Qwen3.8 Omni Flash
Neural Network
Qwen3.8 Omni Flash is Alibaba's omni-modal model with native audio and video understanding for recording analysis and agentic tasks
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 000 000 tokens
- Max answer length
- 131 072 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works Qwen3.8 Omni Flash?
Qwen3.8 Omni Flash is an omni-modal reasoning model from Alibaba, the first in the Qwen line built around agentic capabilities and native audio and video understanding. It analyzes recordings as a whole rather than separating tracks and frames, making it suitable for analyzing and summarizing audio and video materials, as well as for scenarios where the model must sequentially perform task steps. In BotHub, Qwen3.8 Omni Flash is accessible from Russia without a VPN or foreign card; payment is in rubles based on actual usage, and unused tokens remain on your balance. Over 250 models are available in the same window: you can compare Qwen's responses with others and switch without rewriting integrations, thanks to a unified, OpenAI-compatible API. Data is transmitted encrypted; for companies, contracts, invoices, electronic document management (EDO), and an admin panel with limits are available. It is useful for compiling meeting or webinar summaries from video recordings, analyzing support calls, describing video content before publication, building agents that work with multimedia materials, and accelerating research on long recordings.Frequently asked questions about Qwen3.8 Omni 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.
The model works with text, documents, and images as input, can reason before answering, and call functions. It is suitable for analyzing large documents and codebases, refactoring and debugging, STEM tasks, and agentic scenarios with tools. Context is up to one million tokens.
In a single response, the model outputs up to 131,072 tokens — that's tens of thousands of words, meaning a voluminous document, a large code module, or a detailed analysis in its entirety, without manual stitching. The context window is up to one million tokens, so source files will fit along with the response.
Yes, reasoning before answering is confirmed in our data. The model first builds a chain of steps and then provides the result — this helps with math, logic, code analysis, and multi-step tasks. For simple queries, this mode is not always necessary, but it significantly improves accuracy on complex ones.
Yes, the model supports function calling and structured JSON output. You describe the tools, it decides when to call them, and returns the answer in the specified schema — convenient for agents and integrations. In BotHub, this is available via a unified OpenAI-compatible API.
According to our data, images and documents are accepted as input: you can send a screenshot, diagram, photo, or PDF and ask to analyze the content. Audio and video are not marked for us — this is not a denial, just that there is no such label, so please check the format in the interface before uploading.
There is no information about languages in our data, so we will not promise anything specific. The most honest way is to test it on your task: in BotHub, you open the model in the same window as others and compare the responses. Access from Russia without a VPN or foreign card, payment in rubles.