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Qwen3-Omni-Flash-Realtime

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Overview

This is the real-time version of Qwen3-Omni-Flash multimodal large model, based on the Thinker-Talker Hybrid Expert (MoE) architecture. It supports efficient understanding and speech generation of text, images, audio, and video, enabling text interaction in 119 languages ​​and voice interaction in 20 languages. It supports 49 voice timbres and generates human-like speech for accurate cross-language communication. The model features powerful command following and system prompt customization capabilities, flexibly adapting to dialogue styles and role settings. It is widely used in text creation, voice assistants, multimedia analysis, and other scenarios, providing a natural and smooth multimodal interactive experience. This version is a snapshot from December 1, 2025.

Input

TextImageAudioVideo

Output

TextAudio

Features

Prefix Completion

Enable Partial Mode when calling the Qwen API to make the model continue strictly from your provided prefix text.View docs

Function Calling

Use function calling to connect large language models with external tools and systems.View docs

Cache

Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docs

Structured Outputs

Structured Outputs help ensure the model returns a JSON string in the expected format.View docs

Batches

Asynchronously process requests in batches to reduce costs.View docs

Web Search

Enable web search so the model can answer with real-time retrieved data.View docs

Fine-tuning

Train models on sample data to better adapt them to specific tasks.View docs

Rate Limits & Context

  • Max Input
    49K
  • Max Output
    16K
  • Max Input (Thinking)
    16K
  • Max Output (Thinking)
    16K
  • Context
    65K
  • Max Reasoning
    32K
  • TPMTokens Per Minute
    100K
  • RPMRequests Per Minute
    60