Qwen3 Coder
Neural Network
Qwen3 Coder is a Qwen model for code generation, tool use, and agentic development tasks.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 Coder?
Qwen3 Coder is Qwen3-Coder-480B-A35B-Instruct, a Qwen model with a Mixture-of-Experts architecture: 480 billion parameters, with 35 billion activated per request. It is built for agentic programming: function calling, working with external tools, and reasoning in long contexts. In BotHub, Qwen3 Coder works from Russia without a VPN or foreign card, and you pay per token in rubles without a subscription. With over 250 models in one window, you can easily run a task through several and compare results. A unified OpenAI-compatible API allows you to switch models in your project without rewriting the integration, traffic is encrypted, and companies have access to contracts, invoices, electronic document management, and an admin panel with limits. It is useful if you are writing and refactoring code in multiple languages, analyzing someone else's project to find a bug, building an agent that calls functions and external tools, generating tests and documentation, or migrating legacy code to a new stack.Frequently asked questions about Qwen3 Coder
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.
Qwen3-coder works with text, documents, and images, reasons before answering, and calls external functions. It is suitable for writing and refactoring code, debugging, code review, working with large repositories and technical documentation, as well as for agentic scenarios with tools.
In a single response, the model outputs up to 65,536 tokens — that's dozens of pages of text or a large code file in its entirety. If the task is larger, break it into parts: the million-token context window allows you to keep the entire project in one dialogue.
Yes, reasoning before answering is confirmed by our data. The model breaks down the task into steps before providing a result, which significantly helps with debugging, architecture design, and algorithmic tasks. On complex queries, the response takes longer, but the solution logic is more precise.
Yes, both. Function calling and structured JSON output are supported, so the model can be connected to your tools, databases, and internal services. It's convenient via the unified BotHub OpenAI-compatible API: there's no need to change the integration scheme, just switch the model.
The model accepts images and documents as input — send a screenshot of an error, an interface diagram, or a specification file. There is no mention of audio or video in our data; for such tasks, BotHub has separate models available in the same window.
We do not have specific data on qwen3-coder's language capabilities, so it is better to test the model on your task — it's quick. BotHub itself is fully in Russian: access from the Russian Federation without a VPN or foreign card, payment with Russian cards, and you can switch to another model without rewriting the integration.