DeepSeek: what it is, how it works, models, app and differences from ChatGPT

DeepSeek vs ChatGPT: what is the difference?

DeepSeek and ChatGPT belong to the same broad category of generative AI assistants, but they are not the same product and do not use the same models.

The most evident difference concerns the technological and commercial ecosystem.

ChatGPT (which we have covered in a dedicated article) is the assistant developed by OpenAI and brings together a very wide range of capabilities, tools and services.

DeepSeek, by contrast, is the ecosystem developed by the Chinese company of the same name, built with a strong focus on language model research, reasoning, efficiency and the availability of open-weight models.

There is therefore no universal answer to the question “is DeepSeek or ChatGPT better?”.

The choice depends on the task.

For example, one user might prefer a particular service for writing, another for programming, and yet another for API integrations or for the option of using models whose weights are available to the community.

Technical comparisons, too, should be made between specific model versions, not between the generic names of platforms. Simply saying “DeepSeek versus ChatGPT” without specifying the model, date and task makes the comparison of little value.

Is DeepSeek better than ChatGPT?

There is no universally valid answer.

DeepSeek has become a major competitor in generative AI mainly thanks to its combination of reasoning, efficient model development, open weights in some families and attention to cost.

ChatGPT, by contrast, belongs to a different ecosystem, with a very broad set of models, tools and features.

The right comparison should therefore start from the question:

“Which system is best suited to this specific task?”

For writing, programming, research, document analysis, images, automation or software development, performance may vary depending on the model used, the configuration and the type of task.

This is why comparisons based on a single benchmark or a single personal experience are useful, but not sufficient for choosing a platform for professional use. The same applies to Google’s Gemini and Anthropic’s Claude.

Is DeepSeek open source?

Here too, it is important to use precise terminology.

In the AI debate, DeepSeek is often described as “open source”. The project has indeed published models and weights under licences that permit certain uses, and has contributed to making technologies accessible to the community.

For example, DeepSeek-R1 was released under the MIT licence, and DeepSeek has also made available distilled models derived from R1.

However, an open-weight model does not automatically mean that every element of the entire DeepSeek system is open.

The model, the training data, the infrastructure, the cloud service, the application and the APIs are distinct components.

This distinction matters above all for companies and developers evaluating DeepSeek for their own deployment.

DeepSeek for businesses and developers

Professional interest in DeepSeek is not limited to chat.

The APIs allow the models to be integrated into software applications and workflows. The official documentation also indicates compatibility with API formats used in the OpenAI and Anthropic ecosystem.

This opens up scenarios such as internal assistants, automated document analysis, text generation and transformation, software development support, information classification, automation of repetitive tasks, agentic workflows and custom AI applications.

For a company, however, the choice of a model should not be determined solely by its ability to produce a good answer.

Before adopting it, organisations need to assess at least the data processed, privacy, security, retention arrangements, regulatory requirements, reliability, output control, costs, integration options and AI governance.

DeepSeek and privacy: what data is processed?

The privacy question is particularly important for anyone using DeepSeek with personal information, company documents or confidential data.

DeepSeek’s privacy policy, updated in April 2026, states that the service may collect, among other data, account information, prompts, uploaded files, photos, feedback, chat history, device data, network information, logs and an approximate location derived from the IP address.

The same privacy policy also states that personal data may be stored and processed in the People’s Republic of China and that data may be shared with service providers to the extent necessary to provide, support and develop the service.

This does not mean that DeepSeek should automatically be considered unsafe. It does mean, however, that the type of data entered into the service must be assessed carefully.

A precautionary rule applies to any generative chatbot: avoid entering confidential information, credentials, trade secrets or unnecessary personal data without first checking the contractual terms, settings and data processing.

The Italian privacy case

The DeepSeek question has been particularly significant in Italy.

On 30 January 2025, the Italian Data Protection Authority (Garante per la protezione dei dati personali) ordered, with immediate effect, a restriction on the processing of Italian users’ data by the companies providing the DeepSeek service, and simultaneously opened an investigation.

The measure stemmed from shortcomings identified by the Authority in the information provided by the companies on the processing of personal data and on the applicability of European legislation.

This episode matters for understanding a broader point: choosing an artificial intelligence system for professional use is not just a matter of assessing the quality of its answers.

Privacy, international data transfers, security and compliance must all form part of the overall assessment.

What are DeepSeek’s limitations?

Like any generative model, DeepSeek is not infallible.

An answer may be incomplete, inaccurate or out of date. It may be phrased with excessive confidence or based on a misreading of the request. This phenomenon is often associated with generative artificial intelligence hallucinations.

The issue is particularly important when the content produced concerns legal, financial, medical, technical or other information where an error can have significant consequences.

