DeepSeek V4.1 Flash
Neural Network
DeepSeek V4.1 Flash is a sparse DeepSeek MoE model based on Causal Encoder-Decoder architecture, available in BotHub via a unified API.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 393 216 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 MoE model from DeepSeek and the company's first model built on the Causal Encoder-Decoder (CED) architecture. It activates about 8B parameters at input and 16B during generation, keeping computational load moderate. The combination of CED and sparse experts distinguishes this version from previous DeepSeek builds. In BotHub, the model is available immediately: no VPN or foreign cards required, with payment in rubles via Russian cards and pay-as-you-go billing for tokens that do not expire. Other models from the 250+ catalog are available in the same window, making it easy to run the same prompt through multiple neural networks and compare answers. For developers, there is a unified OpenAI-compatible API: connect DeepSeek V4.1 Flash and switch models with one line without rewriting the integration. Chats are encrypted via AES-GCM and not saved, while companies have access to contracts, invoices, EDI, and an admin panel with limits. Suitable for summarizing long documents, answering standard support queries, drafting texts, and extracting structured data from raw records.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.
The model works with text, accepts documents and images as input, supports reasoning before answering, and function calling. The context window is 1,048,576 tokens, so you can upload voluminous reports, codebases, and long conversations: analyze them, write and refactor code, find bugs, and prepare summaries and analytics.
The output limit is 943,718 tokens per response, which is a very large volume for generation. You can receive entire documentation sections, large code modules, detailed analyses, and long structured reports without breaking the task into dozens of separate requests.
Yes, the reasoning mode is confirmed in our data. The model builds a chain of steps before the final answer, which significantly helps with logic, math, and STEM tasks, code debugging, and analyzing conditions where not only the result but also the verifiable solution process is important.
Yes, the model supports function calling and structured JSON output. You can connect it to external services, databases, and your own tools, receiving answers in a specified schema. Through BotHub's unified OpenAI-compatible API, this can be connected without rewriting the integration when switching to another model.
The model accepts images and documents as input — this is confirmed by our data: you can send a screenshot, diagram, PDF, or table and discuss the content. Audio and video input is not noted, so for such formats, it is more convenient to choose a specialized model in the same interface.
The model's language characteristics are not fixed in our data, so it is most accurate to test it directly on your task. The BotHub interface and payment are in rubles via Russian cards, without VPN, and payment is pay-as-you-go for tokens, so a test run will be inexpensive.