# Ming-3.0-tts-0.5B vs OmniVoice

> Ming-3.0-tts-0.5B and OmniVoice score within a point of each other for text-to-speech, 57.8 and 56.9 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-10-21

Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability. Both do text-to-speech.

- Ming-3.0-tts-0.5B: grade C, 57.8/100, rank #69 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.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-0.5B (C)

Good for: Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence

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

Also in its favour:
- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5

Watch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)

### OmniVoice (C)

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

Ahead on:
- Capability, 86 against 66

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-0.5B | OmniVoice | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 66 | 86 | OmniVoice +20 |
| Reach | 25% (25 this run) | 55 | 40 | Ming-3.0-tts-0.5B +15 |
| Risk | 20% (20 this run) | 55 | 35 | Ming-3.0-tts-0.5B +20 |
| Agent interface | 10% (10 this run) | 55 | 50 | Ming-3.0-tts-0.5B +5 |
| Documentation and upkeep | 10% (10 this run) | 44 | 48 | OmniVoice +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **57.8 · C** | **56.9 · C** | |

## Facts side by side

| Fact | Ming-3.0-tts-0.5B | 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 | 2026-10-19 | 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 | 2.8/5 (4) | 2.3/5 (4) |

## Verdicts

**Ming-3.0-tts-0.5B.** A small MIT-licensed speech cousin for offline narration, voice cloning and voice design in seven languages and two dialects. It needs about 4 GB but runs only through inclusionAI's own Python scripts on CUDA, with no streaming. The main risk is a loading path that runs the publisher's code, with no safety evaluation or security contact.

**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-0.5B

1. Clone github.com/inclusionAI/Ming-3.0-tts and `pip install -r requirements.txt` (torch 2.6.0, transformers 4.46.3), since the Hugging Face repository alone won't load
2. Start with `python infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP --text "The garden will remain open until sunset." --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav`
3. Pass `--no_text_normalization` unless pynini 2.1.6 is installed, since text normalisation imports it
4. Pick one task per call and pass only that task's controls, since controls from another task are rejected
5. Run `python tools/preflight.py --model_path <dir>` to check the checkpoint before the first call
6. inclusionAI tested only NVIDIA H800 and H20 with CUDA 12.4, and no doc covers PyTorch on ROCm, so the AMD reference machine is untested
7. On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's
8. Clone only voices you have consent for and label the audio as synthetic, 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-0.5B or OmniVoice?

Ming-3.0-tts-0.5B and OmniVoice score within a point of each other on agent readiness, 57.8 (C) and 56.9 (C). OmniVoice leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-tts-0-5b", "b": "omnivoice"}`. From a terminal: `anchor compare ming-3-0-tts-0-5b omnivoice`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json and https://www.anchorterminal.com/api/v1/tools/omnivoice.json

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