A model’s ability to reason or produce convincing answers does not remove the need for verification.

Can DeepSeek be wrong even when it seems confident?

The answer is: of course. One of the most insidious characteristics of language models is precisely their ability to produce fluent, persuasive answers even when the content is incorrect.

DeepSeek and security

Security must be assessed on several levels.

There is the security of the application, of the infrastructure and of the API and, above all, the security linked to how users actually use the system.

A chatbot connected to company data can become a vulnerable point if documents are uploaded without a proper internal policy.

Likewise, the introduction of agentic AI systems increases the importance of controls: a system that does more than generate text, and can use tools, process information or carry out operations, requires more rigorous governance.

The growing capability of agentic models therefore makes it necessary to distinguish between AI used as an assistant and AI embedded in operational processes.

DeepSeek V4.1 Flash and multimodality

One of the most interesting developments of the V4.1 generation is multimodality.

DeepSeek-V4.1-Flash has native visual understanding. This means the model is not designed solely to process text: it can also interpret visual information.

Multimodality broadens the range of use cases for artificial intelligence.

A model capable of combining text and images can be used, for example, to analyse visual documents, interpret screenshots, understand charts or support workflows in which textual and visual information must be considered together.

The actual functionality available to the user, however, depends on the interface and product through which the model is accessed.

DeepSeek for research and everyday work

The value of an AI assistant depends not only on the model, but on how it is integrated into the work process.

For everyday tasks, it can be used to turn unstructured information into more organised content, create first drafts, summarise material, weigh up alternatives or speed up repetitive tasks.

For example, a professional could give DeepSeek a set of notes and ask it to:

1. identify the main concepts;
2. order them;
3. highlight any contradictions;
4. turn them into a structure;
5. produce a first draft;
6. indicate which information requires verification.

In this scenario AI does not necessarily replace the professional: it reduces mechanical work and leaves the person with the most important part, namely oversight, judgement and decision.

DeepSeek: why it has become important in the AI sector

DeepSeek’s importance stems from the way it has shifted attention to some fundamental questions in artificial intelligence, such as the cost of developing advanced models, architectural efficiency, the role of open-weight models, the importance of reasoning, and the speed at which the competitive AI landscape can change.

The release of R1 in 2025 had already shown how quickly a new model could enter into competition with the most established systems. The subsequent evolution towards V4 and V4.1 also shows that the project did not stop at a single reasoning model.

How to evaluate DeepSeek before using it

For a private user, it may be enough to try it directly and see whether it meets their needs.

For a company, however, a more structured process is advisable.

Before integrating DeepSeek into professional work, it is worth checking:

1. The use case.
What problem should the AI solve? Generating content, analysing data, programming, classifying documents or automating a process?

2. The model.
Not all models are designed for the same type of task.

3. The quality of the output.
Answers must be tested on real cases, not just on demonstration examples.

4. The data.
You need to know what information is sent to the service and how it is processed.

5. Security.
Confidential documents and sensitive data require specific controls.

6. Costs.
For use via API, consumption, request frequency and infrastructure must be taken into account.

7. Governance.
It is necessary to establish who can use AI, with what data and for which tasks.

8. Human oversight.
A review process must be in place for outputs that affect important decisions.

DeepSeek: it is not just about the chatbot

Perhaps DeepSeek’s most significant evolution lies precisely in moving beyond the idea of the chatbot as a mere tool for asking questions.

The trajectory of the latest models is towards systems capable of handling long contexts, using tools, working with code, interpreting visual content and taking part in more complex workflows.

DeepSeek’s API documentation already shows integration geared towards development systems and agents, while the current V4.1-Flash has been designed with particular attention to agentic tasks.

This is why DeepSeek should be seen above all as an evolving technology platform, not simply as “the Chinese alternative to ChatGPT”.

DeepSeek beyond the chatbot

DeepSeek is a generative artificial intelligence platform that develops and distributes language models geared towards conversation, reasoning, programming, analysis and increasingly complex tasks.

Its path has taken in models such as V3 and R1, reached the V4 family and, in September 2026, arrived at DeepSeek-V4.1-Flash, which also introduces native visual understanding and a new architecture geared towards efficiency and performance.

It can be used online, through mobile apps and via API. For developers, it is an attractive option for integrating generative models into custom software and workflows.

For Italian users, however, use must be accompanied by particular attention to privacy and data management, not least in light of the events involving the service and the Italian Data Protection Authority (Garante) in 2025.

The question to ask before using it should therefore be: “which tasks is it genuinely suited to, and under what conditions can I use it?”.

As with any artificial intelligence system, value does not depend solely on the power of the model. It depends on the quality of the instructions, the quality of the data provided, the ability to verify the answers and, above all, the way the technology is embedded in day-to-day work.