DeepSeek V4 Pro
Neural Network
DeepSeek V4 Pro is a Mixture-of-Experts model with 1M token context: DeepSeek for coding and complex reasoning
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 384 000 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 Pro?
DeepSeek V4 Pro is a large-scale model from DeepSeek built on a Mixture-of-Experts architecture: out of 1.6 trillion parameters, about 49 billion are activated per request, ensuring efficient resource usage. The 1 million token context window allows you to keep an entire repository, extensive documentation, or long correspondence in a single chat, with the model's main strengths being advanced reasoning and coding. In BotHub, it is accessible without a VPN or foreign card: you pay with a Russian card only for the tokens used, not for a subscription. Other neural networks from the 250+ model catalog are available in the same window, allowing you to compare answers in a few clicks. A unified OpenAI-compatible API lets you switch projects to another model without rewriting the integration. Chats are encrypted, and for companies, we offer contracts, invoices, and an admin panel with limits. Developers use DeepSeek for refactoring and reviewing entire large projects, analyzing logs, and debugging. Analysts use it for long reports and specifications, while engineers and students use it for math and STEM tasks with step-by-step derivation.Frequently asked questions about DeepSeek V4 Pro
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 accepts text, documents, and images as input, so you can provide it with a repository, a long report, a diagram, or an interface screenshot. A context of up to 1,048,576 tokens keeps a large project in a single chat. It is suitable for writing and refactoring code, debugging, STEM calculations, and analytics.
The output limit is 384,000 tokens per response, which is the size of a small book. It is enough to generate an entire code module, detailed documentation, or a comprehensive analysis without splitting it into parts. You can easily set the desired length directly in the prompt; otherwise, the model will respond based on the situation.
Yes, a reasoning mode is specified in the characteristics: before the final answer, the model processes the task step-by-step. This significantly helps with math, logic, parsing long requirements, and finding the cause of a bug. The trade-off for accuracy is a higher number of tokens and waiting time.
Yes, both function calling and structured JSON output are supported. Describe the tools in your request, and the model will decide when to trigger them and with what arguments. Through the unified OpenAI-compatible BotHub API, this can be integrated into agents and pipelines without rewriting the integration.
The model accepts images and documents: you can ask questions about an image, analyze a diagram, table, or error screenshot. Audio and video support is not noted in our data, so for voiceovers or analyzing recordings, it is better to choose a specialized model — in BotHub, it is right next to it, in the same window.
There are no separate metrics for languages in our data, so we will not promise accuracy — it is more reliable to test it on your own task. In BotHub, payment is based on actual token usage, without a subscription or a foreign card, and other models are available nearby: compare the answers and choose the one that suits you.