Qwen3.5 Flash
Neural Network
Qwen3.5 Flash is a multimodal model from Qwen for text and visual tasks, available via a unified BotHub API.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 000 000 tokens
- Max answer length
- 65 536 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3.5 Flash?
Qwen3.5 Flash is a natively multimodal model in the Qwen lineup that works with both text and images. It is based on a hybrid architecture: a linear attention mechanism combined with a sparse mixture of experts, making the model more computationally efficient and faster at generating responses. This balance is convenient for high-volume requests where low latency is critical. Via BotHub, the model is accessible from Russia without a VPN or foreign card; payment is in rubles and pay-as-you-go: only consumed tokens are charged, and the balance does not expire. With over 250 models in one window, you can compare responses or switch to another neural network without rewriting your integration — the API remains unified. Dialogs are encrypted, and companies have access to contracts, invoices, electronic document management (EDO), and an admin panel with limits. It is useful for analyzing screenshots, diagrams, or document photos, automating support ticket processing, extracting data from images directly in the backend, or quickly understanding third-party code.Frequently asked questions about Qwen3.5 Flash
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 text model with a one-million token context: it writes and refactors code, debugs errors, solves math and logic problems, and summarizes long documents. It accepts text, files, and images as input and can call external tools. Suitable for analytics, support, and integration into services.
In a single response, the model generates up to 65,536 tokens — that's dozens of pages of text. Enough to generate a large code module, a detailed document analysis, or an entire long article without breaking the task into parts. If you need more, continue generation with the next request.
Yes, the reasoning mode is confirmed: before answering, the model builds a chain of intermediate steps and only then formulates a conclusion. This significantly helps with math, logic problems, code debugging, and multi-step document analysis. For simple requests, the difference is smaller, but the response arrives faster.
Yes, the model supports function calling and structured JSON output, making it convenient to use as the foundation for agents and pipelines: it selects the necessary tool itself and returns the answer in the specified schema. In BotHub, this works via a unified OpenAI-compatible API.
According to our data, the model accepts images and documents as input: you can send an interface screenshot, a diagram, a photo of text, or a PDF and ask it to analyze the content. Audio and video support is not noted in our data, so count on text, files, and images.
There is no data on languages in our model card, so we won't promise anything specific. It's easier to test it with your own task: send a typical request and evaluate the result yourself. In BotHub, models are gathered in one window, so comparing several options and choosing the right one takes a couple of minutes.