DeepSeek has changed the artificial intelligence industry. Alongside giants such as OpenAI, Google, and Anthropic, this Chinese company has demonstrated another route to powerful AI: efficient computing and open development.
Its appeal is not simply that it can write text or answer questions. DeepSeek combines competitive reasoning capabilities with cost-conscious engineering and a more open development strategy.
What Is DeepSeek?
Founded in 2023 by entrepreneur Liang Wenfeng, DeepSeek is a Chinese AI company based in Hangzhou. It develops large language models that can generate text, solve mathematical problems, write software, and handle complex instructions.
DeepSeek is both the name of the company and the brand behind several AI model families. Users can interact with its systems through a chatbot, while developers can integrate supported models into their own applications.
The Breakthrough That Attracted Global Attention
In December 2024, DeepSeek introduced V3, followed by its reasoning-focused R1 model in January 2025.
R1 performed strongly in mathematics, programming, and analytical tasks, matching OpenAI’s contemporary o1 model on several published tests.
A central ingredient was Reinforcement learning, a training method in which a system improves by receiving feedback on its results.
This improved reasoning without requiring huge collections of manually written examples.
The achievement showed that clever training methods could be just as strategically important as increasing computing power.
Why DeepSeek Can Be More Computationally Efficient
DeepSeek-V3 used a Mixture-of-Experts (MoE) architecture. Instead of activating the entire network for every task, it selects the parts most relevant to processing a particular piece of text.
V3 contained 671 billion Parameters, but approximately 37 billion were activated per token.
This preserves substantial capacity while reducing the work required for each calculation.
DeepSeek reported approximately 2.788 million Nvidia H800 GPU-hours for V3’s principal pretraining run. This corresponds to roughly $5.6 million at the computing rate used in its technical report.
That figure covers the reported training computation, not the company’s complete research, staffing, hardware, or development expenses.
Open Models Give Developers More Control
Another advantage is the availability of Open weights for major DeepSeek releases.
These learned numerical values allow qualified users to download, adapt, and run models on their own infrastructure.
DeepSeek-R1 was released under the permissive MIT license, allowing commercial reuse and modification.
Organizations can tailor supported models and control where information is processed.
Running the largest models still requires substantial hardware. However, smaller models produced through Distillation make some of the technology accessible on more modest systems.
From DeepSeek-R1 to the V4 Generation
DeepSeek continued improving its models throughout 2025 and 2026.
In April 2026, the V4 preview introduced a one-million-token Context window for processing extensive material in one request.
Tokens are units of text processed by AI. A million-token limit can accommodate extensive documents and codebases.
The V4 family included Pro, designed for demanding reasoning and coding, and Flash, designed for faster, more economical use.
In September 2026, DeepSeek introduced V4.1-Flash, adding native visual understanding and a redesigned architecture aimed at lowering processing costs.
What Makes V4.1-Flash Different?
DeepSeek-V4.1-Flash contains 552 billion total parameters but activates about eight billion for input processing and sixteen billion for output generation.
This asymmetric approach reduces the work needed during Inference, the process of using an already trained model to produce answers.
DeepSeek also reported that the model’s attention cache required one-quarter as much high-bandwidth memory and one-eighth as much SSD storage as the previous generation.
This benefits automated assistants working with long instructions.
V4.1-Flash also supports Multimodal input, allowing it to interpret visual information as well as text.
DeepSeek Versus ChatGPT, Gemini, and Claude
OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude offer advanced AI capabilities, often combined with polished applications, integrations, and specialized features.
DeepSeek’s distinguishing strengths are different:
- Model access: Publicly released weights enable custom deployments that are not possible with every proprietary competitor.
- Cost efficiency: Architecture and infrastructure optimizations can reduce the expense of serving large volumes of requests.
- Reasoning and coding: DeepSeek has competed strongly in mathematics, software development, and technical evaluations.
- Long-document workflows: The V4 generation brought million-token context capacity to its official services.
The best choice depends on the task, deployment needs, and available tools.
Why Businesses and Developers Are Interested
Businesses access hosted DeepSeek models through an API, a software interface for sending requests and receiving results.
This supports applications such as coding assistants, document analysis, customer-service automation, and structured data extraction.
For high-volume applications, a lower cost per request can materially change the business case.
DeepSeek has also introduced peak and off-peak API pricing, with off-peak rates set at half the peak rate for supported services.
Businesses needing tighter control over their systems can evaluate self-hosting models with available weights, provided they have the necessary infrastructure and security expertise.
Expert Perspective: Efficiency Changes the AI Industry
AI researcher Andrew Ng highlighted DeepSeek-R1 in 2025 as evidence that algorithmic improvements can reduce the cost of developing capable AI models.
He also emphasized how accessible model weights create opportunities for companies building AI applications rather than training foundational systems from scratch.
This points to DeepSeek’s broader significance: competition in AI is increasingly about useful performance per dollar, not merely the size of a company’s budget.
DeepSeek’s work encourages the entire industry to pursue faster, more accessible systems.
Interesting Facts
- DeepSeek was founded in 2023, less than two years before R1 attracted global attention.
- The original V3 model activated only about 37 billion of its 671 billion parameters per token.
- DeepSeek-R1’s released family included six smaller distilled models.
- DeepSeek-V4 introduced a one-million-token context window in 2026.
- V4.1-Flash uses different active parameter counts for reading input and generating output.
- DeepSeek’s efficiency research includes methods for reducing the memory required to handle long conversations.
Glossary
- Reinforcement learning — Training that improves behavior using feedback or rewards.
- Mixture-of-Experts (MoE) — A model design that activates selected specialist components for each input.
- Parameters — Numerical values learned by a neural network during training.
- Open weights — Publicly available trained model values that can be downloaded and used under their license.
- Distillation — Training a smaller model to reproduce some capabilities of a larger model.
- Context window — The amount of information a model can process within one request.
- Tokens — Text-processing units used by language models.
- Inference — Running a trained AI model to generate an output.
- Multimodal — Able to process more than one type of information, such as text and images.
- API — An interface that allows software applications to communicate with an AI service.
