Qwen3 235B A22B Thinking 2507
Neural Network
Qwen3 235B A22B Thinking 2507 is an open Qwen MoE model for complex reasoning with a 262,144 token context.
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 Thinking 2507?
Qwen3 235B A22B Thinking 2507 is an open-weights model by the Qwen team with a Mixture-of-Experts architecture: out of 235 billion parameters, 22 billion are activated at each step, and the thinking mode is designed for multi-step reasoning. The native 262,144 token window allows you to submit voluminous documentation, long correspondence, or a large codebase in a single request and maintain coherence throughout. Via BotHub, the model is accessible from Russia without a VPN or foreign card; you pay with a Russian card in rubles only for the tokens used, which do not expire. Other neural networks work alongside it in the same window: answers are easy to compare, and you can switch between models via a unified OpenAI-compatible API without rewriting your integration. It is typically used to solve multi-step mathematical or engineering problems, analyze long technical documents, study unfamiliar projects and suggest code improvements, compile analytical summaries from large datasets, or integrate a reasoning model into your own product.Frequently asked questions about Qwen3 235B A22B Thinking 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 Qwen text model with a reasoning mode: it is suitable for solving complex problems, mathematics and STEM, writing and refactoring code, and analyzing long documents. The context is 131,072 tokens; you can input text, documents, and images, and it supports function calling.
In a single response, the model can output up to 117,964 tokens — enough for a voluminous article, detailed documentation, or a large code module in its entirety. Remember that the question and answer together must fit within the total 131,072 token context.
Yes, reasoning before answering is a key feature of this version. The model first runs through a chain of thought, checks intermediate steps, and only then formulates a conclusion. This significantly helps in mathematics, logic, code debugging, and tasks where a correct answer is more important than a fast one.
Yes, the model supports function calling and structured JSON output. You describe the schema and tools, and it returns an object ready for parsing or a call to the required method. Convenient for agents, pipelines, and integrations where the response goes directly into code.
Images and documents — yes: according to our data, the model accepts them as input along with text, so you can send a screenshot, diagram, or file and ask it to analyze them. Audio and video are not listed in the capabilities; BotHub has separate models for those.
The Qwen3 series models are multilingual, and Russian is among those supported. It is easier to check the quality on your tasks live: in BotHub, the model is available from Russia without a VPN or foreign card, with payment in rubles, so you can compare its answers with other models in the same window.