DeepSeek Neural Network: Overview, Applications, and Examples

Technical writer

Infrastructure

17.09.2025

15 min read

In recent years, the development of large language models (LLMs) has become one of the key areas in the field of artificial intelligence. From the first experiments with recurrent and convolutional networks, researchers gradually moved to attention-based architectures—the Transformer, proposed in 2017 by Google’s team.

This breakthrough paved the way for scaling models capable of processing enormous volumes of textual data and generating coherent, meaningful answers to a wide variety of questions.

Against the backdrop of Western dominance, the work of Chinese research groups is attracting more and more attention. The country is investing significant resources into developing its own AI platforms, seeking technological independence and a competitive advantage in the global market.

One of the latest embodiments of these efforts is the DeepSeek neural network, which combines both the proven achievements of the Transformer architecture and its own innovative optimization methods.

In this article, we will look at how to use DeepSeek for content generation, information retrieval, and problem solving, as well as compare its characteristics with Western and domestic counterparts.

What is DeepSeek AI and How It Works

DeepSeek is a large language model (LLM) developed and launched by the Chinese hedge fund High-Flyer in January 2025.

At its core lies the transformer architecture, distinguished by a special attention mechanism that allows not only analyzing fragments of information in a text but also considering their interconnections.

In addition to the transformer foundation, DeepSeek employs several innovations that may be difficult for a non-technical person to grasp, but we can explain them simply:

Multi-Head Latent Attention (MLA). Instead of storing complete “maps” of word relationships, the model keeps simplified “sketches”—compact latent vectors. When the model needs details, it quickly “fills in” the necessary parts, as if printing out a fragment of a library plan on demand rather than carrying around the entire heavy blueprint. This greatly saves memory and speeds up processing, while retaining the ability to account for all important word relationships.
Mixture-of-Experts (MoE). Instead of a single universal “expert,” the model has a team of virtual specialists, each strong in its own field: linguistics, mathematics, programming, and many others. A special “router” evaluates the incoming task and engages only those experts best suited for solving it. Thanks to this, the model combines enormous computational power with efficient resource usage, activating only the necessary part of the “team” for each request.

Thus, DeepSeek combines time-tested transformer blocks with the innovative MLA and MoE mechanisms, ensuring high performance while relatively conserving resources.

Key Capabilities of DeepSeek: From Code to Conversations

The DeepSeek neural network can generate and process various types of content, from text and images to code and documents:

Dialogues. Builds natural human-like conversations with awareness of previous context. Supports many tones of communication, from formal to informal. Manages long-session memory up to 128,000 tokens of context.
Exploring specific topics. Instantly responds to queries across a wide range of fields: science, history, culture. Collects information from external sources to provide more accurate data.
Creative writing and content generation. Generates ideas and assists in writing articles, stories, scripts, slogans, marketing texts, narratives, poems, and other types of textual content.
Code generation and understanding. Performs any code-related tasks in the most popular programming languages: writing, autocompletion, refactoring, optimization, inspection, and vulnerability detection. Moreover, the model can generate unit tests and function documentation. Essentially, DeepSeek can do everything a human programmer can.
Supported languages include: C, C++, C#, Rust, Go, D, Objective-C, JavaScript, TypeScript, HTML, CSS, XML, PHP, Ruby, Python, Perl, Lua, Bash/Shell/Zsh, PowerShell, Java, Kotlin, Swift, Dart, Haskell, OCaml, F#, Erlang, Elixir, Scala, Clojure, Lisp/Scheme, SQL, JSON, Markdown, and many more.
Document and website analysis. Summarizes the contents of documents, condenses information from external sites, extracts key ideas from large texts.
Translation from foreign languages. Translates text into dozens of languages while preserving original terminology and style.

In short, anything that can be done with textual data, DeepSeek can do. The only limits are the imagination of the user.

DeepSeek Chatbot: Three Key Modes

The DeepSeek chatbot offers three core modes, each optimized for different types of tasks and depth of processing:

Normal. Fast and lightweight answers to common questions. Has a limited context window but provides relatively high-quality responses with minimal delay. Suitable for direct factual queries: definitions, short explanations, notes.
DeepThink. In-depth analytical research with complex reasoning. Has an expanded context window but requires much more time to generate responses. Performs multi-step processing, breaking tasks into sub-tasks. Uses a “chain of thought” method, forming intermediate conclusions for the final answer. Suitable for logic-heavy queries: solving math problems, writing essays, detailed analysis of scientific articles, comprehensive strategic planning.
Search. Thorough analysis of external sources to provide up-to-date information. Automatically connects to the internet to search for current data, news, statistics. Uses specialized APIs and search engines, verifies sources, processes results, cross-checks facts, filters out irrelevant information. Suitable for finding fresh data and fact-checking.

Comparative Table of Modes

Mode	Response Speed	Context Size	Depth of Analysis	External Sources
Normal	high	limited	low	no
DeepThink	low	maximum	high	no
Search	medium	variable	medium	yes

Thus, if you just need a quick answer, use Normal mode. For deep reasoning and detailed justifications, choose DeepThink. To obtain the latest verified data from external sources, use Search.

