# Ming-3.0-tts-tiny vs OmniVoice

> OmniVoice scores 56.9 (C) to Ming-3.0-tts-tiny's 52.2 (D) for text-to-speech. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface. Both do text-to-speech.

- Ming-3.0-tts-tiny: grade D, 52.2/100, rank #88 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-tts-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json
- OmniVoice: grade C, 56.9/100, rank #72 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/omnivoice.md · JSON https://www.anchorterminal.com/api/v1/tools/omnivoice.json
- Best text-to-speech APIs for AI agents: https://www.anchorterminal.com/best/text-to-speech/index.md
- All 76 tts comparisons: https://www.anchorterminal.com/compare/text-to-speech/index.md
- Best voice cloning and custom voice APIs for AI agents: https://www.anchorterminal.com/best/voice-cloning/index.md
- All 76 cloning comparisons: https://www.anchorterminal.com/compare/voice-cloning/index.md

## Which one, for what

### Ming-3.0-tts-tiny (D)

Good for: Agents on a 24 GB NVIDIA GPU that need Chinese or English narration, voice cloning or multi-speaker audio under MIT

Ahead on:
- Reach, 50 against 40
- Risk, 55 against 35
- Agent interface, 60 against 50

Watch for: No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code

### OmniVoice (C)

Good for: Research and non-commercial agents that need cloning or designed voices in low-resource languages on a small GPU

Watch for: Weights are non-commercial by the card, and the bundled tokenizer adds Boson AI's licence with a 100,000-user threshold


## Score by category

| Category | Weight | Ming-3.0-tts-tiny | OmniVoice | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 40 | Ming-3.0-tts-tiny +10 |
| Risk | 20% (20 this run) | 55 | 35 | Ming-3.0-tts-tiny +20 |
| Agent interface | 10% (10 this run) | 60 | 50 | Ming-3.0-tts-tiny +10 |
| Documentation and upkeep | 10% (10 this run) | 44 | 48 | OmniVoice +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.2 · D** | **56.9 · C** | |

## Facts side by side

| Fact | Ming-3.0-tts-tiny | OmniVoice |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | k2-fsa (Next-gen Kaldi) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | CC-BY-NC (weights, per the card); Apache-2.0 (code) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-07-03 |
| 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) | 2.3/5 (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.

**OmniVoice.** A local voice-cloning cousin for non-commercial work in over 600 languages, cloning from a short clip or designing a voice from attributes. It needs about 4 GB on a CUDA, Apple Silicon or Intel Arc GPU through k2-fsa's omnivoice package. The main risk is the licence, changed from Apache 2.0 to non-commercial in July 2026 without a note.

## Before you call either

### Ming-3.0-tts-tiny

1. 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
2. Quick start is `bash examples/one_click/run_seed.sh`, which clones from the bundled reference clip
3. To serve, run `sgl-omni serve --model-path inclusionAI/Ming-3.0-tts-tiny` from SGLang-Omni's main branch and POST to /v1/audio/speech with input, response_format and, for cloning, ref_audio plus ref_text
4. Pick one task per call (seed, multilingual, paralinguistic, podcast, basic, emotion, dialect, freeform, voice_design, ip) and pass only the inputs that task accepts
5. Expect one complete audio body, since there is no streaming, and no timestamps
6. Pass temperature yourself, since the reference generate() uses 0 and SGLang-Omni takes 1.5 from the config
7. Run `python tools/preflight_tiny.py --model_path <dir>` on the downloaded checkpoint before first use
8. Label output as synthetic speech where it could pass for a real recording, as the card asks

### OmniVoice

1. Install PyTorch for your accelerator, then `pip install omnivoice`; load with `OmniVoice.from_pretrained('k2-fsa/OmniVoice', device_map='cuda:0', dtype=torch.float16)` (or mps, or xpu)
2. Clone with `model.generate(text=..., ref_audio='ref.wav', ref_text='...')`; omitting ref_text loads a Whisper model to transcribe the clip
3. Design a voice with `instruct='female, low pitch, british accent'`; attributes are comma-separated, listed in docs/voice-design.md
4. Output is a list of numpy arrays at 24 kHz; write with `sf.write('out.wav', audio[0], 24000)`
5. Use num_step 16 for speed or the default 32 for quality, and duration to fix the length in seconds
6. From the shell, `omnivoice-infer --model k2-fsa/OmniVoice --text ... --output out.wav`; `omnivoice-infer-batch` spreads work across GPUs
7. Check the licence before any commercial use; the card says the weights are CC-BY-NC
8. Only clone a voice you have the right to use

## Questions

### Which is better for AI agents, Ming-3.0-tts-tiny or OmniVoice?

OmniVoice scores 56.9 (C) on agent readiness against Ming-3.0-tts-tiny's 52.2 (D), and leads in 1 of 4 scored categories. Ming-3.0-tts-tiny leads on reach, risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-tts-tiny", "b": "omnivoice"}`. From a terminal: `anchor compare ming-3-0-tts-tiny omnivoice`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-tiny.json and https://www.anchorterminal.com/api/v1/tools/omnivoice.json

## Other comparisons with Ming-3.0-tts-tiny or OmniVoice

- [Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.md)
- [Ming-3.0-tts-0.5B vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.md)
- [Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.md)
- [Ming-3.0-tts-tiny vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-vieneu-tts-v3-turbo.md)
- [OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.md)
- [OmniVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/omnivoice-vs-vieneu-tts-v3-turbo.md)

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