Qwen3 Next 80B A3B Thinking
Neural Network
Qwen3 Next 80B A3B Thinking is a Qwen reasoning model for complex math, code synthesis, debugging, and agentic tasks.
Max answer length
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Context size
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Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 Next 80B A3B Thinking?
Qwen3 Next 80B A3B Thinking is a reasoning model from the Qwen3-Next line by Qwen that builds an extended chain of thought by default before providing an answer. Its profile includes multi-step tasks: mathematical proofs, code synthesis and debugging, logical chains, and agentic scenarios where showing the path is more important than just guessing the answer. Via BotHub, Qwen3 Next is available without VPN or foreign cards: pay with a Russian card in rubles only for tokens actually used, which do not expire. Over 250 models are available in the same window to switch between and compare answers. A unified OpenAI-compatible API allows connecting Qwen to your code and switching models without rewriting integrations. Chats are encrypted, and companies get contracts, invoices, and an admin panel with limits. Developers can use it for complex bug analysis and refactoring, analysts for multi-step calculations, teachers for step-by-step STEM solutions, and agent-building teams for planning action chains.Frequently asked questions about Qwen3 Next 80B A3B Thinking
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 text model with a reasoning mode. It is suitable for code analysis and refactoring, debugging, STEM and logic tasks, document and long-form material analysis, and input image processing. A 262,144-token context allows keeping entire projects and large files in the dialogue.
The output limit is up to 235,929 tokens per response, meaning the model can provide very voluminous results: detailed analysis, large code fragments, extensive documentation, or a full summary of a long document without splitting the task into dozens of separate requests.
Yes, this is a thinking version: before the final answer, the model builds a chain of thought. This allows it to handle math, logic, multi-step technical tasks, and code debugging more accurately, where it is important to break down the problem step-by-step rather than answering immediately.
Yes, the model supports function calling and structured JSON output. This is convenient for agents, parsing documents into a specific schema, and integrating with your services. BotHub provides a unified OpenAI-compatible API, so you can switch to another model without rewriting code.
According to our data, it accepts text, images, and documents as input, so you can send a screenshot, diagram, or file and ask to analyze the content. Image, audio, or video generation is not supported by this model; BotHub has separate models for such tasks.
The Qwen family of models was trained on multilingual data and works confidently with Russian texts: document analysis, summarization, editing, and technical explanations. You can easily test the quality with your own task — access in BotHub is available without VPN or foreign cards, with payment in rubles.