Qwen3.5 Plus
Neural Network
Qwen3.5 Plus is a Qwen vision-language model with a hybrid attention mechanism and MoE for text and image processing.
Max answer length
(in tokens)
Context size
(in tokens)
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3.5 Plus?
Qwen3.5 Plus is a model from the Qwen3.5 line with native vision support: it accepts text and images in a single request. It is based on a hybrid architecture combining linear attention with a sparse Mixture of Experts (MoE), providing higher inference efficiency. Via BotHub, the model is accessible without a VPN or foreign card: pay with Russian cards in rubles, only for tokens used, which do not expire. Over 250 models are available in one window, allowing you to compare responses and switch between them without rewriting integrations: the BotHub API is unified. Traffic is encrypted via AES-GCM, chat history is not saved, and teams have access to contracts, invoices, electronic document management, and admin panel limits. Simple scenarios: analyze a screenshot, diagram, or document snapshot to get an image description; extract data from an image into a structured format; build a multimodal assistant via API; run a task through multiple models to choose the best one; maintain request flow where response speed is critical.Frequently asked questions about Qwen3.5 Plus
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.
Qwen3.5-plus-02-15 is a text model with reasoning capabilities: suitable for coding (writing, refactoring, debugging), analyzing long documents, STEM tasks, and logic. It accepts text, images, and documents, and calls external functions. Context is one million tokens: a large repository or a collection of PDFs fits in a single request.
Up to 65,536 tokens per response, which is dozens of pages of text. Enough for a large technical document, an entire code module, or a detailed analysis with reasoning. If the response hits the limit, ask it to continue: the one-million-token context window is enough to hold the entire conversation.
Yes, reasoning before answering is confirmed: the model first builds an internal chain of steps and only then provides the result. This is noticeable in math, logic, complex debugging, and multi-step instructions. Reasoning consumes output tokens, so for simple requests, ask it to answer briefly.
Yes. Function calling and structured JSON output are supported: describe the schema and get a response that your code can parse without workarounds. Convenient for agents, document parsing, and integrations; via the unified OpenAI-compatible BotHub API, the model connects without rewriting code.
Images and documents — yes: interface screenshots, diagrams, tables, PDFs, or contracts can be sent with the question; the model reads them and responds with text. Audio and video are not noted in our data, so for voiceovers or recording analysis, choose a specialized model in BotHub.
Language characteristics are not noted in our data for this model, so we won't make specific promises — test it with your tasks. In BotHub, it's simple: one interface, switching between models in a couple of clicks, pay-as-you-go for tokens with Russian cards without a VPN.