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Qwen-Audio-3.1-Realtime

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Overview

Qwen-Audio-3.1-Realtime is a next-generation real-time, full-duplex speech model with enhanced spoken-language reasoning, multi-turn instruction following, empathetic communication, and role-playing capabilities, balancing intelligent responses with a natural conversational rhythm. Improved noise rejection, understanding of multi-party conversations, and real-time contextual understanding allow the model to handle interruptions, wait for users to finish speaking, and take turns more naturally in real-world settings. The model supports multilingual conversations and intelligent tool use, connecting to knowledge bases and business systems while naturally integrating task results into the conversation. With stronger safeguards for refusing unsafe requests and enforcing safety boundaries, it delivers more intelligent, natural, and reliable interactions across customer service, workplace collaboration, and voice-based companionship.

Input

TextAudio

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
    $6.4Per 1M tokens
  • Input: Text
    $0.8Per 1M tokens
  • Output: Text
    $6.4Per 1M tokens
  • Output: Text&Audio (Output text is not charged)
    $24Per 1M tokens

Rate Limits & Context

  • Max Input
    245K
  • Max Output
    16K
  • Max Input (Thinking)
    245K
  • Max Output (Thinking)
    16K
  • Context
    262K
  • Max Reasoning
    2K
  • TPMTokens Per Minute
    100K
  • RPMRequests Per Minute
    60

API Reference

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import asyncio
import base64
import json
import os
import pyaudio
import websockets

API_KEY = os.environ["DASHSCOPE_API_KEY"]
URL = "wss://maas.qwencloudapi.com/api-ws/v1/realtime?model=qwen-audio-3.1-realtime-plus"

pya = pyaudio.PyAudio()
mic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)
spk = pya.open(format=pyaudio.paInt16, channels=1, rate=24000, output=True)

async def main():
    headers = {"Authorization": f"Bearer {API_KEY}"}
    async with websockets.connect(URL, additional_headers=headers) as ws:
        await ws.send(json.dumps({
            "type": "session.update",
            "session": {
                "modalities": ["text", "audio"],
                "voice": "longanqian",
                "turn_detection": {
                    "type": "server_vad",
                    "threshold": 0.5,
                    "silence_duration_ms": 500
                }
            }
        }))

        async def send_audio():
            while True:
                data = await asyncio.to_thread(mic.read, 3200, False)
                await ws.send(json.dumps({
                    "type": "input_audio_buffer.append",
                    "audio": base64.b64encode(data).decode()
                }))
                await asyncio.sleep(0.02)

        async def recv_events():
            async for msg in ws:
                event = json.loads(msg)
                t = event["type"]
                if t == "response.audio.delta":
                    audio = base64.b64decode(event["delta"])
                    await asyncio.to_thread(spk.write, audio)
                elif t == "conversation.item.input_audio_transcription.completed":
                    print(f"[You] {event['transcript']}")
                elif t == "response.audio_transcript.done":
                    print(f"[AI] {event['transcript']}")
                elif t == "error":
                    print(f"[Error] {event['error']['message']}")

        await asyncio.gather(send_audio(), recv_events())

if __name__ == "__main__":
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        mic.close()
        spk.close()
        pya.terminate()
        print("\nSession ended.")