Qwen3-LiveTranslate-Flash-Realtime
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Overview
The real-time version of Qwen3-LiveTranslate-Flash, which is a high-precision, highly responsive, and robust multilingual simultaneous audio and video interpretation model. Leveraging Qwen3-Omni's powerful infrastructure, massive multimodal data, cross-language and cross-modal alignment, and visual enhancement technologies, Qwen3-LiveTranslate-Flash offers both offline and real-time audio and video translation capabilities. It can understand 19 languages and speak 10 languages, including 8 Chinese dialects.
Input
ImageAudio
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 docsFunction Calling
Use function calling to connect large language models with external tools and systems.View docsCache
Context Cache stores shared prefixes for long-context requests to reduce repeated computation, improve latency, and lower cost.View docsStructured Outputs
Structured Outputs help ensure the model returns a JSON string in the expected format.View docsPricing
- Input: Audio$10Per 1M tokens
- Input: Image$1.3Per 1M tokens
- Output: Text$10Per 1M tokens
- Output: Audio$38Per 1M tokens
Rate Limits & Context
- Max Input49K
- Max Output4K
- Context53K
- TPMTokens Per Minute100K
- RPMRequests Per Minute10
API Reference
Call APICopy success!
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# pip install websocket-client
import json
import websocket
import os
API_KEY=os.getenv("DASHSCOPE_API_KEY")
API_URL = "wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime?model=qwen3-livetranslate-flash-realtime"
headers = [
"Authorization: Bearer " + API_KEY
]
def on_open(ws):
print(f"Connected to server: {API_URL}")
def on_message(ws, message):
data = json.loads(message)
print("Received event:", json.dumps(data, indent=2))
def on_error(ws, error):
print("Error:", error)
ws = websocket.WebSocketApp(
API_URL,
header=headers,
on_open=on_open,
on_message=on_message,
on_error=on_error
)
ws.run_forever()