What is DeepSeek?

In January 2025, a little-known Chinese AI lab released a chatbot that shot to the top of app store charts and rattled the technology stock market in a single week. That lab was DeepSeek, and it has stayed in the headlines ever since. If you have heard the name but are not sure what it means, this guide explains it in plain language: who built it, what its models can do, why it caused such a stir, how to use it, and what to watch out for. It is written for beginners and professionals alike, with the latest facts from 2026. If you want a broad foundation in how systems like this work, the Certified Artificial Intelligence (AI) Expert program is a good place to start.

Quick Answer: What Is DeepSeek?

DeepSeek is a Chinese artificial intelligence company based in Hangzhou, and also the name of its family of large language models and its chatbot. It was founded in 2023 by Liang Wenfeng, who also co-founded the quantitative hedge fund High-Flyer. The company is best known for building powerful AI models at a much lower reported cost than many Western rivals and for releasing the model weights, meaning the trained model files, under the permissive MIT license.

The Story of DeepSeek: From Hedge Fund to Global AI Lab

DeepSeek grew out of High-Flyer, a Chinese quantitative trading firm that had invested heavily in computing hardware and machine learning. In 2023, Liang Wenfeng launched DeepSeek as a dedicated AI research arm.

The company reached global attention with two releases. DeepSeek V3, a general-purpose model, arrived in late 2024. Then, on January 10, 2025, the company released a chatbot based on its R1 reasoning model. R1 matched leading models on many benchmarks at a fraction of the reported cost, and its debut hit US tech stocks hard.

Understanding how DeepSeek built its models means understanding core ideas from machine learning, such as reinforcement learning and mixture-of-experts design. Professionals who want to master those techniques can look at the Certified Machine Learning Expert program, which covers how learning systems are built and applied.

In September 2025, DeepSeek reached another milestone. Its R1 paper was published in the journal Nature, the first mainstream large language model to go through formal peer review in a leading journal. The supplementary material revealed that training R1's reasoning abilities cost about $294,000, on top of roughly $6 million spent on the base model it was built from. DeepSeek said R1 was trained mainly on Nvidia H800 chips, and it also responded to claims that it had copied outputs from rivals, stating that R1 did not learn by copying reasoning examples from OpenAI's models.

DeepSeek Models Explained

DeepSeek V3: The General-Purpose Model

V3 is a large language model designed for everyday tasks such as writing, summarizing, translating, and coding. It became known for strong performance relative to the computing resources used to train it, which is why it drew so much attention from engineers.

DeepSeek R1: The Reasoning Model

R1 focuses on reasoning, meaning it works through a problem step by step before answering. DeepSeek's key idea was to use reinforcement learning, where the model is rewarded for correct answers, to encourage better reasoning with less dependence on human-labeled examples. This is useful for math, logic, and coding problems.

DeepSeek V4: The 2026 Family

On April 24, 2026, DeepSeek previewed its V4 family and released the weights under the MIT license on Hugging Face, a popular model-sharing site. There are two main versions:

• V4-Pro: About 1.6 trillion parameters in total, with roughly 49 billion active at any moment.

• V4-Flash: About 284 billion parameters in total, with around 13 billion active.

Both support a context window of one million tokens, which means they can take in very long documents in one go. Reports say V4-Flash received an official release on July 31, 2026, and V4-Pro left preview for general availability in mid-August 2026, though you should confirm current versions on DeepSeek's own channels.

How DeepSeek Works

You do not need a computer science degree to grasp the main ideas behind these models.

Mixture-of-experts. Instead of using every part of the network for every word, a mixture-of-experts model routes each piece of input to a small group of specialist sub-networks. That is why V4-Pro can have 1.6 trillion parameters but only 49 billion active at once. Fewer active parts means less computing per answer, which lowers cost.

Reinforcement learning. Rather than only copying human examples, the model tries answers and gets a score for how good they are. Over many rounds, it learns strategies that lead to better results.

Efficient memory use. DeepSeek reports that V4 reduces the memory needed for long context by roughly 9.5 to 13.7 times compared with its V3.2 model, using a mix of lower-precision number formats. Lower memory needs make long documents cheaper to process.

Hardware flexibility. Huawei announced that its Ascend AI chips can serve V4, which signals growing options beyond Nvidia hardware. DeepSeek has not clearly disclosed what chips it used to train V4, so treat claims about training hardware with caution.

