Qwen3 Coder Next
Neural Network
Qwen3 Coder Next is an open MoE model by Qwen for coding agents and local development, available via API on BotHub.
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How it works Qwen3 Coder Next?
Qwen3 Coder Next is an open-weights language model from Qwen, built for coding agents and local development workflows. It features a sparse MoE architecture: 80B total parameters with only 3B active per token, ensuring efficient computation without sacrificing scale. Via BotHub, it is accessible from Russia without a VPN or foreign card: pay in rubles for tokens as you use them, not via subscription, and unused tokens do not expire. Access over 250 models in one window—compare responses for the same task and switch without rewriting integrations, thanks to a unified, OpenAI-compatible API. Chats are encrypted via AES-GCM, and teams get access to contracts, invoices, EDI, and an admin panel with limits. Typical tasks: connect the model as an engine to an agent that edits repositories and runs tests; analyze unfamiliar code and explain its logic; find the cause of a failing test and suggest a patch; migrate a module from one framework to another; build an internal review service where the model performs the first pass on pull requests.Frequently asked questions about Qwen3 Coder Next
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: generating functions and modules, refactoring, analyzing third-party repositories, debugging, and explaining logic. A 262,144-token context window allows you to upload large projects or documentation in full and discuss them in a single dialogue.
The output limit is up to 235,929 tokens per response, enough for an entire module with tests, detailed code analysis, or large technical documentation. If the response cuts off, just ask it to continue—the model will pick up where it left off.
We have not noted a separate reasoning mode for this model in our data, so we cannot promise it. In practice, a simple trick helps: ask it to break down the task step-by-step and show its thought process before providing the final code—the quality of the response improves significantly.
Yes, the model supports function calling and structured JSON output. This is useful for agents, autotests, linters, and integrations with your services. Through the unified OpenAI-compatible BotHub API, you connect it once and switch to other models without rewriting code.
Images and documents are accepted as input: you can send an error screenshot, architecture diagram, interface layout, or specification file and discuss them along with the code. Audio and video input is not supported; the model responds with text.
The Qwen family of models was trained on multilingual data, so they handle tasks in Russian confidently. Formulate requirements just as you would for a colleague: what the stack is, what result is needed, and what the constraints are. On BotHub, the model is available from Russia without a VPN or foreign card, with payment in rubles.