DeepSeek V4.1 Flash 1017
Neural Network
DeepSeek V4.1 Flash 1017 is a dated snapshot of the DeepSeek V4.1 Flash model using a sparse MoE architecture for code and text tasks.
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 1017?
DeepSeek V4.1 Flash 1017 is a fixed snapshot of DeepSeek V4.1 Flash from October 17, 2026; the vendor serves this model via API under the name deepseek-flash. It is based on a sparse mixture-of-experts architecture: only a subset of experts is activated per request, making the model efficient for streaming text tasks. The snapshot date is important where model behavior must remain consistent between releases. BotHub provides access without VPN or foreign cards: pay with Russian cards in rubles, only for tokens used, which do not expire. With over 250 models in one window, it is easy to compare answers and switch models. A unified OpenAI-compatible API integrates the model into your service, and switching models does not require rewriting the integration; traffic is encrypted via AES-GCM, and chats are not saved. Companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. Use this model for writing and refactoring code, debugging, bulk text processing and structuring, drafting documentation, and building multimodal pipelines.Frequently asked questions about DeepSeek V4.1 Flash 1017
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 reasoning mode. It is suitable for working with code: writing, refactoring, and debugging, as well as for analytics, STEM tasks, and document analysis. You can input text, files, and images, which is convenient for extracting data from screenshots and tables.
The output limit is up to 393,216 tokens per response, and the context window is 1,048,576 tokens. This is enough to generate a large code module, a detailed technical analysis, or an entire long document without breaking the task into dozens of separate requests.
Yes, the reasoning mode is confirmed. The model first builds a chain of steps and then provides an answer — this helps with math, logic, code debugging, and multi-step analysis. For simple queries, this approach is usually unnecessary, but for complex ones, it significantly increases accuracy.
Yes, the model supports function calling and structured JSON output. It can be connected to your tools, databases, and external services via the unified OpenAI-compatible BotHub API, and if necessary, you can switch to another model without rewriting the integration.
According to our data, text, documents, and images are accepted as input: you can send an interface screenshot, diagram, chart, or PDF and ask to analyze the content. There are no notes regarding audio or video. The model responds with text — it does not generate images.
DeepSeek models were trained on multilingual data and generally work confidently with Russian-language queries. There are no exact specifications by language in our data, so it is easier to test it on your own task: in BotHub, you can run the same prompt through several models and compare the results.