DeepSeek V4 Flash 0731
Neural Network
DeepSeek V4 Flash 0731 is a MoE model from DeepSeek for reasoning and agent tasks, DeepSeek for code in one BotHub window.
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 Flash 0731?
DeepSeek V4 Flash 0731 is a version of the DeepSeek model with a sparse mixture-of-experts architecture: out of 284 billion parameters, 13 billion active ones work per request, making computation efficient. This post-training revision is tuned for three things: writing and analyzing code, step-by-step reasoning, and agent scenarios where the model guides the task step-by-step. In BotHub, it is accessible from Russia without a VPN or foreign card; payment is made with Russian cards in rubles based on usage, and unused tokens do not expire. Over 250 other neural networks are available in the same window, allowing you to compare answers and switch between them, while a unified OpenAI-compatible API lets you change models in your product without rewriting the integration. For developers, DeepSeek is convenient for refactoring, analyzing third-party repositories, and debugging; for analysts, it's great for long logical chain tasks; for teams, it's useful for building agents. In the corporate environment, contracts, invoices, and electronic document management are available, and correspondence is encrypted with AES-GCM.Frequently asked questions about DeepSeek V4 Flash 0731
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 of about a million tokens. It is suitable for working with long documents and large codebases, refactoring and debugging, log analysis, analytics, STEM tasks, and reasoning. It accepts text, documents, and images, and can call tools.
According to our data, the output limit is up to 943,718 tokens, which is a very large volume for a single response. It is enough for detailed technical documentation, multi-file code generation, or a detailed analysis of a large source file without splitting the task into dozens of requests.
Yes, reasoning mode is supported: the model builds a chain of steps before the final answer. This significantly helps in mathematics, logic, code analysis, and multi-step tasks where it is important not just to guess the result, but to arrive at it sequentially.
Yes, the model supports function calling and structured JSON output. This allows for building agents, connecting external tools, and receiving predictable responses for parsing. A unified OpenAI-compatible API is available in BotHub, so you won't have to rewrite your integration.
According to our data, text, images, and documents are accepted as input. You can send an interface screenshot, diagram, chart, or PDF and ask to analyze the content. There are no notes in our data regarding audio and video processing, so you should not count on them.
DeepSeek models are multilingual, and this version generally works confidently with Russian queries. There are no specific quality measurements for Russian in our data, so it is most reliable to test it on your own tasks: in BotHub, you can compare the answer with other models in one window.