Head to head · Text-to-speech · October 2026 research run
Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny
Ming-3.0-tts-0.5B scores 57.8 (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 agent interface. 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 Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence
Ahead on
- Reach, 55 against 50
Watch for
No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)
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
- Agent interface, 60 against 55
Watch for
No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code
Score by category
| Category | Weight this run | Ming-3.0-tts-0.5B | Ming-3.0-tts-tiny | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 50 | Ming-3.0-tts-0.5B +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 55 | 60 | Ming-3.0-tts-tiny +5 |
| Documentation and upkeep | 10%10 | 44 | 44 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 57.8/100 | D 52.2/100 |
Facts side by side
| Fact | Ming-3.0-tts-0.5B | Ming-3.0-tts-tiny |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | inclusionAI (Ant Group) | inclusionAI (Ant Group) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-19 | 2026-10-20 |
| 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) | 2.5/5 (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.
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.
Before you call either
Ming-3.0-tts-0.5B
- 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 - 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 - Pass
--no_text_normalizationunless pynini 2.1.6 is installed, since text normalisation imports it - Pick one task per call and pass only that task's controls, since controls from another task are rejected
- Run
python tools/preflight.py --model_path <dir>to check the checkpoint before the first call - 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
- On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's
- Clone only voices you have consent for and label the audio as synthetic, as the card asks
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
Questions
Which is better for AI agents, Ming-3.0-tts-0.5B or Ming-3.0-tts-tiny?
Ming-3.0-tts-0.5B scores 57.8 (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 agent interface.
Other comparisons with Ming-3.0-tts-0.5B or Ming-3.0-tts-tiny
Machine-readable
- This page as Markdown
/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/ming-3-0-tts-0-5b.json·/api/v1/tools/ming-3-0-tts-tiny.json - From a terminal
anchor compare ming-3-0-tts-0-5b ming-3-0-tts-tiny(the CLI) - Over MCP
compare_tools {"a": "ming-3-0-tts-0-5b", "b": "ming-3-0-tts-tiny"}at/mcp, no key