{ "data": { "a": { "slug": "ming-3-0-tts-0-5b", "name": "Ming-3.0-tts-0.5B", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://github.com/inclusionAI", "kind": "weights", "category": "text-to-speech", "summary": "Ming-3.0-tts-0.5B is inclusionAI's open-weight text-to-speech model on a Qwen2.5-0.5B backbone, with voice cloning, voice design, built-in voices and inline paralinguistic tags in seven languages, published under MIT and run with inclusionAI's Python code.", "url": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-tts-0-5b.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-tts-0.5B" }, { "registry": "github", "name": "inclusionAI/Ming-3.0-tts" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The inference code is a public GitHub repository.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Running it costs the hardware. The checkpoint is 2.96 GB in BF16, and inclusionAI says it tested inference on NVIDIA H800 80 GB and H20 96 GB with CUDA 12.4 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/inclusionAI/Ming-3.0-tts", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "voice.clone", "voice.design", "model.open-weights" ], "tags": [ "open-weights", "text-to-speech", "voice-cloning", "multilingual", "mit", "safetensors", "self-hosted", "python", "new" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 57.8, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 12, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 66, "interface": 55, "reach": 55, "risk": 55, "upkeep": 44 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "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.", "bestFor": "Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence", "strengths": [ "MIT with nothing added, no gate and no account", "2.99 GB in BF16, about 3.6 GB to run, small enough for a laptop GPU", "Eight documented tasks with fixed control values, 100+ Chinese and five English voices, and 14 inline paralinguistic tags", "Safetensors and ONNX only, with a SHA-256 for every large file", "Card and inference code changed two days ago, with a commit history that says what changed" ], "weaknesses": [ "No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)", "Loading runs the publisher's code with trust_remote_code, and the prompt is built in code rather than a template", "One task per call, and a whole waveform back with no streaming or timestamps", "No safety evaluation, training data, quantised build or security route of its own", "No hosted route for an agent without a GPU" ], "agentNotes": [ "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_normalization` unless 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 \u003cdir\u003e` 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 57.8 } ], "editorialScores": { "capability": 66, "interface": 55, "reach": 55, "risk": 55, "upkeep": 44 }, "provenanceScore": 53, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts\npip install -r requirements.txt\npython infer.py --model_path inclusionAI/Ming-3.0-tts-0.5B --task IP \\\n --text \"The garden will remain open until sunset.\" \\\n --ip US_James --use_zero_spk_emb --no_text_normalization --output output/tts.wav" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/ming-3-0-tts-0-5b" }, "weights": { "trainedBy": "inclusionAI (Ant Group)", "builtOn": "Qwen2.5-0.5B", "params": "0.5B", "goodFor": "Text to speech with voice cloning, voice design, built-in voices and inline paralinguistic tags in seven languages.", "runtimes": [], "builds": [ { "name": "Ming-3.0-tts-0.5B", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "who": "publisher", "precision": "bf16", "sizeGB": 3, "memoryGB": 8, "format": "safetensors", "note": "Plus a 28 MB ONNX speaker encoder; runs with inclusionAI's PyTorch code, none of the runtimes listed for the machine" } ], "run": "git clone https://github.com/inclusionAI/Ming-3.0-tts.git \u0026\u0026 cd Ming-3.0-tts \u0026\u0026 pip install -r requirements.txt \u0026\u0026 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", "runBuild": "Ming-3.0-tts-0.5B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ming 3.0", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ming-3.0-tts-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-tiny", "params": "7.9B total, about 1.3B active (backbone)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-tiny" }, { "name": "Ming-3.0-audio-flash", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "params": "126B