These ideas show how AI progress depends on algorithms, hardware, and data working together. Learners who want to see how AI connects with other emerging fields, such as blockchain and advanced infrastructure, often explore a Deep Tech Certification path to see the bigger picture.

Why DeepSeek Mattered

• It challenged the "bigger budget wins" idea. Many people assumed that only companies spending billions could build top models. DeepSeek's reported costs suggested that smart engineering could close part of the gap.

• It pushed open weights into the spotlight. Because the model files are released under the MIT license, developers and companies can download them, study them, and run them on their own servers, within the license terms.

• It affected markets. The R1 launch in January 2025 contributed to a sharp fall in US technology stocks, as investors questioned how much computing power AI really needs.

• It highlighted the chip race. Support for Huawei Ascend hardware shows how geopolitics and export controls shape where and how AI runs.

• It lowered prices. Reports say DeepSeek's API prices undercut many Western providers, which put pressure on the whole market.

How to Use DeepSeek

There are three common ways:

• The web chatbot and mobile app. The simplest option. Open the site or app, type a question, and get an answer. This route involves sending your data to DeepSeek's service.

• The API. Developers can call DeepSeek models from their own software on a pay-per-use basis. Check the current pricing and terms before building.

• Open weights. Technical teams can download models from Hugging Face and run them on their own hardware. Smaller distilled versions can run on modest machines, while the largest models need multi-GPU servers.

Good habits apply to any AI tool: write clear prompts, check important facts, and avoid pasting private or confidential data into any service you have not reviewed.

DeepSeek Compared With Other AI Assistants

Which is better depends on your needs. No single model wins every task, so test with your own work.

Risks, Privacy, and Limits

A fair picture includes the concerns:

• Data location. Reports say the DeepSeek app stores user data on servers in China, which has raised privacy and national security worries. Several governments and organizations have restricted its use on official devices, so check your employer's rules.

• Censorship and bias. The chatbot has been criticized for limiting answers on topics sensitive to the Chinese government. Journalists have also reported inconsistent behavior in some tests.

• Unclear training data. DeepSeek has not published its full training data, so questions remain about what the models learned from.

• Distillation debate. The industry argument over whether rivals' outputs were used in training continues, although DeepSeek denies copying.

• Hallucinations. Like all language models, DeepSeek can state wrong facts with confidence.

• Security and compliance. Companies should assess any AI tool before using it with sensitive data.

• Fast change. Model names, versions, and availability shift quickly, and some details come from secondary reports. Always confirm on official sources.

What DeepSeek Means for Careers and Business

For learners, DeepSeek shows how important it is to understand AI fundamentals instead of memorizing one tool. For businesses, it shows that open models can lower costs and improve control, but they also need careful security and compliance review. For marketers, AI tools can speed up research, drafting, and analysis, while human judgment remains essential for strategy and brand voice. Professionals who want to pair AI skills with customer insight and positioning can build that side through a Marketing Certification.

As artificial intelligence becomes part of everyday business operations, employers increasingly need people who can apply AI knowledge responsibly and effectively. Evaluating candidates on relevant skills, reasoning, and practical understanding can support stronger hiring decisions. HireValid can be explored by organisations seeking structured candidate assessments.

Conclusion

DeepSeek is a Chinese AI lab and model family that shook the industry by showing that strong AI could be built with smaller budgets and shared openly. From V3 and R1 to the 2026 V4 family with one million tokens of context and MIT-licensed weights, it has become a major force in the global AI conversation. At the same time, privacy, censorship, and transparency questions remain real. The smartest approach is to test the tools for yourself, protect your data, verify important answers, and keep learning the fundamentals so you can judge any new model with confidence.

FAQs

1. What is DeepSeek?

DeepSeek is an artificial intelligence company and AI model family that develops large language models for tasks such as conversation, reasoning, coding, research, and agentic applications. Its model lineup has evolved from V3 and V3.2 into the V4 family released in 2026.

2. Who developed DeepSeek?

DeepSeek was developed by the Chinese AI company DeepSeek. The organization focuses on developing large-scale AI models and making model research and releases available to the broader developer community.

