What Is DeepSeek? A Guide to the Open-Source Reasoning Model

What is DeepSeek? DeepSeek is a family of large language models (LLMs) and reasoning models developed by a Chinese research company, with weights released under an open license. Its most distinctive trait is offering performance close to comparable closed models at a much lower compute cost, thanks to its MoE architecture and low-cost training.

DeepSeek came onto the global agenda in early 2025 with the release of the DeepSeek-R1 model, because it challenged the assumption that training a large model is only possible with massive budgets. This guide answers what DeepSeek is, how it works, what being an open-source LLM means, why the MoE architecture and reasoning-model logic matter, and how it should be evaluated in an enterprise context.

Definition

DeepSeek

A family of large language models (LLMs) and reasoning models developed by a Chinese research company, with weights released under an open license. DeepSeek stands out for offering performance close to comparable closed models at markedly lower compute cost, thanks to its MoE (Mixture of Experts) architecture and low-cost training approach.

Also known as: DeepSeek AI, DeepSeek-V3, DeepSeek-R1, open-source LLM

DeepSeek's importance lies not in a single model but in the claim it demonstrated: producing a near-frontier model with a training budget far smaller than the industry was used to is possible. This shook the perception that AI is only the playground of a few of the largest companies.

This cost advantage has two practical consequences. First is access: because the weights are published openly, an organization can run the model on its own server and is not dependent on an API provider. Second is competition: if low-cost training is possible, similarly capable models can come from more actors, which pushes prices down. DeepSeek should therefore be read not just as a product but as a signal about the economics of the sector.

DeepSeek is commonly called an open-source LLM, but this phrase must be understood correctly. What is open is the model's weights: the parameters the model learned can be downloaded and used by anyone. This is a fundamental difference compared to a closed model (accessed only via an API).

Strictly speaking, however, it is more accurate to call this "open weight," because the full training data and every detail of the training code are often not published. Still, the practical result is powerful: as an open-source LLM, DeepSeek gives organizations the freedom to inspect the model, host it on their own infrastructure, and adapt it to their own data. This freedom is a decisive advantage over closed models, especially in scenarios where data control is critical.

At the technical heart of DeepSeek's low-cost success lies the MoE architecture (Mixture of Experts). In a classic language model, every query activates all of the network's parameters; as the model grows, the compute cost of each answer grows too. MoE breaks this equation.

How to

How a query is processed in the MoE architecture

The core steps of how DeepSeek's Mixture of Experts approach processes a query.

1. 1

   Take the input

   The user's input (token sequence) is given to the model and reaches the router layer.

2. 2

   Select experts

   The router selects the few most suitable 'expert' sub-networks for this input; the other experts stay silent.

3. 3

   Run only the selected ones

   Only the selected experts compute; so total capacity is large while active computation is small.

4. 4

   Combine the output

   The experts' outputs are combined to produce the next token of the answer.

The meaning of this design is this: DeepSeek can have a very large total parameter capacity, but only a small part of that capacity runs on each query. The result is combining the knowledge capacity of a large model with the compute cost of a small one. The MoE architecture is the single most important reason for low-cost training and low-cost inference. To understand token and parameter concepts more fundamentally, see the what is a token and what is an LLM guides.

The most talked-about member of the DeepSeek family is DeepSeek-R1, a reasoning model. An ordinary language model takes the question and produces the answer directly; a reasoning model, before answering, produces a visible "thinking" step — it carries out step-by-step reasoning (chain-of-thought) and only then reaches the conclusion.

The gain of this approach is clear on multi-step tasks: math problems, logic puzzles, and complex code turn out more accurate when solved through intermediate steps rather than in one shot. The cost is longer response time and higher token cost, because the thinking steps are also computed and generated. That is why a reasoning model suits not every task but tasks that genuinely require reasoning; for a simple summary or classification it creates unnecessary cost.

To position DeepSeek correctly, it should be seen on two axes: open weight or closed, and general-purpose or reasoning-focused. The table below roughly compares DeepSeek with typical alternatives.

