Qwen 2.5 7B Instruct
Neural Network
Qwen 2.5 7B Instruct is a Qwen language model with expanded knowledge and improved skills in coding and mathematics.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen 2.5 7B Instruct?
Qwen 2.5 7B Instruct is a compact language model from the Qwen 2.5 series, featuring significantly increased knowledge and improved coding and math skills compared to Qwen2. Its 7B size makes it a convenient option for streaming tasks where a large model is overkill for every request. In BotHub, the model is accessible from Russia without a VPN or foreign card: you pay with a Russian card in rubles only for used tokens, and tokens do not expire. With over 250 neural networks available in one window, it's easy to compare answers and switch models without rewriting your integration—the API is unified. Chats are encrypted via AES-GCM and not saved. Companies have access to contracts, invoices, electronic document management, and an admin panel with limits. Use cases are simple: refactor code, write tests, quickly understand someone else's project, solve math or science problems with step-by-step explanations, prepare technical documentation drafts, or use Qwen 2.5 7B Instruct as a base layer in a multi-model service where complex requests are routed to larger models.Frequently asked questions about Qwen 2.5 7B Instruct
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 compact text model from Qwen for everyday text work: answering questions, rewriting and summarizing, analyzing documents, and helping with code and scripts. The 131,072-token context allows you to upload long materials in their entirety and analyze them without splitting them into parts.
The single response limit is 29,491 tokens, which is enough for a long article, detailed documentation, or a long code module in one pass. If the material is larger, break the task into parts and pass the previous fragment in the context, which holds 131,072 tokens.
A separate step-by-step reasoning mode is not noted in our data, so we will not promise it. In practice, a simple trick helps: ask the model to break down the task step-by-step directly in the prompt and show intermediate conclusions before the final result—quality on logic and calculations usually improves.
Yes, the model supports function calling and structured JSON output. You describe the available tools, the model selects the necessary one and returns the arguments in a parsed format. This is convenient for agents, parsing data into a strict schema, and integrations where the response goes directly into code.
In addition to text, images and documents are accepted as input: you can ask to describe an image, extract data from a screenshot, or analyze an uploaded file. This model does not process audio or video—for such tasks in BotHub, switch to a specialized model in the same window.
The Qwen 2.5 series was trained on multilingual data, and Russian is among the supported languages, so the model handles standard tasks well. If the result seems weak on your material, compare the answer with another model in the same interface—switching takes a couple of seconds.