3. What is the latest DeepSeek model in 2026?

DeepSeek's current model family includes DeepSeek-V4, with the company releasing V4 variants during 2026. DeepSeek also released DeepSeek-V4.1-Flash in September 2026, describing it as a smaller model in the V4.1 architecture family with native multimodal visual understanding.

4. What is DeepSeek-V4?

DeepSeek-V4 is a newer generation of DeepSeek's large language model architecture introduced in 2026. DeepSeek's V4 Preview was announced with a focus on efficient long-context intelligence and a context length of up to one million tokens.

5. What is DeepSeek-V4.1-Flash?

DeepSeek-V4.1-Flash is a smaller model in DeepSeek's V4.1 architecture family. DeepSeek introduced it in September 2026 with native multimodal visual understanding and a focus on faster inference, higher throughput, and efficient AI workloads.

6. What was DeepSeek-V3.2?

DeepSeek-V3.2 was a reasoning-focused model released in December 2025. It introduced thinking capabilities integrated with tool use and was designed for both general tasks and agentic workflows.

7. Is DeepSeek an open-source AI model?

DeepSeek has released model weights and technical materials for several of its models, allowing developers and researchers to study or work with them. However, the exact licensing and availability terms can differ between models, so users should check the license associated with the specific model they intend to use.

8. What can DeepSeek be used for?

DeepSeek can be used for writing, research, reasoning, coding, mathematics, data analysis, problem-solving, and AI-agent workflows. Depending on the model, it can also process visual information and support tool-enabled applications.

9. Is DeepSeek good for coding?

DeepSeek models are designed to support coding and software-development tasks. The company has also reported improvements in code-agent and tool-use capabilities across its newer model generations.

10. Can DeepSeek perform advanced reasoning?

Yes. Reasoning has been a major focus of DeepSeek's model development. DeepSeek-V3.2, for example, was explicitly designed as a reasoning-first model and supports thinking during tool-use workflows.

11. Does DeepSeek support multimodal AI?

Yes. Newer DeepSeek models support multimodal capabilities. DeepSeek-V4.1-Flash was specifically introduced with native visual understanding, allowing the model to work with visual information in addition to text.

12. What is DeepSeek used for in AI agents?

DeepSeek models can provide reasoning, planning, coding, and tool-use capabilities for AI agents. DeepSeek-V3.2 was specifically designed to integrate thinking with tool use, while later V4 models expanded agent capabilities further.

13. Does DeepSeek have an API?

Yes. DeepSeek provides an API that developers can use to integrate its models into applications and workflows. The company has also expanded API support for newer V4 models and agent-oriented capabilities.

14. Is DeepSeek available as a chatbot?

Yes. DeepSeek provides an official AI assistant through its web and mobile applications. The company also provides desktop software through its DeepSeek Harness, which is designed for longer and more complex tasks involving local files.

15. Is DeepSeek free to use?

DeepSeek provides free access to its official AI assistant. However, API usage and certain developer services can have separate pricing and usage conditions.

16. What makes DeepSeek different from other AI models?

DeepSeek has emphasized efficient model architectures, reasoning, coding, long-context processing, and agent capabilities. Its open model releases and research publications have also made its technology relevant to developers and researchers interested in running or studying advanced AI models.

17. What is DeepSeek Sparse Attention?

DeepSeek Sparse Attention, or DSA, is an attention mechanism introduced with DeepSeek-V3.2-Exp to improve the efficiency of training and inference for long-context workloads. DeepSeek described it as a fine-grained sparse attention approach designed to reduce computational requirements while maintaining output quality.

18. Can DeepSeek be used for business applications?

Yes. Developers and organizations can integrate DeepSeek models into applications involving customer support, coding assistance, research, data analysis, automation, and AI agents. Businesses should evaluate factors such as model performance, data handling, security, compliance, infrastructure, and licensing before deployment.

19. Is DeepSeek better than ChatGPT?

There is no universal winner. ChatGPT and DeepSeek use different model families and product ecosystems, and their performance varies depending on the task, model version, tools, pricing, context requirements, and user needs.

20. What is the future of DeepSeek?

DeepSeek's recent releases indicate a continued focus on efficient reasoning, multimodal understanding, long-context processing, coding, and AI agents. Its 2026 V4 and V4.1 releases suggest that the company is continuing to develop models designed for increasingly complex AI and agentic workloads.