Qwen3 Next 80B A3B Instruct
Neural Network
Qwen3 Next 80B A3B Instruct is a Qwen instruct chat model for reasoning, Q&A, and coding tasks in BotHub.
Max answer length
(in tokens)
Context size
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Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 Next 80B A3B Instruct?
Qwen3 Next 80B A3B Instruct is an instruct model from the Qwen3 Next series, optimized for fast and stable responses without intermediate chain-of-thought: it provides results immediately. Developed by the Qwen team, it targets complex tasks: reasoning, code generation, knowledge-based Q&A, and multilingual scenarios. In BotHub, Qwen connects without VPN or foreign cards; payment is via Russian cards in rubles based on usage, and unused tokens do not expire. With over 250 models in one window, you can compare responses to the same prompt and switch without rewriting integrations: the unified OpenAI-compatible API remains the same. Chats are encrypted via AES-GCM, and for companies, we offer contracts, invoices, EDI, and an admin panel with limits. Useful for writing and refactoring functions, debugging code, gathering reference answers from internal knowledge bases, drafting documentation, and powering chat assistants via API when response speed is critical.Frequently asked questions about Qwen3 Next 80B A3B 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 text model with a 262,144 token context: long documents, code, analytics, rewriting, and structuring materials. You can input not only text but also images and documents, and tool-calling support allows you to integrate the model into your own scenarios and services.
The single response limit is up to 235,929 tokens, meaning the model can output the volume of an entire book at once: a detailed analysis, a large code module, or a series of related sections. In practice, you set the required volume yourself in the prompt and API parameters.
There is no specific note about a reasoning mode in our data, so we do not promise hidden chain-of-thought. However, you can explicitly ask the model to reason aloud: outline steps, check logic, and only then provide a conclusion. This technique helps significantly with complex tasks.
Yes, both are confirmed: the model calls functions and returns structured JSON according to your schema. This is the foundation for agents, parsers, and integrations—the response arrives in a predictable format that goes straight into code without manual parsing. Via the unified BotHub API, this works like a standard OpenAI-compatible request.
Images and documents—yes, these are confirmed input formats: send a screenshot, diagram, PDF, or table and ask to analyze the content. There are no notes about audio or video in our data, so count on text, images, and files.
The Qwen family is originally developed as multilingual, and such models usually handle Russian texts—summarizing, editing, analysis—confidently. The most accurate way to check is with your own material: in BotHub, you can run the same prompt through several models in one window and compare the results.