Qwen3 VL 32B Instruct
Neural Network
Qwen3 VL 32B Instruct is a multimodal model from Qwen for analyzing images, video, and text in a single prompt.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 VL 32B Instruct?
Qwen3 VL 32B Instruct is a multimodal model from Qwen with 32 billion parameters that works equally well with text, images, and video: it doesn't just recognize objects in a frame, but builds reasoning based on what it sees and links it to the text part of the prompt. Deep visual perception is combined here with a strong text foundation, making the model suitable for tasks requiring precise content analysis rather than general descriptions. In BotHub, you can connect Qwen3 VL 32B Instruct without a VPN or foreign card — payment is made with Russian cards in rubles and charged per token, which do not expire. All 250+ models are gathered in one window, so you can compare answers and switch to another model without rewriting the integration: a unified OpenAI-compatible API, AES-GCM encryption, and for companies — a contract, invoice, EDI, and admin panel with limits. It is useful for analyzing interface screenshots and layouts, extracting data from documents, tables, and diagrams, analyzing video frames, preparing descriptions for image catalogs, and testing hypotheses where visual context is important.Frequently asked questions about Qwen3 VL 32B 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 Qwen model: it accepts text, images, and documents, and responds with text. It is suitable for analyzing screenshots, diagrams, tables, and scans, working with code, reasoning on STEM tasks, and long documents — context up to 262,144 tokens.
Up to 32,768 tokens in a single response — this is enough for a voluminous document analysis, a large code module, or a detailed technical instruction. If the material is even larger, divide the task into parts: the input context of 262,144 tokens allows you to keep the entire project in the dialogue.
A separate reasoning mode is not noted in our data, but the model handles step-by-step analysis well: ask it to write out the solution step-by-step or explain the logic of the output. For multi-stage tasks, this usually provides a more accurate and verifiable result.
Yes, the model supports function calling and structured JSON output. This is convenient for agents, parsing documents into ready-made fields, and integrations with your services. A unified OpenAI-compatible API is available in BotHub, so switching between models does not require rewriting code.
Images and documents — yes, this is a key part of the model: you can send a photo, screenshot, diagram, PDF, or table and get a text analysis. Audio and video input is not noted in our data, so for such tasks, choose a specialized model in BotHub.
Qwen3-VL is a multilingual line, and the model works with Russian queries: formulate the task in the usual way, specify the response format and context. It is convenient to check the quality on your own data — in BotHub, the model is available without a VPN or foreign card, with payment in rubles.