total, 6.5B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash" }, { "name": "Ming-3.0-audio-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "params": "9.3B total, 2.7B active", "listing": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny" }, { "name": "Ming-3.0-asr-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny", "params": "not stated on the card (Hub metadata counts 8,865,800,992 in the files)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny" } ], "olderCount": 54 }, "alsoIn": [ "voice-cloning" ], "area": "voice", "provenance": { "legalEntity": "inclusionAI (the LICENSE's copyright holder; source headers read Copyright (c) Ant Group)", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusionAI publishes through its Hugging Face organisation (not verified by Hugging Face) and its GitHub organisation, which link to each other. Neither links a website of its own, so no domain is recorded and no security.txt could be looked for.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the Hugging Face commit history, whose messages say what changed (dialect metrics updated on 15 October, the card's inference instructions aligned with the GitHub release on 19 October). The GitHub repository's history starts with the initial release on 19 October 2026.", "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b.json" }, "answer": "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.", "b": { "slug": "qwen3-tts-1-7b-customvoice", "name": "Qwen3-TTS 12Hz 1.7B CustomVoice", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "text-to-speech", "summary": "Qwen3-TTS 12Hz 1.7B CustomVoice is Qwen's open-weight text-to-speech model with nine preset voices whose tone, emotion and pace follow a natural-language instruction, in ten languages and two Chinese dialects, with its 12 Hz speech tokenizer bundled, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-tts-1-7b-customvoice.json", "repo": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice" }, { "registry": "pypi", "name": "qwen-tts" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-21). The qwen-tts package and its source are public on PyPI and GitHub.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it costs the hardware, 4.5 GB of BF16 safetensors including the speech tokenizer, on a CUDA GPU in the card's examples. Qwen points to its DashScope (Model Studio) realtime TTS API for a hosted version, and fal and Replicate serve the CustomVoice voices per character (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://github.com/QwenLM/Qwen3-TTS", "capabilities": [ "speech.tts", "speech.voices", "speech.languages", "speech.batch", "model.open-weights" ], "tags": [ "open-weights", "model", "text-to-speech", "preset-voices", "instruction-control", "multilingual", "chinese-dialects", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-01-29", "graded": true, "anchor": { "graded": true, "score": 58, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1197, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 71, "interface": 55, "reach": 60, "risk": 45, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "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.", "bestFor": "Agents with a CUDA GPU that need local, instruction-styled speech from fixed voices in major languages", "strengths": [ "Apache 2.0 with nothing added, ungated, in Qwen's own organisation with hashed files", "4.5 GB of BF16 files with the speech tokenizer included, about 6 GB in memory", "Nine preset voices whose emotion, tone and pace follow a natural-language instruction", "Ten languages plus Beijing and Sichuan dialect voices, with batch generation in one call", "Served by fal and Replicate for agents without a GPU" ], "weaknesses": [ "Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, only Qwen's package and vLLM-Omni", "No quantised build from Qwen, and the package doesn't stream generation despite the card's latency claim", "No voice cloning in this edition; that's the Base model", "No safety results, misuse guidance or security contact", "No publisher reply to any of the 40 most recent Hugging Face discussions, and no change since March 2026" ], "agentNotes": [ "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)`", "Call `generate_custom_voice(text=..., language=..., speaker=..., instruct=...)