Qwen3 VL 30B A3B Thinking
Neural Network
Qwen3 VL 30B A3B Thinking is a multimodal Qwen model with advanced reasoning for STEM, math, and image/video analysis.
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 Thinking?
Qwen3 VL 30B A3B Thinking is a multimodal model from the Qwen team: it combines text generation with image and video understanding. The Thinking variant reasons extensively and breaks down tasks step-by-step, which helps in math, STEM, and other complex scenarios. In BotHub, access is available without a VPN or foreign card; payment is made with a Russian card in rubles, pay-as-you-go for used tokens, with no subscription. Over 250 neural networks are available in one window, allowing you to switch and compare them on a single prompt. A unified OpenAI-compatible API allows you to connect the model to your product; data is transmitted with AES-GCM encryption and is not stored. It is useful if you are analyzing diagrams, charts, and interface screenshots, asking for explanations of solutions based on photos of problems, searching for specific fragments in screen recordings, preparing descriptions for visual materials, or debugging code with a screenshot of an error.Frequently asked questions about Qwen3 VL 30B A3B 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 multimodal Qwen model with a reasoning mode: it accepts text, images, and documents as input and outputs text. It is suitable for analyzing screenshots, diagrams, and PDFs, analytics, coding tasks, and problems requiring step-by-step analysis. The context window is 262,144 tokens.
In a single response, the model outputs up to 32,768 tokens — enough for extensive material, detailed analysis, or large code snippets. If you need more, ask it to continue: the 262,144-token context allows it to keep the entire dialogue and source files in memory.
Yes, reasoning before answering is confirmed. The model first breaks the task down into steps and only then formulates a conclusion. This is especially noticeable in math, logic, code debugging, and image analysis, where it is important not just to guess the answer, but to reach it sequentially.
Yes, both function calling and structured JSON output are supported, so the model can be connected to your tools to receive answers in a specified schema. In BotHub, this works via a unified OpenAI-compatible API: you can switch models without rewriting your integration.
Images and documents — yes: the model analyzes screenshots, photos, diagrams, and PDFs and responds with text. There is no note in our data regarding audio and video; it is safer to test this scenario with your own task. The model is not noted for native image generation.
The BotHub interface and payment are in Russian and rubles, without VPN or foreign cards. The quality of responses to your specific phrasing should be tested on a real task: send a couple of typical queries and, if necessary, compare the result with another model in the same window.