Qwen 2.5 Coder 32B Instruct
Neural Network
Qwen 2.5 Coder 32B Instruct is a specialized Qwen model for code: generation, reasoning, and debugging.
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 Coder 32B Instruct?
Qwen 2.5 Coder 32B Instruct is part of the Qwen 2.5 Coder series, developed by the Qwen team specifically for coding tasks; this series was previously known as CodeQwen. Compared to CodeQwen1.5, this version shows significant improvements in code generation, reasoning, and debugging, while the Instruct variant is optimized for following instructions in dialogue. Inside BotHub, the model is accessible without a VPN or foreign card: pay with a Russian card in rubles only for the tokens you actually use, which do not expire. Other models are available in the same window, allowing you to switch and compare answers in a few clicks, while a unified OpenAI-compatible API lets you change models in your code without rewriting the integration. Chats are encrypted via AES-GCM and data is not saved; for companies, we offer contracts, invoices, EDI, and an admin panel with limits. It is useful when you need to build a function or module based on a task description, analyze someone else's repository and explain the logic, find the cause of a failing test, translate a code snippet between programming languages, or clean up old code before a review.Frequently asked questions about Qwen 2.5 Coder 32B 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 Qwen model focused on coding: writing functions and modules, refactoring, explaining third-party code, debugging, and translating between programming languages. The 128,000-token context allows you to upload a large repository or documentation and work with them in their entirety.
The single response limit is 29,491 tokens. This is sufficient for a large code file, an extensive test suite, or a detailed technical analysis with comments. If the task is larger, break it down into steps: the model remembers previous messages within the context.
A separate hidden reasoning mode is not noted in our data. In practice, it helps to ask the model to break down the solution into steps or create a plan first before writing code — this results in a cleaner answer. For complex reasoning chains, you can switch to a reasoning model in BotHub.
Our data does not indicate support for function calling or structured output, so we cannot guarantee it. The easiest way to check is in practice: request the answer strictly in the required format and describe the schema in the prompt. Access is provided via the unified BotHub OpenAI-compatible API.
Images and documents are supported as input: you can send a screenshot of an error, an architecture diagram, an interface fragment, or a file with technical requirements and specifications. There are no notes about audio or video in our data. The model generates text and code as output.
Qwen 2.5 is a multilingual series, and you can set development tasks using familiar phrasing without translation. If an answer seems inaccurate, it is easy to compare the result with another model in the same window in BotHub and choose the best option.