Qwen3 235B A22B 2507
Neural Network
Qwen3 235B A22B 2507 is a multilingual instruct model by Qwen using MoE architecture: Qwen3 API for text generation and instructions.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 235B A22B 2507?
Qwen3 235B A22B 2507 is an instruct model by Qwen based on the Qwen3 235B architecture with a mixture of experts: 22 billion active parameters are used per pass, combining the knowledge volume of a large model with efficient computation. It is multilingual and tuned for instruction following and general-purpose text generation. In BotHub, you can use it without a VPN or foreign card, pay with Russian cards in rubles, and pay per token, with all chats encrypted via AES-GCM. Access over 250 neural networks in one window: easily compare answers, and use a unified OpenAI-compatible API to connect Qwen3 to your service and switch models without rewriting integrations. Companies get access to contracts, invoices, electronic document management, and an admin panel with limits. The model is useful for drafting articles or emails, summarizing and rewriting long documents, breaking down requirements, supporting chatbot scenarios, and running a single prompt through multiple models to choose the best response.Frequently asked questions about Qwen3 235B A22B 2507
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 large Qwen text model with a 262,144 token context. It analyzes long documents, writes and refactors code, helps with analytics, reasoning, and STEM tasks, and accepts images as input. Suitable for agents, knowledge base work, and voluminous technical documentation.
The output limit is 235,929 tokens per response, which is the volume of a small book. Therefore, the model provides detailed technical analysis, large code files, or comprehensive reports in full, so you don't have to split the task or ask it to continue.
Yes, reasoning mode is confirmed: before the final answer, the model works through the task step-by-step. This is noticeable in math, logic, code debugging, and multi-step document analysis. Thanks to the large context, the chain of thought fits comfortably alongside the source data.
Yes, both function calling and structured JSON output are supported — the model can be connected to tools, databases, and external services. In BotHub, this works via a unified OpenAI-compatible API, so switching to another model does not require rewriting the integration.
In addition to text, images and documents are accepted as input: the model analyzes and describes screenshots, diagrams, charts, and files in text. Audio and video are not supported in our data — for those, it is more convenient to use a specialized model in the same BotHub window.
Qwen3 is being developed as a multilingual family, and the model works confidently with Russian-language queries. However, the result depends on the specific task, so test it with your own material: in BotHub, you can immediately compare the answer with another model and choose the suitable one.