DeepSeek R1 0528
Neural Network
DeepSeek R1 0528 is an open reasoning model from DeepSeek for logic, math, and programming tasks.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works DeepSeek R1 0528?
DeepSeek R1 0528 is the May update of the R1 reasoning model from DeepSeek: 671 billion parameters, with 37 billion activated per request. The model belongs to the reasoning class: before answering, it breaks down the task into steps, and the reasoning tokens themselves are open, so the solution process can be verified rather than taken on faith. Like the entire R1 line, it is open-source. Through BotHub, the model is available without a VPN or foreign card — payment with Russian cards in rubles, pay-as-you-go for consumed tokens, no subscription. Over 250 neural networks are available in one window: answers are easy to compare, and a unified API allows switching models without rewriting integration. Correspondence is encrypted via AES-GCM, and for companies, there is a contract, invoice, EDI, and admin panel with limits. Use R1 0528 for analyzing mathematical and engineering problems with verifiable logic, for writing and debugging code, for refactoring someone else's project, for analyzing technical documents and controversial points in them, as well as for educational breakdowns where it is important to show not just the result, but the reasoning itself.Frequently asked questions about DeepSeek R1 0528
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 text model with a focus on reasoning: solving complex problems, math and STEM, working with code — writing, refactoring, debugging. It accepts not only text but also documents with images as input. The 163,840 token context allows you to work with long specifications, logs, and entire modules.
The maximum for one response is 32,768 tokens. This is enough for a detailed technical breakdown, a long article, or a large code file. Keep in mind that part of the budget goes to internal reasoning, so it is more convenient to request very long texts in parts and assemble them sequentially.
Yes, this is its key feature: before the final answer, the model builds a chain of reasoning, breaks down the task into steps, and checks intermediate conclusions. This mode significantly helps in math, logic, and code debugging, where the solution process is important, not just the result.
Yes, the model supports function calling and structured JSON output. It is convenient to integrate into agents and pipelines where the response goes directly into code or a database. A unified OpenAI-compatible API is available in BotHub, so you can switch to another model without rewriting the integration.
The model accepts images and documents as input — you can ask it to analyze a screenshot, diagram, table, or PDF and get a text response. For working with audio and video, BotHub has separate specialized models that are easy to connect in the same interface.
DeepSeek models were trained on multilingual data, and the R1 line works confidently with Russian-language queries: it understands phrasing, terms, and the context of the task. It is better to check the quality for your specific scenario live — in BotHub, this is done without a VPN or foreign card, with payment in rubles.