A rough comparison of DeepSeek and typical LLM alternatives

Feature	DeepSeek (open weight)	Closed API models
Access	Weights downloadable, runs on-premise	Only via the provider API
Cost model	Low-cost training + self-hosting	Per-use API fee
Data control	High — data can stay in the company	Provider-dependent, contract-bound
Ecosystem maturity	Developing, requires own setup	Mature tools and integrations
Reasoning variant	Available with DeepSeek-R1	Varies by model

The lesson of this comparison is this: there is no single answer called "the best model"; the right choice depends on your priority. If data control and cost are the priority, an open-weight model (like DeepSeek) stands out; if speed, ease of integration, and a mature ecosystem are the priority, a closed API may be preferred. You can find the product perspective on the ChatGPT side in the what is ChatGPT guide.

At the center of DeepSeek's enterprise appeal are open weights: when you host the model on your own infrastructure, your data may never leave the organization's boundary. This is a strong position for GDPR and data sovereignty; it can be critical especially in scenarios involving personal data or trade secrets.

That is why the enterprise question should not be "is DeepSeek good?" but "with which deployment mode, with which data, for which task?" Placing an open-weight model into a secure enterprise architecture — with data boundaries, access control, and verification layers — is a design task in its own right. To build this architecture, see the enterprise RAG systems solution or the general AI consulting service.

Although DeepSeek is a strong option, a few common misconceptions need correcting. First is the "open source, therefore free" fallacy: even if the weights are free, the GPU infrastructure, maintenance, and engineering needed to run the model are a real cost. Second is the "cheapest is best" fallacy: low-cost training is impressive, but a model's fit for an enterprise task is not measured by price alone.

The third and most important misconception is taking data security for granted. Open weights offer the possibility of data control, but realizing that possibility requires the right architectural setup; using the hosted API without thought destroys this advantage. Finally, it is assumed that DeepSeek-R1, a reasoning model, is better at every task; yet on simple tasks its long thinking steps create unnecessary latency and cost. The right approach is to choose the model by task.

Is DeepSeek open source?

DeepSeek models' weights are released under an open license, so it is commonly called an open-source LLM. You can download the model and run it on your own server. But since the full training data is not released, strictly speaking it is more accurate to call it 'open weight'.

Why could DeepSeek be trained so cheaply?

The main reasons are the MoE architecture and engineering optimizations in the training process. MoE activates only a small part of the model at each step, combining large capacity with low compute cost. This is the technical basis of the low-cost training claim.

What is the difference between DeepSeek and ChatGPT?

ChatGPT is a closed product served via an API; DeepSeek is a model family with openly published weights that can be run on your own infrastructure. DeepSeek offers cost and control advantages, while ChatGPT provides a mature product ecosystem and easy integration.

What is DeepSeek-R1?

DeepSeek-R1 is DeepSeek's reasoning-model variant; it generates step-by-step thinking (chain-of-thought) before answering. This approach improves accuracy on multi-step tasks like math, logic, and code, but raises response time and token cost.

Can DeepSeek be used safely in an enterprise project?

Thanks to open weights, the model can be hosted on-premise, which is a strong advantage for data control. If the provider-hosted API is used, where the data goes, GDPR compliance, and contract terms should be evaluated beforehand.

Does DeepSeek understand Turkish well?

DeepSeek is trained on multilingual data and gives reasonable results in many languages including Turkish, but its Turkish performance can vary compared to English. In enterprise use, measuring Turkish quality with a pilot test on a narrow task first is the healthiest approach.

In short, the answer to what is DeepSeek is: a family of open-source LLMs and reasoning models with openly published weights, notable for its MoE architecture and low-cost training. Its biggest contribution is showing that near-frontier performance is possible at a much lower cost. A reasoning model like DeepSeek-R1 is strong at math and code through step-by-step thinking; in enterprise use, the data-control advantage of open weights gains value together with the right architecture and GDPR planning. To reinforce the basics, see the what is an LLM and what is generative AI guides, and for an enterprise setup start with AI consulting.