`; it returns `(wavs, sr)`, write `wavs[0]` with soundfile", "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", "Pass lists of texts, languages, speakers and instructions to batch several utterances in one call", "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", "FlashAttention 2 lowers memory and needs float16 or bfloat16", "For cloning from a reference clip use Qwen3-TTS-12Hz-1.7B-Base instead" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58 } ], "editorialScores": { "capability": 71, "interface": 55, "reach": 60, "risk": 45, "upkeep": 36 }, "provenanceScore": 63, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install -U qwen-tts # Python 3.12, CUDA GPU; flash-attn optional\npython -c \"import torch, soundfile as sf; from qwen_tts import Qwen3TTSModel; m = Qwen3TTSModel.from_pretrained('Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice', device_map='cuda:0', dtype=torch.bfloat16); w, sr = m.generate_custom_voice(text='She said she would be here by noon.', language='English', speaker='Ryan', instruct='Very happy.'); sf.write('out.wav', w[0], sr)\"" }, "letme": { "capability": "https://letme.dev/speech.tts", "tool": "https://letme.dev/qwen3-tts-1-7b-customvoice" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-0-6b", "qwen3-asr-1-7b", "qwen3-30b-a3b-instruct-2507", "qwen3-4b-instruct-2507", "qwen3-coder-30b-a3b-instruct", "qwen3-coder-next", "qwen3-omni-30b-a3b-instruct", "qwen3-vl-2b-instruct", "qwen3-vl-30b-a3b-instruct", "qwen3-vl-32b-instruct", "qwen3-vl-4b-instruct", "qwen3-vl-8b-instruct", "qwen3-5-0-8b", "qwen3-5-122b-a10b", "qwen3-5-2b", "qwen3-5-397b-a17b", "qwen3-5-4b", "qwen3-5-9b", "qwen3-6-27b", "qwen3-6-35b-a3b", "qwen3-8-27b", "qwen3-8-flash-next" ], "weights": { "trainedBy": "Alibaba (Qwen team)", "params": "1.92B (main checkpoint) plus the 12 Hz speech tokenizer", "context": 32768, "goodFor": "Local text-to-speech with nine preset voices whose emotion and style follow an instruction, in ten languages and two Chinese dialects.", "runtimes": [], "builds": [ { "name": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "who": "publisher", "precision": "bf16", "sizeGB": 4.52, "memoryGB": 8, "format": "safetensors", "note": "Includes the 0.68 GB speech tokenizer. Runs through Qwen's qwen-tts package" } ], "run": "pip install -U qwen-tts \u0026\u0026 qwen-tts-demo Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --ip 127.0.0.1 --port 8000", "runBuild": "Qwen3-TTS-12Hz-1.7B-CustomVoice", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Alibaba (Qwen)", "family": "Qwen3", "url": "https://www.anchorterminal.com/open-weights/#qwen", "others": [ { "name": "Qwen3-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-0.6B", "params": "0.6B" }, { "name": "Qwen3-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-1.7B", "params": "1.7B" }, { "name": "Qwen3-14B", "url": "https://huggingface.co/Qwen/Qwen3-14B", "params": "14B" }, { "name": "Qwen3-235B-A22B", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B", "params": "30B-A3B" }, { "name": "Qwen3-32B", "url": "https://huggingface.co/Qwen/Qwen3-32B", "params": "32B" }, { "name": "Qwen3-4B", "url": "https://huggingface.co/Qwen/Qwen3-4B", "params": "4B" }, { "name": "Qwen3-8B", "url": "https://huggingface.co/Qwen/Qwen3-8B", "params": "8B" }, { "name": "Qwen3-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-14B-Base", "url": "https://huggingface.co/Qwen/Qwen3-14B-Base", "params": "14B" }, { "name": "Qwen3-30B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Base", "params": "30B-A3B" }, { "name": "Qwen3-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3-4B-Base", "params": "4B" }, { "name": "Qwen3-8B-Base", "url": "https://huggingface.co/Qwen/Qwen3-8B-Base", "params": "8B" }, { "name": "Qwen3-Reranker-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-0.6B", "params": "0.6B" }, { "name": "Qwen3-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-8B", "params": "8B" }, { "name": "Qwen3-Embedding-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-0-6b" }, { "name": "Qwen3-Embedding-4B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-4b" }, { "name": "Qwen3-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-embedding-8b" }, { "name": "Qwen3-Reranker-4B", "url": "https://huggingface.co/Qwen/Qwen3-Reranker-4B", "params": "4B" }, { "name": "Qwen3-235B-A22B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507", "params": "235B-A22B" }, { "name": "Qwen3-Coder-480B-A35B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct", "params": "480B-A35B" }, { "name": "Qwen3-235B-A22B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507", "params": "235B-A22B" }, { "name": "Qwen3-30B-A3B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507" }, { "name": "Qwen3-30B-A3B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507", "params": "30B-A3B" }, { "name": "Qwen3-Coder-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-30b-a3b-instruct" }, { "name": "Qwen3-4B-Instruct-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-4b-instruct-2507" }, { "name": "Qwen3-4B-Thinking-2507", "url": "https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507", "params": "4B" }, { "name": "Qwen3-Next-80B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct", "params": "80B-A3B" }, { "name": "Qwen3-Next-80B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking", "params": "80B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Captioner", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Captioner", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-Omni-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-omni-30b-a3b-instruct" }, { "name": "Qwen3-VL-235B-A22B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct", "params": "235B-A22B" }, { "name": "Qwen3-VL-235B-A22B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking", "params": "235B-A22B" }, { "name": "Qwen3Guard-Gen-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Gen-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-4B", "params": "4B" }, { "name": "Qwen3Guard-Gen-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Gen-8B", "params": "8B" }, { "name": "Qwen3Guard-Stream-0.6B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-0.6B", "params": "0.6B" }, { "name": "Qwen3Guard-Stream-4B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-4B", "params": "4B" }, { "name": "Qwen3Guard-Stream-8B", "url": "https://huggingface.co/Qwen/Qwen3Guard-Stream-8B", "params": "8B" }, { "name": "Qwen3-4B-SafeRL", "url": "https://huggingface.co/Qwen/Qwen3-4B-SafeRL", "params": "4B" }, { "name": "Qwen3-VL-30B-A3B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Instruct", "params": "30B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-30b-a3b-instruct" }, { "name": "Qwen3-VL-30B-A3B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-Thinking", "params": "30B-A3B" }, { "name": "Qwen3-VL-4B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-4b-instruct" }, { "name": "Qwen3-VL-4B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking", "params": "4B" }, { "name": "Qwen3-VL-8B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct" }, { "name": "Qwen3-VL-8B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking", "params": "8B" }, { "name": "Qwen3-VL-2B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-2b-instruct" }, { "name": "Qwen3-VL-2B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-2B-Thinking", "params": "2B" }, { "name": "Qwen3-VL-32B-Instruct", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct", "params": "32B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-32b-instruct" }, { "name": "Qwen3-VL-32B-Thinking", "url": "https://huggingface.co/Qwen/Qwen3-VL-32B-Thinking", "params": "32B" }, { "name": "Qwen3-VL-Embedding-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b" }, { "name": "Qwen3-VL-Embedding-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B", "params": "8B", "listing": "https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b" }, { "name": "Qwen3-VL-Reranker-2B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-2B", "params": "2B" }, { "name": "Qwen3-VL-Reranker-8B", "url": "https://huggingface.co/Qwen/Qwen3-VL-Reranker-8B", "params": "8B" }, { "name": "Qwen3-TTS-12Hz-0.6B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-0.6B-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice", "params": "0.6B" }, { "name": "Qwen3-TTS-12Hz-1.7B-Base", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base", "params": "1.7B" }, { "name": "Qwen3-TTS-12Hz-1.7B-VoiceDesign", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign", "params": "1.7B" }, { "name": "Qwen3-TTS-Tokenizer-12Hz", "url": "https://huggingface.co/Qwen/Qwen3-TTS-Tokenizer-12Hz", "params": "not in name" }, { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "params": "0.6B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b" }, { "name": "Qwen3-ASR-1.7B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-1.7B", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-asr-1-7b" }, { "name": "Qwen3-ForcedAligner-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B", "params": "0.6B" }, { "name": "Qwen3-Coder-Next", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next", "params": "not in name; Hub safetensors total 79.