- - 🏢 Alibaba Qwen team's compact open-source text-to-speech model
- - 📏 0.6B-parameter checkpoint, balancing speed and footprint
- - ⚡ End-to-end streaming latency as low as 97ms
- - 🎯 Supports roughly 3-second voice cloning from reference audio
- - 🌐 Trained across multiple languages for multilingual synthesis
- - 🔧 Discrete multi-codebook LM with Qwen3-TTS-Tokenizer-12Hz
- - 💬 Voice design and natural-language instruction control
- - 🔒 Distributed via Hugging Face, GitHub, and ModelScope
Alibaba Group is a Chinese multinational technology company founded in 1999 and headquartered in Hangzhou, Zhejiang. Originally built around e-commerce and cloud computing, Alibaba has become one of the most prolific contributors to open-weight AI research, developing the Qwen…
Explore 51 more models by Alibaba Group →Qwen 3 TTS 0.6B is the lighter member of Alibaba's Qwen3-TTS family, open-sourced by the Qwen team at Alibaba Cloud in 2026 alongside the larger Qwen 3 TTS 1.7B. Both share a discrete multi-codebook language-model architecture that converts text into speech tokens and decodes them into waveforms, powered by the self-developed Qwen3-TTS-Tokenizer-12Hz for acoustic compression and semantic modeling. This streaming-first design helps push end-to-end synthesis latency as low as 97 milliseconds for real-time interactive use.
Compared with its 1.7B sibling, the 0.6B checkpoint trades some fidelity for a smaller footprint and faster generation, making it suitable for resource-constrained environments and low-latency synthesis. The two checkpoints share the same tokenizer and overall pipeline, with the 0.6B variant positioned as the speed-oriented option in the family.
The family supports voice cloning from a roughly 3-second reference clip, description-based voice design, and natural-language instruction control across multiple languages. Venice exposes this compact checkpoint for fast, low-latency speech synthesis, where its smaller size and streaming architecture suit conversational and assistant scenarios. Alibaba Cloud also offers Qwen-TTS speech synthesis through its Model Studio APIs, and the open-weight models and code are distributed on Hugging Face, GitHub, and ModelScope.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Qwen 3 TTS 0.6B · Organic sellers
| Seller | Health | Rep↓ | Sales | Routing | $ / 1M chars |
|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | — | 81.71 | — | #2 | $43.75 |
| Venice.ai Proxy 0x1f22…18c9 | failingupstream error · 3.5 h | 86.84 | $0.59 | #1 | $48.125 |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.