Qwen3 VL 235B A22B Thinking
Neural Network
Qwen3 VL 235B A22B Thinking is a multimodal Qwen model for reasoning over images, video, and text in STEM tasks.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 VL 235B A22B Thinking?
Qwen3 VL 235B A22B Thinking is a multimodal Qwen model that combines text generation with image and video understanding. The Thinking version is tuned for step-by-step reasoning and focused on STEM and mathematics: the model breaks down the problem before providing an answer. In BotHub, it is available from Russia without a VPN or foreign card, and you pay with Russian cards in rubles for tokens used. Nearby, in one window, there are over 250 neural networks that are easy to switch between and compare answers, and a unified OpenAI-compatible API allows you to connect the model to your service and replace it with another without rewriting the integration. Chats are encrypted via AES-GCM; for companies, there is a contract, invoice, EDI, and an admin panel with limits. Simple scenarios: solve a problem with a drawing, diagram, or formula, extract insights from charts and tables in a report, describe and structure video content, check the solution process from a photo of notes, or embed visual analysis into your product via API.Frequently asked questions about Qwen3 VL 235B A22B 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 model with a reasoning mode: it accepts text, images, and documents as input and responds with text. It is suitable for analyzing screenshots, diagrams, and tables, analyzing long documents, coding and STEM tasks, as well as scenarios with function calling.
In a single response, the model outputs up to 32,768 tokens — enough for a lengthy article, detailed analysis, or a large code module. The context window is 131,072 tokens, so long input and long output fit into a single request without splitting.
Yes, this is the thinking version: before the final answer, the model builds a chain of reasoning. This helps with tasks where multiple steps are important — mathematics, logic, code debugging, analyzing conflicting data. For simple questions, this mode may respond slightly slower, but more accurately.
Yes, the model supports function calling and structured JSON output. You can connect it to your tools, databases, and services, receive answers in a specified schema, and use the unified OpenAI-compatible BotHub API to switch to another model if needed without rewriting the integration.
According to our data, images and documents are accepted as input: send a photo, screenshot, diagram, or file and ask to analyze the content. Audio and video input, as well as generation of anything other than text, are not noted in our data — the easiest way to check the scenario is with your own task in BotHub.
The language of prompts and responses is not described in our data, so we will not promise anything specific — it is better to test the model with your own text. BotHub itself works from Russia without a VPN or foreign card, payment is via Russian cards, and tokens do not expire.