How to Use DeepSeek: Interface, Access, and Launch

Although DeepSeek AI does not exist within a vast ecosystem (like Google’s Gemini), the neural network offers several ways to interact with it.

Option 1. Remote Application

In the simplest case, there are three ways to interact with the model hosted on DeepSeek’s remote servers:

All options provide dialogue with the model through a chatbot. In every case, the user interface includes a dialogue window, a message input field, file attachment buttons, and a panel with active sessions.

To access the model, you must either register with DeepSeek using an email address or log in through a Google account.

After that, a familiar chatbot page opens, where you can converse with the model and manage active sessions, just like with other LLMs such as ChatGPT, Gemini, Claude, etc.

Option 2. Local Application

A more advanced way is to install DeepSeek on a local machine. This is possible thanks to its open-source code, unlike many other LLM services.

DeepSeek can run on Windows, macOS, and Linux. Minimum requirements: 8 GB of RAM and 10 GB of free disk space, plus Python 3.8 or higher.

When running locally, there are several interaction methods:

Method 1. Web interface.

A graphical UI that allows querying, viewing logs, connecting external storage, monitoring metrics, analyzing performance, and more. The local interface differs from the public one by offering advanced model management tools. It is primarily intended for internal use by individual users or companies and contains parameters that only specialists would understand.

Method 2. Console terminal.
Method 3. REST API.

A full REST interface for sending HTTP requests to the locally installed model. Example with curl:

curl -X GET 'http://localhost:8080/api/search?index=my_index&query=search' \
  -H "Authorization: Bearer UNIQUE_TOKEN"

This universal method does not depend on the client type, whether a console terminal or a complex C++ program.

Method 4. Python script.

DeepSeek provides a wrapper fully compatible with the OpenAI API, allowing use of the standard OpenAI client with only a URL change. Example:

from openai import OpenAI

client = OpenAI(api_key="UNIQUE_TOKEN", base_url="http://localhost:8080")

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant, DeepSeek."},
        {"role": "user", "content": "Hello!"},
    ],
    stream=False
)

print(response.choices[0].message.content)

Method 5. JavaScript script.

Similarly, you can interact with DeepSeek using the OpenAI client in JavaScript. Example (Node.js):

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: 'http://localhost:8080',
  apiKey: 'UNIQUE_TOKEN'
});

async function main() {
  const completion = await openai.chat.completions.create({
    messages: [{ role: "system", content: "You are a helpful assistant." }],
    model: "deepseek-chat",
  });

  console.log(completion.choices[0].message.content);
}

main();

Notably, it is precisely the open-source nature that made DeepSeek popular and competitive in the LLM market.

However, the local version is intended for advanced users with deep ML knowledge and specific tasks requiring local deployment.

Detailed information on local installation is available in the official DeepSeek GitHub repository and the HuggingFace page.

Specialized DeepSeek Models

In addition to the core model, several specialized versions exist:

DeepSeek Coder. For working with code (analysis and editing) in multiple programming languages. Available on GitHub.
DeepSeek Math. For solving and explaining complex mathematical problems, performing symbolic computations, and constructing formal proofs. Available on GitHub.
DeepSeek Prover. For automated theorem proving. Available on HuggingFace.
DeepSeek VL. A multimodal model for analyzing and generating both text and images. Available on GitHub.

DeepSeek Pricing Plans

The DeepSeek service provides completely free access to its core models (DeepSeek-V and DeepSeek-R) through the website and mobile app. At present, there are no limits on the number of queries in the free version.

The only paid feature in DeepSeek is the API, intended for application developers. In other words, if someone wants to integrate DeepSeek into their own app, they must pay for API usage, which processes the requests.

Payment in DeepSeek follows a pay-as-you-go model with no monthly subscriptions. This means that the user only pays for the actual API usage, measured in tokens.

There are no minimum payments. The user simply tops up their balance and spends it as queries are made. The balance does not expire over time.

You can find more details on API pricing in the official DeepSeek documentation.

	DeepSeek-V	DeepSeek-R
1 million tokens (input)	$0.27	$0.55
1 million tokens (output)	$1.10	$2.19

To control expenses, manage API tokens, and view usage statistics, DeepSeek has DeepSeek Platform.

It also provides links to documentation and reference materials that describe the basics of using the model, integrating with external applications, and pricing specifics.

Prompts for DeepSeek: How to Give Commands and Get Results

Although prompts for DeepSeek can vary, there are several general principles to follow when writing them.

Clarity and Specificity

It’s important to clearly describe both the details of the request and the desired format of the answer. Avoid vague wording, and provide context if needed.

For example, you can specify the target audience and the approximate output format:

I’m preparing a school report on history. I need a list of the 5 most important discoveries of the early 20th century, with a short explanation of each in the format of a headline plus a few paragraphs of text.

For such queries, you can use Search mode. In this case, DeepSeek will reinforce the response with information from external sources and perform better fact-checking.

In some cases, you can describe the format of the response in more detail:

I need a list of the 15 most important discoveries of the early 20th century in the form of a table with the following columns:

Name of the discovery (column name: “Name”)
Authors of the discovery (column name: “Authors”)
Date of the discovery (column name: “Date”)
Short description of the discovery (column name: “Description”)
Hyperlinks to supporting publications (column name: “Sources”, data in the format [1], [2], [3], ... with clickable links, but no more than 5 sources)

The table rows must be sorted by date in descending order.

The more detail you provide, the better. When writing prompts for DeepSeek, it’s worth taking time to carefully consider what you need and in what format.

You can also use text descriptions to set filters: date ranges, geography, language of sources, readability level, and many other parameters.

For example:

I need a table of the 15 most important discoveries of the early 20th century that were made in the UK between 1910 and 1980. The table rows must be sorted by date in descending order, and the columns should be:

Name (column: “Name”)
Authors (column: “Authors”)
Date (column: “Date”)

As you can see, filtering in DeepSeek is done through natural language text rather than the sliders or filters familiar from internet catalogs or UGC platforms.

Clear Formalization

In addition to detailed text descriptions, you can formalize requests with a structured format, including special symbols:

[Task]: Create a table of the 10 most important discoveries of the early 20th century.

[Constraints]:

- Territory: United Kingdom

- Period: 1910–1980

[Structure]:

- Columns: number, name, author, date (day, month, year)

[Context]: For history students specializing in British history.

This creates a clear request structure:

Task. What needs to be done.
Context. Where to search and for whom.
Constraints. What to include or exclude.

You can, of course, customize the structure depending on the task.

Advanced Techniques

LLM-based neural networks are extremely flexible. They support more complex dialogue patterns and information-processing methods.

To get more relevant answers, you can use advanced prompting techniques, often mirroring real human dialogue.

Option 1. Role-based prompts

Explicitly asking the model to take on a role with specific qualities can add depth and define the style of the answer.

Imagine you are an expert in English history with more than 30 years of experience studying the nuances of the UK’s scientific context. In your opinion, what 10 discoveries in the UK can be considered the most important of the 20th century? Please provide a brief description of each, just a couple of words.

This style of prompt works best with DeepThink mode, which helps the model immerse itself more deeply in the role and context.

Option 2. Query chains

In most cases, obtaining a comprehensive response requires multiple queries—initial exploratory prompts followed by more specific ones.

For example:

First, a clarifying question: What sources exist on scientific discoveries in the UK during the 20th century?
Then, the main request: Based on these sources, prepare a concise description of 5 scientific discoveries. Format: title + a couple of explanatory paragraphs.

The best results often come from combining DeepThink and Search modes. DeepSeek will both gather external information and process it in depth to synthesize a thorough answer.

DeepSeek vs. Other AI Models: Comparison and Conclusions

Unique Features of DeepSeek

Free access. The two main models (one for simpler tasks, one for complex tasks) are available completely free of charge. Only the developer API is paid, and the pricing is usage-based, not subscription-based.
No limits. All models are not only free but also unlimited, i.e., users can generate as much content as they want. While generation speed may not be the fastest, unlimited free use outweighs most drawbacks.
Open source. Industry experts, AI enthusiasts, and ordinary users can access DeepSeek’s source code on GitHub and HuggingFace.
Global availability. The DeepSeek website is accessible in most countries.

Comparison with Other LLM Services

Platform	Generation Speed	Free Access	Pricing Model	Content Types	Developer	Country	Launch Year
DeepSeek	High	Full	Pay-as-you-go	Text	High-Flyer	China	2025
ChatGPT	High	Limited	Subscription	Text, images	OpenAI	USA	2022
Gemini	High	Limited	Subscription	Text, images, video	Google	USA	2023
Claude	Medium	Limited	Subscription	Text	Anthropic	USA	2023
Grok	Medium	Limited	Subscription	Text, images	xAI	USA	2023
Meta AI	Medium	Limited	Subscription / Usage	Text, images	Meta (banned in RF)	USA	2023
Qwen	Medium	Full	Pay-as-you-go	Text	Alibaba	China	2024
Mistral	High	Limited	Subscription	Text	Mistral AI	France	2023
Reka	High	Full	Pay-as-you-go	Text	Reka AI	USA	2024
ChatGLM	Medium	Limited	Pay-as-you-go	Text	Zhipu AI	China	2023

Conclusion

On one hand, DeepSeek is a fully free service, available without volume or geographic restrictions. On the other hand, it is a powerful and fast model, on par with many industry leaders.

The real standout, however, is its open-source code. Anyone can download it from the official repository and run it locally.

These features distinguish DeepSeek from competitors, making it not only attractive for content generation but also highly appealing for third-party developers seeking integration into their own applications.

That’s why when ChatGPT or Gemini fall short, it’s worth trying DeepSeek. It just might find the right answers faster and more accurately.

Infrastructure

17.09.2025

15 min read