Few AI names travelled as quickly as DeepSeek. A research lab most people had never heard of released a model in early 2025 that competed with far better-known systems, and it did so at a much lower reported cost. Since then the name has turned up in developer chats, company policies and news headlines. This guide sorts out what DeepSeek is, what it has released, and how to work with it carefully.
One caveat first. AI model lineups change often, and the most recent sources I could verify date to 2025. For what is current today, the official site and the model pages are the place to look, and I will point out where.
What DeepSeek is, and why it got attention
DeepSeek is a Chinese artificial intelligence company that builds large language models, the same family of technology behind chat assistants. It runs a chat website and app, and it offers an API for developers who want to build on its models.
What set it apart was a combination of strong results and an open approach. Trade press reported that its V3 model, announced in December 2024, was released as open source and had 671 billion parameters. Parameters are the adjustable numbers inside a model, and while more of them does not automatically mean better, it gives a rough sense of scale.
Then in January 2025 came R1, a reasoning model. Reasoning models spend extra effort working through a problem step by step before answering, which tends to help with maths, code and logic. Reports at the time described R1 as competing with OpenAI’s reasoning model at a much lower cost, which is why it drew so much notice.
The releases worth knowing
Here is the sequence as I could confirm it from news and developer-platform announcements.
- V3 (December 2024). A general-purpose model released openly, marketed as a rival to leading closed systems.
- R1 (January 2025). The reasoning model that put DeepSeek on the map.
- R1-0528 (mid 2025). An updated R1 version, which GitHub announced as generally available in GitHub Models in June 2025.
- V3.1 (August 2025). An open-source update reported by TechNode, which also noted that no date had been confirmed for an R2 model at that time.
I could not verify what DeepSeek has shipped since then, and I will not guess. Names like R2 or V4 circulate in rumour posts, and a rumour is not a release. Treat a model as real only when you can find it on DeepSeek’s own site or its Hugging Face page.
Open weights: what that does and does not mean
When people say DeepSeek is open source, the more careful term is open weights. The trained model files are published so that you can download and run them yourself, subject to the licence. Secondary sources describe the V3 release as using the permissive MIT licence, but read the licence file attached to the specific model you download, because terms can differ between versions.
Why does that matter? Three reasons.
First, you can run it on your own hardware. A company that cannot send data to an outside service can host a copy internally. That is a real privacy advantage over a website.
Second, researchers can inspect and test it. Independent evaluation is far easier when the weights are available.
Third, other firms can offer it. Cloud platforms and developer tools have added DeepSeek models, so you can often use one without going through DeepSeek’s own app. The GitHub announcement is one example.
What open weights do not give you is the training data or the full recipe. You can use the model, but you cannot fully reproduce how it was made.
Using DeepSeek sensibly
The question is not whether DeepSeek is good in general. It is whether it suits a particular job and your data.
Start with where the data goes. If you use the official app or website, your prompts are processed by the company’s servers. Read its privacy policy to see what is collected and where it is stored, and compare that with what your employer allows. Many organisations have rules about which AI tools staff may use, and some governments and companies have restricted particular services. Check the rules that apply to you instead of relying on a headline.
If privacy matters, a locally hosted or cloud-hosted copy of the open weights keeps your prompts under your control, though running large models needs serious hardware. Smaller distilled versions exist for modest machines, and they trade capability for size.
Next, match the model to the task. A reasoning model like R1 is useful for maths, coding problems and multi-step analysis, but it can be slower and wordier. A general chat model is quicker for drafting and summaries. Try a few of your own real prompts on each rather than trusting a benchmark chart.
Finally, check the answers. Like every language model, DeepSeek can state wrong things confidently. For facts, figures or code that will run in production, verify before you rely on it.
How to find the latest DeepSeek model
A short checklist keeps you out of outdated blog posts.
- Open DeepSeek’s official site and API documentation for the current model names and prices.
- Check its Hugging Face organisation page for open-weight releases and licence files.
- Look at the changelog or release notes on any platform you use, such as a cloud service that hosts the model.
- Ignore pages with no date and treat hosting-company listings as possibly out of date.
That last point is worth repeating. Several pages I came across called a 2025 release the latest, and they may simply never have been updated.
The short version on DeepSeek: it earned attention by releasing capable models openly and cheaply, and the open-weights approach gives you options other services do not. The details of the current lineup belong to the official pages, not to any article, including this one.
For a neutral background reference, the Wikipedia article on DeepSeek is a useful place to start.
Official source: www.deepseek.com.
Related reading: DeepSeek Explained: What the Chinese AI Chatbot Is and Why It Matters and Claude Subscription Price in India: Plans, Rupees and Fine Print.
