Qwen3.5 35B A3B
Neural Network
Qwen3.5 35B A3B is a native Qwen vision-language model with a sparse MoE architecture for text and visual tasks.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 262 144 tokens
- Max answer length
- 81 920 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 35B A3B?
Qwen3.5 35B A3B is a native vision-language model from the Qwen3.5 series: it was trained from the start to handle both text and images, rather than having vision added as an overlay. It features a hybrid architecture combining linear attention with a sparse mixture of experts (MoE): out of 35 billion parameters, about 3 billion are activated per request, making inference computationally efficient. In BotHub, the model is accessible from Russia without a VPN or foreign card; payment is made in rubles via Russian cards, charged only for tokens used, which do not expire. With over 250 neural networks available in the same window, you can compare Qwen3.5 35B A3B's responses with other models. A unified OpenAI-compatible API allows you to switch models in your code without rewriting integrations. Chats are encrypted via AES-GCM. For companies, we offer contracts, invoices, electronic document management (EDO), and an admin panel with limits. Use cases: analyzing screenshots, diagrams, and documents, writing and refactoring code, reasoning on STEM tasks, processing support tickets, and prototyping multimodal services via API.Frequently asked questions about Qwen3.5 35B A3B
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.
qwen3.5-35b-a3b accepts text, documents, and images as input, can reason before answering, and call functions. It is suitable for coding tasks—writing, refactoring, debugging—as well as analyzing long files, analytics, STEM tasks, and integration into services via the unified BotHub API.
The single response limit is 65,536 tokens, and the total context window is 262,144 tokens. This is sufficient for comprehensive analysis, long documentation, or large code files in their entirety, without manually splitting tasks into small parts.
Yes, the reasoning mode is confirmed in our data: the model can break down a task into steps before providing a result. This is usually noticeable in math, logic, code debugging, and multi-step instructions where a precise answer is more important than a fast one.
Yes, the model supports function calling and structured JSON output. You can connect it to your tools and databases via the BotHub OpenAI-compatible API, and switch to another model if needed without rewriting the integration.
The model accepts images and documents as input—you can send a screenshot, diagram, table, or PDF and ask it to analyze the content. Audio and video input is not noted in our data, so you should check these formats directly in the BotHub interface.
Our data does not record model characteristics by individual languages, so it is best to test it on your specific task. In BotHub, you can access the model without a VPN or foreign card, pay in rubles as you go, and compare its responses with other models in one window.