DeepSeek in one minute
DeepSeek can process text and code, work through problems and, with suitable models, understand images. A chat product, an API and downloadable model weights provide different ways of using it. On 8 October 2026, V4.1-Flash is the latest release in the official overview; V4-Pro-0813 remains available through the API as well.
Which DeepSeek models are current?
The official release history lists DeepSeek-V4.1-Flash, released on 10 September 2026. On the review date, the deepseek-flash API name points to this version. The current model table also lists deepseek-v4-pro, with version V4-Pro-0813. Earlier names such as V3 and R1 belong to the family’s development, but do not describe its newest release.
V4.1-Flash accepts text and images and returns text. Examples include explaining a chart or combining a product photograph with specifications. Understanding images does not automatically mean generating images or video. For programming, the model can propose code; a development environment still needs to run and test it.
The API supports adjustable reasoning effort. More computation may help with a difficult task, but does not guarantee a correct answer. Check which version an API alias points to before comparing results from different months.
MoE, context and open weights explained
V4.1-Flash uses Mixture of Experts (MoE): a selection of specialised components is activated for each token. The total parameter count therefore differs from the number used for a single step. The model card also describes a Causal Encoder-Decoder architecture and compression of temporary context storage. The aim is to process long inputs more efficiently.
The model card specifies a context of up to one million tokens. This is available working space for context and output, not a guarantee that every detail in a large dossier will be retrieved correctly. Attachments, instructions and tool results share the available space; the product interface may apply its own limits.
V4.1-Flash weights are available under the MIT licence. Open weights lets developers run or adapt the model within the licence terms. Hardware and maintenance are still required. It does not mean all training data is public.
The model’s evaluation tables were published by DeepSeek. They are vendor measurements, not independent validation or proof that DeepSeek is the best choice for every task.
An example for an online shop
An online shop receives product information as text and images. An application can ask DeepSeek to suggest attributes and flag missing values. A person then checks dimensions, materials and claims before the information enters the CMS. A tool writes to the CMS only when that integration and permission actually exist.
With your own installation, you manage the model, infrastructure and updates. With the API, the provider manages the model and its service terms apply. Compare both approaches using your own documents, error rate and total operating costs. The current API pricing table is linked below; fixed amounts would quickly become outdated here.