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-coder-next" }, { "name": "Qwen3-Coder-Next-Base", "url": "https://huggingface.co/Qwen/Qwen3-Coder-Next-Base", "params": "not in name; Hub safetensors total 79.7B" }, { "name": "WebWorld-14B", "url": "https://huggingface.co/Qwen/WebWorld-14B", "params": "14B" }, { "name": "WebWorld-32B", "url": "https://huggingface.co/Qwen/WebWorld-32B", "params": "32B" }, { "name": "WebWorld-8B", "url": "https://huggingface.co/Qwen/WebWorld-8B", "params": "8B" }, { "name": "Qwen3.5-397B-A17B", "url": "https://huggingface.co/Qwen/Qwen3.5-397B-A17B", "params": "397B-A17B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-397b-a17b" }, { "name": "Qwen3.5-122B-A10B", "url": "https://huggingface.co/Qwen/Qwen3.5-122B-A10B", "params": "122B-A10B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-122b-a10b" }, { "name": "Qwen3.5-27B", "url": "https://huggingface.co/Qwen/Qwen3.5-27B", "params": "27B" }, { "name": "Qwen3.5-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.5-35B-A3B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-35B-A3B-Base", "params": "35B-A3B" }, { "name": "Qwen3.5-9B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-9B-Base", "params": "9B" }, { "name": "Qwen3.5-4B", "url": "https://huggingface.co/Qwen/Qwen3.5-4B", "params": "4B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-4b" }, { "name": "Qwen3.5-4B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-4B-Base", "params": "4B" }, { "name": "Qwen3.5-9B", "url": "https://huggingface.co/Qwen/Qwen3.5-9B", "params": "9B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-9b" }, { "name": "Qwen3.5-0.8B", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B", "params": "0.8B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-0-8b" }, { "name": "Qwen3.5-0.8B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-0.8B-Base", "params": "0.8B" }, { "name": "Qwen3.5-2B", "url": "https://huggingface.co/Qwen/Qwen3.5-2B", "params": "2B", "listing": "https://www.anchorterminal.com/tools/qwen3-5-2b" }, { "name": "Qwen3.5-2B-Base", "url": "https://huggingface.co/Qwen/Qwen3.5-2B-Base", "params": "2B" }, { "name": "Qwen3.6-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B", "params": "35B-A3B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-35b-a3b" }, { "name": "Qwen3.6-27B", "url": "https://huggingface.co/Qwen/Qwen3.6-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-6-27b" }, { "name": "Qwen-AgentWorld-35B-A3B", "url": "https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B", "params": "35B-A3B" }, { "name": "Qwen3.8-27B", "url": "https://huggingface.co/Qwen/Qwen3.8-27B", "params": "27B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-27b" }, { "name": "Qwen3.8-2.4T-A95B", "url": "https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B", "params": "2.4T-A95B" }, { "name": "Qwen3.8-Flash-Next", "url": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next", "params": "125B total, 6B active, plus a 51B n-gram embedding table (publisher card); Hub safetensors total 180.0B", "listing": "https://www.anchorterminal.com/tools/qwen3-8-flash-next" }, { "name": "Qwen-Drive-1.0-4B", "url": "https://huggingface.co/Qwen/Qwen-Drive-1.0-4B", "params": "4B" } ], "olderCount": 109 }, "area": "voice", "provenance": { "legalEntity": "Alibaba Cloud (the copyright holder in the Qwen licences; RDAP names Alibaba Cloud (Singapore) Private Limited as registrant of qwen.ai, from the first batch's check)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The files sit in the Qwen organisation on Hugging Face, which github.com/QwenLM/Qwen3-TTS links to for every download.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The Hugging Face history is six commits from 21 to 29 January 2026 (upload, README edits, a tag). The GitHub repository's last commit is 17 March 2026 (a fine-tuning fix), with no release tags.", "qwen.ai/.well-known/security.txt redirects (302) rather than serving a file, and QwenLM/Qwen3-TTS has no SECURITY.md. The domain registration date is carried from the qwen3-8-27b check of 20 October 2026." