the global AI sector.

Why Is DeepSeek R1 Free?

DeepSeek R1 is free because High-Flyer AI has adopted an open-source strategy, similar to Meta’s approach with its Llama models. Free access boosts the model’s popularity and supports faster community growth.

A key factor is also China’s AI strategy – the government supports the development of domestic models as alternatives to OpenAI, Google, and Anthropic. Although DeepSeek R1 is free, High-Flyer AI can generate revenue from paid implementations, training, technical support, and integrations with Chinese tech platforms.

What Can DeepSeek R1 Do?

DeepSeek R1 is a versatile AI model capable of handling a wide range of natural language processing, coding, and data analysis tasks. Thanks to its Mixture of Experts (MoE) architecture, it activates only the necessary parts of the neural network, improving efficiency and reducing resource consumption.

One of DeepSeek R1’s main applications is text generation – the model writes articles, reports, summaries, and analyses, adjusting tone based on context. In programming, it supports languages like Python, JavaScript, C++, and Java, helping with code generation, optimization, and bug analysis.

DeepSeek R1 also excels in data analysis – it summarizes large data sets, identifies key insights, and supports reporting. It can be used in finance, medicine, and marketing, where quick analysis of large data volumes is essential.

Companies can integrate the model with CRM, ERP, and chatbots, automating customer service and sales processes. Its capabilities make it a viable alternative to commercial AI solutions.

How Is DeepSeek R1 Different from Other AI Models?

DeepSeek R1 stands out with its MoE architecture, which activates only the necessary components of the neural network, reducing computing power consumption and speeding up response generation. Unlike GPT-4, which processes the entire network at once, DeepSeek R1 operates more efficiently.

The second key difference is the model’s open-source nature. While OpenAI, Anthropic, and Google release their models within closed ecosystems, DeepSeek R1 can be self-hosted, customized, and integrated with existing systems.

DeepSeek R1 outperforms some models in code generation, optimization, and bug analysis. Its integration with development tools makes writing and debugging code easier. When generating long-form content, it benefits from an extended context window, improving the coherence of analyses and reports.

How to Use DeepSeek R1?

The model is available both in the cloud and locally, giving users flexibility in deployment. The simplest option is the online version at chat.deepseek.com, where it works like a chatbot – just enter a query to receive a response.

Advanced users can leverage the API, which allows DeepSeek R1 to be integrated with applications and used for automated query processing. The API documentation is available on DeepSeek AI’s official website.

The model can also be run locally, which is beneficial for companies seeking full control over their data. DeepSeek R1 can be deployed on a private server or in a GPU environment, though this requires suitable hardware and proper configuration.

Thanks to various deployment options, DeepSeek R1 is suitable for both individual and business applications.

DeepSeek R1 in Visual Studio Code

DeepSeek R1 can be integrated with Visual Studio Code (VS Code). It supports code generation, bug analysis, and script optimization, helping automate programming processes. To use DeepSeek R1 in VS Code, you can connect via the model’s API or use available Python scripts. The open-source community is currently working on dedicated plugins that will simplify integration in the future.

Key DeepSeek R1 Features in VS Code:

• Code generation – suggests code snippets based on short descriptions, similar to GitHub Copilot.
• Bug analysis – detects errors and suggests fixes, speeding up debugging.
• Code optimization – proposes more efficient solutions and better practices.
• Automatic documentation – generates comments and explanations for the code.

Can DeepSeek R1 Generate Images?

Currently, DeepSeek R1 does not support image generation, as it is a natural language processing model (LLM). Unlike multimodal models like GPT-4 Turbo or Gemini 1.5, it focuses solely on text analysis and generation. However, it can assist in the image creation process by generating precise prompts for tools like Stable Diffusion, MidJourney, or DALL·E.

There is no official information yet on whether High-Flyer AI plans to expand the model with image generation capabilities, but the development of multimodal AI may influence future updates to DeepSeek.

Can DeepSeek R1 Create PPTX and DOCX Files?

DeepSeek R1 does not natively generate PPTX or DOCX files, but it can assist in creating them by preparing ready-to-copy content for Microsoft Word and PowerPoint. It supports:

• Slide structuring – dividing presentations into logical sections.
• Bullet point generation – concise summaries ready for slides.
• Report and documentation generation – text formatted for use in Word.

To automatically save generated content as PPTX or DOCX, you can combine DeepSeek R1 with Python libraries such as python-docx (Word) and python-pptx (PowerPoint). This allows for quick conversion of content into the correct file formats.

While DeepSeek R1 doesn’t save files directly, when paired with the right tools, it can significantly speed up the document creation process.

DeepSeek R1 vs ChatGPT – Which Tool Should You Choose?

DeepSeek R1 and ChatGPT are competing AI models with different approaches to availability and deployment. ChatGPT (especially GPT-4 Turbo) operates in a closed ecosystem and requires a subscription, while DeepSeek R1 is free and open-source, allowing users to self-host and modify the model.

