Qwen3 235B A22B
Neural Network
Qwen3 235B A22B is a mixture-of-experts model from Qwen with a switchable reasoning mode for complex logic and math.
Max answer length
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Context size
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Prompt cost
(per 1M tokens)
Answer cost
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How it works Qwen3 235B A22B?
Qwen3 235B A22B is a model from the Qwen team based on the mixture-of-experts architecture: out of 235 billion parameters, 22 billion are used per pass, making large-scale computations more economical. A distinctive feature is the smooth switching between a reasoning mode, where the model unfolds a chain of thought for complex logic and math, and a standard mode for quick, to-the-point answers. Via BotHub, the model is accessible from Russia without a VPN or foreign card; payment is in rubles and based on actual usage—for consumed tokens that do not expire. More than 250 neural networks are gathered in one window with Qwen3, so you can compare answers in a couple of clicks, and a unified OpenAI-compatible API allows you to change the model in your product without rewriting the integration; correspondence is encrypted, and companies have access to a contract, invoice, and admin panel with limits. Use this model when you need to break down a multi-step task, check a math solution, compile an analytical report, prototype assistant logic, or run the same query through several models to choose the best answer.Frequently asked questions about Qwen3 235B A22B
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.
Qwen3-235B-A22B works with text and documents, accepts images as input, reasons before answering, and calls external functions. It is suitable for code analysis and refactoring, analytics, STEM tasks, and building agents. The context is 40,960 tokens, which is enough for a large file or a collection of materials.
In a single response, the model outputs up to 8,192 tokens—this is enough for an article, a detailed analysis, or a large code fragment. If you need a larger result, break the task into parts and continue in the same dialogue: the 40,960-token context will retain the previous steps.
Yes, reasoning before answering is a confirmed capability of this model. It breaks the task into steps, checks intermediate conclusions, and only then formulates the result. This is noticeable in math, logic, code debugging, and where a chain of arguments is important rather than an instant response.
Yes. The model supports function calling and structured JSON output, making it convenient to use in agents, connect to search, databases, and internal services. In BotHub, this works via a unified OpenAI-compatible API: you won't have to rewrite the integration scheme when switching models.
You can upload images and documents: the model will analyze a screenshot, diagram, chart, or PDF and respond with text. Audio and video input are not noted in our data—for such tasks, there are separate models in the BotHub catalog, and you can switch to them in the same window.
We do not have data on languages, so we make no promises in advance. It is easier to check with your own task: send a typical query and compare the answer with another model in the same BotHub window. Payment is based on actual token usage, no subscription is needed, and access works without a VPN or foreign card.