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Qwen3.5-Omni-Plus-Realtime

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Overview

Qwen 3.5-Omni is the latest generation of Qwen's multimodal large model, supporting text, image, audio, and audio-visual understanding and interaction. As a fully evolved version of Qwen3-Omni, it supports audio input in 60+ languages, voice output in 30+ languages, and controllable voice dialogue, WebSearch and complex FunctionCall invocation, and has intelligent semantic interruption interaction capabilities. It is widely used in scenarios such as text creation, voice assistants, and multimedia analysis, providing a natural and smooth multimodal interactive experience.

Input

TextImageVideoAudio

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

Pricing

  • Input: Audio
    $16.5Per 1M tokens
  • Output: Text&Audio (Output text is not charged)
    $62Per 1M tokens
  • input：Text/Image/Video
    $2.1Per 1M tokens
  • Output: Text
    $12.4Per 1M tokens

Rate Limits & Context

  • Max Input
    196K
  • Max Output
    65K
  • Context
    262K
  • TPMTokens Per Minute
    100K
  • RPMRequests Per Minute
    60

Built-in Tools

search_strategy:agentCompletions API

API Reference

Call API
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# Dependencies: dashscope >= 1.23.9, pyaudio
import os
import base64
import time

import pyaudio
from dashscope.audio.qwen_omni import MultiModality, AudioFormat, OmniRealtimeCallback, OmniRealtimeConversation
import dashscope


url = f'wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime'
# API key: if DASHSCOPE_API_KEY is not set, use: dashscope.api_key = "sk-xxx"
dashscope.api_key = os.getenv('DASHSCOPE_API_KEY')
# Voice
voice = 'Ethan'
# Model
model = 'qwen3.5-omni-plus-realtime'
# Assistant instructions
instructions = (
    "You are Xiaoyun, a personal assistant. Answer the user's questions in a humorous and witty way."
)
class SimpleCallback(OmniRealtimeCallback):
    def __init__(self, pya):
        self.pya = pya
        self.out = None
    def on_open(self):
        # Initialize audio output stream
        self.out = self.pya.open(
            format=pyaudio.paInt16,
            channels=1,
            rate=24000,
            output=True
        )
    def on_event(self, response):
        if response['type'] == 'response.audio.delta':
            # Play audio
            self.out.write(base64.b64decode(response['delta']))
        elif response['type'] == 'conversation.item.input_audio_transcription.completed':
            # Print user transcript
            print(f"[User] {response['transcript']}")
        elif response['type'] == 'response.audio_transcript.done':
            # Print assistant transcript
            print(f"[LLM] {response['transcript']}")

# 1. Initialize audio device
pya = pyaudio.PyAudio()
# 2. Create callback and conversation
callback = SimpleCallback(pya)
conv = OmniRealtimeConversation(model=model, callback=callback, url=url)
# 3. Connect and configure session
conv.connect()
conv.update_session(output_modalities=[MultiModality.AUDIO, MultiModality.TEXT], voice=voice, instructions=instructions)
# 4. Initialize microphone input stream
mic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)
# 5. Main loop: stream microphone audio
print("Conversation started. Speak into the microphone (Ctrl+C to exit)...")
try:
    while True:
        audio_data = mic.read(3200, exception_on_overflow=False)
        conv.append_audio(base64.b64encode(audio_data).decode())
        time.sleep(0.01)
except KeyboardInterrupt:
    # Clean up
    conv.close()
    mic.close()
    callback.out.close()
    pya.terminate()
    print("\nConversation ended.")