Alibaba
Qwen Audio 3.1 TTS+开源
Alibaba’s open-weight TTS — natural prosody, zero-shot voice cloning and 30+ languages.
智商指数
89.4 #18
AAQI 89.0
输出速度
140 t/s
ITL 7.1 ms/tok
混合价格 (3:1)
$0.013 /1M
输入 $0.01/M · 输出 $0.02/M
上下文
32K
≈ 80 页 A4 · 最大输出 8,192
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
Qwen Audio 3.1 TTS+
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Best open-weight TTS — rivals commercial tiers
- Voice cloning without per-voice training
- Runs on a single 8GB GPU at Q4
- Cheap hosted API fallback via DashScope
需要注意的权衡
- Slightly robotic on long emotional passages
- Cloning quality depends on reference audio
- English/Chinese strongest — tail languages vary
杀手级用例
Self-hosted voice products
Own TTS for apps needing data control.
Voice cloning pipelines
Zero-shot cloning for personalized agents.
Multilingual IVR
Single checkpoint across 30+ locales.
开发者快速上手与 API
请先设置
DASHSCOPE_API_KEY — 获取 API 密钥curl https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "qwen-audio-tts-plus",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",
api_key=os.environ["DASHSCOPE_API_KEY"],
)
resp = client.chat.completions.create(
model="qwen-audio-tts-plus",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://dashscope-intl.aliyuncs.com/compatible-mode/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DASHSCOPE_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "qwen-audio-tts-plus",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时对决
实时规格与架构
底层解析
Qwen Audio 3.1 TTS+ — 8B open speech synthesis
- Open weights — self-host the full TTS stack
- Zero-shot voice cloning from ~10s of reference audio
- 30+ languages with code-switch support
- Prosody and style controls via natural-language prompts
发布日期
2026年10月2日
架构
Qwen Audio 3.1 TTS+ — 8B open speech synthesis
参数量
8B
开源协议
Open Weights
上下文窗口
32,000 tokens
最大输出
8,192 tokens
模态
text + audio
缓存价格
读 $0.004 · 写 $0.0125 /1M
推理
—
输入价格
$0.01/M
输出价格
$0.02/M
输出速度
140 t/s
首Token延迟
—
数据更新
2026年10月22日
硬件与本地推理
数据来源:Convly AI(CC BY 4.0) ↗VRAM(4-bit Q4)
5 GB
最低 GPU
RTX 3060 8GB / any 8GB GPU
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026qwenaudio31ttsplus,
title = {Benchmark Analysis and Intelligence Evaluation of Qwen Audio 3.1 TTS+},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/qwen-audio-3-1-tts-plus}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
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基于能力、定价和基准测试得分与 Qwen Audio 3.1 TTS+ 最接近的替代模型。