DeepSeek V4.1 Flash
Neural Network
DeepSeek V4.1 Flash is a sparse DeepSeek MoE model based on Causal Encoder-Decoder architecture, DeepSeek API for batch tasks.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 384 000 tokens
- Reasoning effort
- none, low, high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works DeepSeek V4.1 Flash?
DeepSeek V4.1 Flash is a sparse mixture-of-experts DeepSeek model and the first based on the Causal Encoder-Decoder architecture: only a subset of experts is activated at each step, with 8B parameters activated at the input. The batch variant is designed for batch task execution when responses are needed asynchronously. In BotHub, the model works without a VPN or foreign card; payment is in rubles via Russian cards, charged per used token, which do not expire. Access over 250 neural networks in one window: switch and compare responses. A unified OpenAI-compatible API allows switching models in a multi-model system without rewriting integrations. Chats are encrypted with AES-GCM. Companies get contracts, invoices, electronic document management, and an admin panel with limits. Useful for labeling and classifying document arrays, running batch generations for product cards, analyzing support tickets, drafting code, debugging, or processing logs on a schedule.Frequently asked questions about DeepSeek V4.1 Flash
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
This is a DeepSeek text model with a context window of over a million tokens. It is suitable for coding, refactoring, debugging, analyzing long documents, analytics, and STEM tasks. You can input text, documents, and images, and the batch mode is convenient for processing large volumes of similar requests.
The output limit is up to 131,072 tokens per response, which is sufficient for a voluminous technical document, a large code module, or a detailed analysis. The context window holds up to 1,048,576 tokens, so source materials can be uploaded in their entirety.
Yes, reasoning mode is supported: the model breaks down the task step-by-step before providing a result. This significantly helps with mathematics, logic, requirements analysis, and debugging code, where the sequence of conclusions is more important than speed.
Yes, the model works with function calling and structured JSON output. This allows building agents and pipelines, connecting external tools, and receiving predictable responses for parsing. A unified OpenAI-compatible API is available in BotHub, so there is no need to rewrite integrations.
According to our data, the model accepts text, documents, and images as input: you can send a screenshot, diagram, PDF, or a large code file and ask for an analysis. Audio and video support is not noted for this model; it provides results in text.
DeepSeek models traditionally handle Russian-language queries at a good level, although we do not have specific language benchmarks. We recommend testing the model with your tasks in BotHub: access without VPN or foreign cards, payment in rubles per token used.