Qwen3 Coder Plus
Neural Network
Qwen3 Coder Plus is Alibaba's coding model: agentic programming with tool calling and environment interaction.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 Coder Plus?
Qwen3 Coder Plus is a proprietary model by Alibaba, an evolution of the open Qwen3 Coder 480B A35B. Its profile is agentic programming: the model doesn't just output code snippets but manages tasks through tool calling and environment interaction, executing steps sequentially to reach the result. In BotHub, it works from Russia without a VPN or foreign card; payment is in rubles per token used, and unused tokens do not expire. Other neural networks are available in the same window: you can compare approaches or switch models without rewriting integrations, as the API is unified and OpenAI-compatible. Dialogs are protected by AES-GCM encryption, and for companies, we provide contracts, invoices, EDI, and an admin panel with limits. Connect Qwen3 Coder Plus to your agents and CI scenarios, assign it refactoring and unfamiliar repository analysis, debugging with root cause identification, writing tests and utility scripts, and reviewing changes before handing them to the team.Frequently asked questions about Qwen3 Coder Plus
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, refactoring, debugging, reviewing, and explaining others' solutions. A 128,000-token context allows loading a large file or multiple modules at once. It accepts text, documents, and images as input.
The model outputs up to 65,536 tokens per response — that's dozens of pages of text or a large code module. If the task is larger, just ask it to continue: the context is preserved in a single dialog, and generation can be split into several steps.
In our data, a separate reasoning mode is not noted; the answer comes immediately. However, you can ask it to break down the task step-by-step in the prompt: the model will detail the logic before providing the final code. This helps with complex debugging.
Yes, it supports both function calling and structured JSON output. Describe the tool schema — the model will select the right one and return arguments in a strict format. This is convenient for agents and pipelines where the response goes directly into code without manual parsing.
You can upload images and documents: the model will analyze screenshots of errors, interface diagrams, specification fragments, or PDFs with requirements and respond with text. Audio and video are not listed as input formats in our data, nor is the generation of anything other than text.
The quality of Russian language performance is not documented in our data, so we won't make promises — it's easier to test it with your own task. In BotHub, the interface is in Russian, payment is via Russian cards without VPN, and if you're not satisfied with the result, you can switch to another model in the same window.