Qwen-Audio-3.1
Qwen-Audio-3.1 is Alibaba's 2026 refresh of the Qwen audio family, announced from the Qwen team account on 2026-09-23. It is not one checkpoint but a five-model stack. The existing ASR, TTS and Realtime products get upgrades, and two new models join them: ASR-Next for audio understanding and TTS-Next for audio creation.
The headline for most teams is price. Alibaba announced cuts of roughly 70% on TTS, 85% on Realtime and up to 95% on ASR across the family, which matters for anyone paying per minute of speech rather than per seat.
What ships in the stack
| Model | Role | API id on Qwen Cloud |
|---|---|---|
| ASR | Multilingual and dialect transcription, with native polishing that strips fillers and repetitions | qwen-audio-3.1-asr-flash-filetrans |
| ASR-Next | Multi-speaker ASR with speaker labels, timestamps and aligned transcripts, plus emotion and sound captioning | announced, API coming soon |
| TTS | Instruction-controlled synthesis with cross-lingual voice transfer | qwen-audio-3.1-tts-flash |
| TTS-Next | Unified LM plus diffusion pass for voice, sound effects and background audio | announced, API coming soon |
| Realtime | Full-duplex speech model with interruption handling and empathetic pacing | qwen-audio-3.1-realtime-plus |
The TTS model in detail
The official technical overview describes Qwen-Audio-3.1-TTS as a production-oriented synthesis system built on a 12.5 Hz low-frame-rate speech tokenizer and a five-stage training pipeline: separate language-model and flow-matching pretraining, joint training with high-quality data annealing, language-model reinforcement learning, flow-matching robustness training, and a final reinforcement-learning pass.
Control comes from free-style natural-language instructions plus 86 inline tags for phrase and word level direction, including non-verbal events such as laughter, breathing and coughing. Coverage is 16 languages, seven of them new in this release, and 20 Chinese dialect regions. One-pass long-form synthesis reaches 3 minutes, output runs up to 48 kHz, and the model is built to keep working from noisy, reverberant or unclear reference speech without a separate denoising step. Alibaba states it ranks first on the independent Artificial Analysis Text-to-Speech leaderboard.
Pricing
Rates below are from the Qwen Cloud model pages, checked on 2026-09-23.
| Model | Input | Output | Rate limit |
|---|---|---|---|
| ASR-Flash-Filetrans | $0.15 / 1M tokens | $0.47 / 1M tokens | 600 RPM |
| TTS-Flash | $0.23 / 1M tokens | $1.87 / 1M tokens | 180 RPM |
| Realtime-Plus | published on the Qwen Cloud pricing pages | published on the Qwen Cloud pricing pages | see the model page |
Limitations
- Two of the five models are announcement-only. ASR-Next and TTS-Next had no public API id or price at check time, and the team said more API access is coming.
- No open weights in this revision. The 3.1 audio family is hosted only. Open audio weights from Alibaba stop at the earlier Qwen-Omni generation.
- Dialect support is China-centric. The 20 dialect regions are Chinese; other coverage follows the 16-language list.
- The leaderboard claim is vendor-cited. The Artificial Analysis position is reported by Alibaba, so verify it against the live board for your own language pair.
FAQ
Is Qwen-Audio-3.1 open source?
No. The 3.1 audio family runs as a hosted API on Qwen Cloud. For an open-weight TTS model in a comparable tier, look at Breeze TTS 2.
What does ASR-Next add over plain ASR?
Speaker labels, timestamps and aligned transcripts, plus emotion, ambient and machine-sound understanding for sound captioning, event localization and audio question answering.
Can I run it locally?
Not from these checkpoints. sanoTTS solves a different problem entirely: a sub-3M-parameter model that runs in a browser or on an ESP32 board.
Alternatives
- Breeze TTS 2: open-weight multilingual TTS with reference-free voice design.
- ElevenLabs Turbo v2.5: hosted TTS from a dedicated voice vendor.
- Deepgram Nova-2: hosted ASR with broad language coverage.
- Qwen3.8-Omni-Flash: the omni-modal sibling when you want audio, video and text in one model instead of a pipeline of specialists.
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