Comparison of DeepSeek R1 and ChatGPT:

• Content Quality – ChatGPT handles long context better and provides more consistent responses.
• Deployment Flexibility – DeepSeek R1 can be run locally, reducing costs and removing reliance on cloud providers.
• Use Cases – ChatGPT excels at long-form content analysis and creative writing, while DeepSeek R1 is better suited for developers and data analysts.

AI Act and Implementing DeepSeek R1 in a Company

EU AI regulations classify systems based on risk levels – learn more.

DeepSeek R1, as open-source, is not prohibited, but deploying it in Europe requires compliance with the AI Act and GDPR, especially in high-risk areas such as recruitment, biometric analysis, or credit scoring. Companies using this model must not only comply with new regulations but also implement control mechanisms to minimize legal and ethical risks.

Company Obligations When Deploying DeepSeek R1

Transparency – any company using DeepSeek R1 for decision-making must document how the model works, identify data sources, and specify decision-making mechanisms. If personal data is processed, end users must be informed. In some cases, explicit user consent may be required under GDPR.

• Risk Control – companies must implement audit procedures to limit bias and erroneous decisions. This includes:
• Training data validation – verifying the model isn’t biased against specific user groups.
• Algorithm testing – analyzing AI-generated decisions and eliminating systemic errors.

• Error reporting – establishing a system to report incorrect results and enable manual intervention.

Content Responsibility – companies must monitor outputs generated by the AI in areas that affect end users. If DeepSeek R1 is used for document analysis, content generation, or evaluation processes, the company must have oversight mechanisms to prevent misleading recommendations or inaccurate content.

AI Act and Open-Source – Does It Limit Development?

The AI Act does not ban open-source AI, but it requires strict oversight when implemented within companies. If a system is used in high-risk areas, the company is fully responsible for its impact. Non-compliance can lead to severe financial penalties, and in extreme cases – a ban on using AI in specific processes. The regulations don’t eliminate open-source AI but change how companies are legally allowed to implement it.

As a result, organizations can no longer deploy models like DeepSeek R1 without conducting a risk analysis and ensuring regulatory compliance. The AI Act forces a more thoughtful and controlled approach to open-source AI, especially in sectors with high levels of accountability.

DeepSeek R1 vs O1 – Which AI Is Better?

DeepSeek R1 and O1 are both performance-optimized models, but they differ in architecture and licensing approach.

Architecture – DeepSeek R1 uses the Mixture of Experts (MoE), which activates only selected parts of the model, reducing resource consumption. O1 focuses on processing speed for large text datasets.

Availability – DeepSeek R1 is fully open-source, while O1 operates under a more restrictive license.

Use Cases – DeepSeek performs better in code generation and data analysis, whereas O1 excels in tasks like strategic planning, causal analysis in complex business processes, and multi-dimensional recommendation generation.

Choose DeepSeek R1 if you need full control over your AI system. O1 may be the better choice for companies seeking a ready-to-use business solution with specialized capabilities.

DeepSeek R1 vs Claude – Which Model to Choose?

Claude (Anthropic) and DeepSeek R1 differ significantly in how they generate content and process information.

Context Length – Claude supports up to 100,000 tokens, making it ideal for analyzing long documents.

Licensing – Claude is a closed-source model, while DeepSeek R1 can be locally deployed.

Text Quality – Claude produces more natural-sounding responses, whereas DeepSeek R1 excels in programming tasks and data analysis.

Claude is the better choice for users who need extended context and precise answers in complex language tasks. DeepSeek R1 is a strong option for developers and companies that want to self-host the model, tailor it to specific requirements, and process data without third-party cloud providers.

Comparison of DeepSeek R1 with Other Language Models

DeepSeek R1 stands out for:

1. Open-source availability
2. Efficient architecture (MoE)
3. Strong developer and data analyst support
4. Flexible deployment options (cloud/local)

However, the best choice always depends on your specific needs – whether it’s context length, fine-tuning freedom, licensing, or model specialization.

Conclusions and Future of DeepSeek R1

DeepSeek R1 is one of the most promising open-source AI models, offering free access and high deployment flexibility. Thanks to its Mixture of Experts (MoE) architecture, it consumes fewer resources than traditional LLMs and performs well in code generation and data analysis.

Challenges:

• Lack of multimodality – it cannot generate images, unlike ChatGPT or Gemini.
• EU regulations – the AI Act requires stricter control over implementation in business settings.

Potential Development Directions:

1. Extended context handling – competitors are already implementing this.
2. Integration with multimodal tools – adding image generation capabilities.
3. Better optimization for developers and businesses – stronger support for enterprise applications.

In summary, DeepSeek R1 is a robust alternative to closed-source AI models, but its future will depend on how quickly it adapts to growing market demands and regulatory requirements.