Qwen3 VL 30B A3B Instruct
Neural Network
Qwen3 VL 30B A3B Instruct is a Qwen multimodal model for text generation and image/video understanding.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 VL 30B A3B Instruct?
Qwen3 VL 30B A3B Instruct is a multimodal model from Qwen that combines text generation with image and video understanding. The Instruct variant is fine-tuned for following instructions in general multimodal tasks. Its strength lies in visual data perception: it analyzes frame content and responds with coherent text. In BotHub, it is available without a VPN or foreign card: you pay with a Russian card in rubles for what you use, tokens do not expire, and no subscription is required. With over 250 models in one window, you can compare responses or switch to another neural network in seconds. A unified OpenAI-compatible API allows you to do the same in code without rewriting integrations. Chats are encrypted via AES-GCM, data is not saved, and for companies, we offer contracts, invoices, EDI, and an admin panel with limits. Use cases: analyzing images (diagrams, screenshots, photos), extracting meaning from video, preparing descriptions for visual materials, building assistants that process images and questions, and automating support tickets with attached screenshots.Frequently asked questions about Qwen3 VL 30B 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 multimodal model from the Qwen family: it accepts text, images, and documents, and responds with text. It is suitable for analyzing screenshots, diagrams, tables, and scans, working with code, refactoring and debugging, as well as long analytical tasks and STEM questions.
In a single response, the model generates up to 16,384 tokens — enough for a long article, a detailed document analysis, or a large code module. The context window is 262,144 tokens, so you can keep large files in the dialogue and continue the response with the next prompt.
A separate hidden reasoning mode is not noted in our data. However, you can ask the model to break down the task step-by-step directly in the response — for math, logic, and code analysis, this technique usually significantly improves the quality of the result.
Yes, the model supports function calling and structured JSON output. This allows you to build agents, connect external tools, and receive predictable objects for your backend. A unified OpenAI-compatible API is available in BotHub, so you won't have to rewrite your integration.
The model accepts text, images, and documents as input: screenshots, photos, diagrams, PDFs, and tables. The model will describe the content, extract data, and compare multiple images. Audio and video are not listed as supported input formats in our data; the model responds with text.
Qwen models were created as multilingual, and they work confidently with Russian-language queries. The best way to check for your task is to send a couple of typical prompts in BotHub: payment is based on actual token usage, without a subscription, VPN, or foreign card.