Qwen3 VL 8B Instruct
Neural Network
Qwen3 VL 8B Instruct API is a multimodal model from the Qwen3-VL family for understanding text, images, and video.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 262 144 tokens
- Max answer length
- 235 929 tokens
- Capabilities
- function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3 VL 8B Instruct?
Qwen3-VL-8B-Instruct is a compact multimodal model from the Qwen3-VL line by Qwen. It processes text, images, and video. The Interleaved-MRoPE mechanism helps maintain long sequences of frames and reason about events over time rather than just describing individual frames. The Instruct version is tuned for following instructions. In BotHub, the model is accessible without a VPN or foreign card, with payment via Russian cards based on usage. Over 250 models are gathered in one window, making them easy to compare and switch between, and a unified OpenAI-compatible API allows replacing the model in your project without rewriting the integration. Correspondence is protected by AES-GCM encryption, and companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. It is useful if you are analyzing screenshots and scans, extracting data from documents and tables, describing photos for a catalog, searching for specific moments in video recordings, or building a multimodal assistant via API.Frequently asked questions about Qwen3 VL 8B 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.
Qwen3-VL-8B-Instruct works with text, images, and documents: it analyzes screenshots, diagrams, tables, and scans, extracts data, answers questions about content, and helps with code and analytics. A 256,000-token context allows you to work with long materials and entire collections of files.
Up to 32,768 tokens per response — roughly a long article or a large code snippet. If the material is longer, ask it to continue: the model maintains a 256,000-token context and will easily assemble the result in several steps.
We have not noted a separate reasoning mode for this model in our data. However, you can ask it to reason step-by-step directly in the prompt: break down the task and show the solution process. If you need built-in reasoning, it is easy to switch to a reasoning model in BotHub.
Yes. The model supports function calling and structured JSON output, making it easy to integrate into pipelines: provide a response schema and get ready-to-use fields for a database or service. It works via the unified OpenAI-compatible BotHub API.
Images and documents — yes: upload screenshots, photos, diagrams, PDFs, and scans, and ask questions about the content. Audio and video input is not noted in our data, so for sound and clips, it is more convenient to choose a specialized model in the same BotHub window.
We do not publish data on prompt and response languages for this model, so we cannot promise anything specific. The best way is to test it with your task: send a typical request in BotHub, compare the result with other models in the same window, and choose the one that fits.