DeepSeek V4.1 Flash 1017
Neural Network
DeepSeek V4.1 Flash 1017 is an October snapshot of the sparse MoE DeepSeek model, provided under the name deepseek-flash.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 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 the DeepSeek V4.1 Flash model from October 17, 2026. It is provided in the API under the name deepseek-flash, so connecting to this version does not require rewriting your integration. The architecture is sparse, mixture-of-experts: only a portion of parameters is used per request, providing vast knowledge at moderate costs. This snapshot is valued for its reproducibility—the version does not change with weight updates. Through BotHub, the model is accessible from Russia without a VPN or foreign card; payment is in rubles based on usage, and tokens do not expire. Other models are available in the same window, making it easy to compare responses and switch, while the unified OpenAI-compatible API eliminates the need to rewrite code. Requests are not saved, the channel is protected by AES-GCM encryption, and companies have access to contracts, invoices, electronic document management (EDO), and limits in the admin panel. Scenarios: chat assistants and support widgets, text preparation and rewriting, document summarization, idea and draft generation, and product integration via API.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.
deepseek-v4.1-flash-1017:batch is a DeepSeek text model. It accepts text, documents, and images, reasons before answering, calls functions, and returns structured JSON. It is suitable for working with code, analyzing large documents, parsing scans and tables, and for pipelines requiring batch processing. Context: up to 1,048,576 tokens.
Up to 943,718 tokens per response is the maximum output volume specified for the model in our data. The actual response length depends on the request and settings, but there is plenty of headroom: you can get a detailed code analysis, a long document, or a large JSON without stitching parts together.
Yes. In our data, the model is marked as supporting reasoning before answering: it can think through solution steps and then provide a result. This is useful where logic and verification of intermediate calculations are important—in mathematics, code analysis, and parsing contradictory conditions.
Yes, both points are marked in the model data: function calling and structured JSON output are supported. Through these, you can connect external services, retrieve data via API, and return responses in a format that a program can immediately parse—convenient for automation and agent scenarios.
Images and documents—yes, they are listed among the model's input formats. There is no mention of audio or video in our data, so we do not promise to support them. If such parsing is needed, it is better to check with support before starting work.
Our data does not contain a quality assessment of the Russian language for this model, so we will not promise perfect results. The model works with standard text input, but before putting it into production, it is worth testing it with your own examples—run typical queries and check the results.