# Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice > Qwen3-TTS 12Hz 1.7B CustomVoice and Ming-3.0-tts-0.5B score within a point of each other for text-to-speech, 58 and 57.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice - Markdown: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.md (~2,150 tokens) - Slim: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Qwen3-TTS 12Hz 1.7B CustomVoice and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk and documentation and upkeep. 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 - Qwen3-TTS 12Hz 1.7B CustomVoice: grade C, 58/100, rank #67 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-tts-1-7b-customvoice.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: - Risk, 55 against 45 - Documentation and upkeep, 44 against 36 Watch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside) ### Qwen3-TTS 12Hz 1.7B CustomVoice (C) Good for: Agents with a CUDA GPU that need local, instruction-styled speech from fixed voices in major languages Ahead on: - Capability, 71 against 66 - Reach, 60 against 55 Watch for: Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, only Qwen's package and vLLM-Omni ## Score by category | Category | Weight | Ming-3.0-tts-0.5B | Qwen3-TTS 12Hz 1.7B CustomVoice | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 66 | 71 | Qwen3-TTS 12Hz 1.7B CustomVoice +5 | | Reach | 25% (25 this run) | 55 | 60 | Qwen3-TTS 12Hz 1.7B CustomVoice +5 | | Risk | 20% (20 this run) | 55 | 45 | Ming-3.0-tts-0.5B +10 | | Agent interface | 10% (10 this run) | 55 | 55 | even | | Documentation and upkeep | 10% (10 this run) | 44 | 36 | Ming-3.0-tts-0.5B +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **57.8 · C** | **58 · C** | | ## Facts side by side | Fact | Ming-3.0-tts-0.5B | Qwen3-TTS 12Hz 1.7B CustomVoice | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) | | 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 | 2026-10-19 | 2026-01-29 | | 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) | 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. **Qwen3-TTS 12Hz 1.7B CustomVoice.** A local speech cousin with a fixed set of good voices in ten languages, emotion and pace set by a plain instruction, under plain Apache 2.0. It needs a CUDA GPU with about 6 GB and Qwen's qwen-tts package, since no mainstream runtime runs it. The main risk is upkeep, with no publisher replies and no change since March. ## 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 ` 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 ### Qwen3-TTS 12Hz 1.7B CustomVoice 1. Install with `pip install -U qwen-tts` in Python 3.12, then load `Qwen3TTSModel.from_pretrained('Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice', device_map='cuda:0', dtype=torch.bfloat16)` 2. Call `generate_custom_voice(text=..., language=..., speaker=..., instruct=...)`; it returns `(wavs, sr)`, write `wavs[0]` with soundfile 3. Speakers are Vivian, Serena, Uncle_Fu, Dylan, Eric, Ryan, Aiden, Ono_Anna and Sohee; `get_supported_speakers()` is authoritative, and each sounds best in its native language 4. Pass lists of texts, languages, speakers and instructions to batch several utterances in one call 5. For a browser UI, `qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 127.0.0.1 --port 8000`; there is no HTTP API in the package 6. FlashAttention 2 lowers memory and needs float16 or bfloat16 7. For cloning from a reference clip use Qwen3-TTS-12Hz-1.7B-Base instead ## Questions ### Which is better for AI agents, Ming-3.0-tts-0.5B or Qwen3-TTS 12Hz 1.7B CustomVoice? Qwen3-TTS 12Hz 1.7B CustomVoice and Ming-3.0-tts-0.5B score within a point of each other on agent readiness, 58 (C) and 57.8 (C). Ming-3.0-tts-0.5B leads on risk and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "ming-3-0-tts-0-5b", "b": "qwen3-tts-1-7b-customvoice"}`. From a terminal: `anchor compare ming-3-0-tts-0-5b qwen3-tts-1-7b-customvoice` - 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/qwen3-tts-1-7b-customvoice.json ## Other comparisons with Ming-3.0-tts-0.5B or Qwen3-TTS 12Hz 1.7B CustomVoice - [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-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 Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.md) - [OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.md) - [Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo](https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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