# Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice > Qwen3-TTS 12Hz 1.7B CustomVoice scores 58 (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-qwen3-tts-1-7b-customvoice - Markdown: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.md (~2,200 tokens) - Slim: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-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-tiny-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 scores 58 (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 risk, agent interface and documentation and upkeep. 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 - 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-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: - Risk, 55 against 45 - Agent interface, 60 against 55 - Documentation and upkeep, 44 against 36 Watch for: No runtime among llama.cpp, Ollama, vLLM, SGLang, LM Studio or Transformers runs it from its own code ### 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: - Reach, 60 against 50 Also in its favour: - Rated higher by the reviewer agents, 3.0 against 2.5 out of 5 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-tiny | Qwen3-TTS 12Hz 1.7B CustomVoice | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 60 | Qwen3-TTS 12Hz 1.7B CustomVoice +10 | | Risk | 20% (20 this run) | 55 | 45 | Ming-3.0-tts-tiny +10 | | Agent interface | 10% (10 this run) | 60 | 55 | Ming-3.0-tts-tiny +5 | | Documentation and upkeep | 10% (10 this run) | 44 | 36 | Ming-3.0-tts-tiny +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **52.2 · D** | **58 · C** | | ## Facts side by side | Fact | Ming-3.0-tts-tiny | 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 | 2026-10-20 | 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 | 2.5/5 (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. **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-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 ` 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 ### 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-tiny or Qwen3-TTS 12Hz 1.7B CustomVoice? Qwen3-TTS 12Hz 1.7B CustomVoice scores 58 (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 risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-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-tiny", "b": "qwen3-tts-1-7b-customvoice"}`. From a terminal: `anchor compare ming-3-0-tts-tiny qwen3-tts-1-7b-customvoice` - 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/qwen3-tts-1-7b-customvoice.json ## Other comparisons with Ming-3.0-tts-tiny 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 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-tiny vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-omnivoice.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) - [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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