DeepSeek V4 Flash
Neural Network
DeepSeek V4 Flash: MoE model with 1M token context and fast inference, available via API in BotHub
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 717 tokens
- Reasoning effort
- none, high, extra high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works DeepSeek V4 Flash?
DeepSeek V4 Flash is a Mixture-of-Experts model: out of 284 billion parameters, about 13 billion are activated per request, ensuring fast responses and efficient computation. The 1M token context window allows you to handle bulky documents, long threads, and large codebases in a single dialogue without splitting them. In BotHub, the model is accessible without a VPN or foreign card: pay with Russian cards in rubles for used tokens that do not expire. Access over 250 other neural networks in one window to easily compare answers. A unified OpenAI-compatible API lets you switch models in a multi-model system without rewriting integrations. Chats are encrypted via AES-GCM, and companies get contracts, invoices, EDI, and an admin panel with limits. Use Flash when you need to analyze multi-page reports or documentation, review and debug large repositories, process streams of support tickets, or summarize long conversations.Frequently asked questions about DeepSeek V4 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 processes text, documents, and images, can reason before answering, and supports function calling. It is suitable for coding (writing, refactoring, debugging), analyzing large files, analytics, and STEM tasks. A context of up to 1,048,576 tokens allows you to keep entire repositories and batches of documents in a single request.
The single response limit is 384,000 tokens. This is enough to output a large code module, detailed documentation, or a comprehensive analysis without splitting the task or asking to continue. The actual length depends on the request: the model writes as much as the task requires.
Yes, the reasoning mode is confirmed in our data. The model first builds an internal chain of steps and then provides the result. This helps with math, logic, STEM, and code debugging—where the answer needs to be derived sequentially rather than guessed on the first try.
Yes, both function calling and structured JSON output are supported. You can connect the model to external tools, databases, and services, receive answers in a specified schema, and parse them without post-processing. In BotHub, this is available via a unified OpenAI-compatible API.
You can send images along with text: the model reads screenshots, diagrams, charts, and tables. Document uploads are also supported, so bulky files can be analyzed directly in the request. There is no information in our data regarding audio or video—you should test the required format with your specific task.
There are no separate metrics for languages in our data, so we cannot promise exact figures—it is faster to test it with your task in a couple of requests. BotHub itself is Russian-friendly: payment with Russian cards in rubles, access without VPN or foreign cards, and you can compare answers from other models side-by-side.