Head to head · Text-to-speech · October 2026 research run
Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo
VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk. Both do text-to-speech.
Best text-to-speech APIs for AI agents · All 76 tts comparisons
Best voice cloning and custom voice APIs for AI agents · All 76 cloning comparisons
Which one, for what
Best for Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT
Ahead on
- Risk, 55 against 50
Watch for
No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code
Best for Agents that need Vietnamese or Vietnamese and English speech on a CPU or small GPU, streamed through an OpenAI-style endpoint
Ahead on
- Reach, 65 against 50
- Documentation and upkeep, 58 against 44
Also in its favour
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5
Watch for
Only Vietnamese and English
Score by category
| Category | Weight this run | Ming-3.0-tts-tiny | VieNeu-TTS v3 Turbo | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 65 | VieNeu-TTS v3 Turbo +15 |
| Risk | 20%20 | 55 | 50 | Ming-3.0-tts-tiny +5 |
| Agent interface | 10%10 | 60 | 60 | even |
| Documentation and upkeep | 10%10 | 44 | 58 | VieNeu-TTS v3 Turbo +14 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 52.2/100 | C 58.5/100 |
Facts side by side
| Fact | Ming-3.0-tts-tiny | VieNeu-TTS v3 Turbo |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | pnnbao-ump (VieNeu, Phạm Nguyễn Ngọc Bảo) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-09-23 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.5/5 (4) | 3/5 (4) |
Verdicts
Ming-3.0-tts-tiny
A text-to-speech cousin for voice cloning, voice design and multi-speaker podcast audio in seven languages, under MIT. It needs the publisher's own code or SGLang-Omni and about 22 GB of GPU memory, with CUDA 11.8 pinned. The main risks are that loading runs repository code, no safety work is published and it can clone a voice from a short clip.
VieNeu-TTS v3 Turbo
A tiny, fast speech cousin for Vietnamese and English, with regional preset voices, cloning from a short clip and an OpenAI-compatible streaming endpoint, under Apache 2.0. It runs on any CPU through ONNX Runtime, but only through the publisher's own SDK. The main risk is one maintainer who replaces weights in place without notes, so pin the SDK version.
Before you call either
Ming-3.0-tts-tiny
- Install from the GitHub repository,
git clone https://github.com/inclusionAI/Ming-3.0-tts.git && cd Ming-3.0-tts && pip install -r requirements-tiny.txt, in its own Python 3.10.15 environment, since the 0.5B model pins different versions - Quick start is
bash examples/one_click/run_seed.sh, which clones from the bundled reference clip - To serve, run
sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tinyfrom SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text - Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts
- Expect one complete audio body, since there is no streaming, and no timestamps
- Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config
- Run
python tools/preflight_tiny.py --model_path <dir>on the downloaded checkpoint before first use - Label output as synthetic speech where it could pass for a real recording, as the card asks
VieNeu-TTS v3 Turbo
- On CPU,
pip install vieneu, thenfrom vieneu import Vieneu; tts = Vieneu(); tts.save(tts.infer(text, voice='Mai Anh'), 'out.wav');Vieneu(precision='int8')is about twice as fast on a CPU with VNNI - On GPU, install torch 2.8 for CUDA 12.8 and transformers 4.57.6 before vieneu; it switches to PyTorch with batching and stream scheduling
- Serve with
uv run python -m apps.openai_speechfrom a clone of pnnbao97/VieNeu-TTS, then POST to http://localhost:8000/v1/audio/speech with model vieneu-v3-turbo, voice and response_format pcm or wav - Call
list_preset_voices()for the voice names of your installed version; pin the SDK version if the roster matters - Clone with
ref_audio='voice.wav', denoise=True, or enrol once withadd_voice(name, path)and reuse the name - Keep temperature near 0.8; the style argument is ignored on v3 Turbo
- Only clone a voice you have the right to use
Questions
Which is better for AI agents, Ming-3.0-tts-tiny or VieNeu-TTS v3 Turbo?
VieNeu-TTS v3 Turbo scores 58.5 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 2 of 4 scored categories. Ming-3.0-tts-tiny leads on risk.
Other comparisons with Ming-3.0-tts-tiny or VieNeu-TTS v3 Turbo
Machine-readable
- This page as Markdown
/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ming-3-0-tts-tiny.json·/api/v1/tools/vieneu-tts-v3-turbo.json - From a terminal
anchor compare ming-3-0-tts-tiny vieneu-tts-v3-turbo(the CLI) - Over MCP
compare_tools {"a": "ming-3-0-tts-tiny", "b": "vieneu-tts-v3-turbo"}at/mcp, no key