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "Alibaba (Qwen team)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-19", "b": "2026-01-29", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "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.", "question": "Which is better for AI agents, Ming-3.0-tts-0.5B or Qwen3-TTS 12Hz 1.7B CustomVoice?" } ], "goodFor": [ { "aheadOn": [ "Risk, 55 against 45", "Documentation and upkeep, 44 against 36" ], "also": null, "goodFor": "Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence", "slug": "ming-3-0-tts-0-5b", "watchFor": "No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)" }, { "aheadOn": [ "Capability, 71 against 66", "Reach, 60 against 55" ], "also": null, "goodFor": "Agents with a CUDA GPU that need local, instruction-styled speech from fixed voices in major languages", "slug": "qwen3-tts-1-7b-customvoice", "watchFor": "Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, only Qwen's package and vLLM-Omni" } ], "job": { "capability": "speech.tts", "name": "Text-to-speech" }, "others": [ { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny.json", "title": "Ming-3.0-tts-0.5B vs Ming-3.0-tts-tiny", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-ming-3-0-tts-tiny" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.json", "title": "Ming-3.0-tts-0.5B vs OmniVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo.json", "title": "Ming-3.0-tts-0.5B vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-vieneu-tts-v3-turbo" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice.json", "title": "Ming-3.0-tts-tiny vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-tiny-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.json", "title": "OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice" }, { "json": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo.json", "title": "Qwen3-TTS 12Hz 1.7B CustomVoice vs VieNeu-TTS v3 Turbo", "url": "https://www.anchorterminal.com/compare/qwen3-tts-1-7b-customvoice-vs-vieneu-tts-v3-turbo" } ], "scores": [ { "by": 5, "edge": "qwen3-tts-1-7b-customvoice", "key": "capability", "ming-3-0-tts-0-5b": 66, "name": "Capability", "qwen3-tts-1-7b-customvoice": 71, "weight": 35 }, { "by": 5, "edge": "qwen3-tts-1-7b-customvoice", "key": "reach", "ming-3-0-tts-0-5b": 55, "name": "Reach", "qwen3-tts-1-7b-customvoice": 60, "weight": 25 }, { "by": 10, "edge": "ming-3-0-tts-0-5b", "key": "risk", "ming-3-0-tts-0-5b": 55, "name": "Risk", "qwen3-tts-1-7b-customvoice": 45, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "ming-3-0-tts-0-5b": 55, "name": "Agent interface", "qwen3-tts-1-7b-customvoice": 55, "weight": 10 }, { "by": 8, "edge": "ming-3-0-tts-0-5b", "key": "upkeep", "ming-3-0-tts-0-5b": 44, "name": "Documentation and upkeep", "qwen3-tts-1-7b-customvoice": 36, "weight": 10 } ], "summary": "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.", "tutorials": [], "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-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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice", "json": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.min.md" }, "markdown": "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.\n\n- 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\n- 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\n- Best text-to-speech APIs for AI agents: https://www.anchorterminal.com/best/text-to-speech/index.md\n- All 76 tts comparisons: https://www.anchorterminal.com/compare/text-to-speech/index.md\n- Best voice cloning and custom voice APIs for AI agents: https://www.anchorterminal.com/best/voice-cloning/index.md\n- All 76 cloning comparisons: https://www.anchorterminal.com/compare/voice-cloning/index.md\n\n## Which one, for what\n\n### Ming-3.0-tts-0.5B (C)\n\nGood for: Offline Chinese and English narration, cloning and voice design on a small CUDA GPU, under a plain licence\n\nAhead on:\n- Risk, 55 against 45\n- Documentation and upkeep, 44 against 36\n\nWatch for: No standard runtime runs it, only inclusionAI's GitHub scripts (mlx-audio and vLLM-Omni aside)\n\n### Qwen3-TTS 12Hz 1.7B CustomVoice (C)\n\nGood for: Agents with a CUDA GPU that need local, instruction-styled speech from fixed voices in major languages\n\nAhead on:\n- Capability, 71 against 66\n- Reach, 60 against 55\n\nWatch for: Not in any released version of Transformers, vLLM, SGLang, llama.cpp, Ollama or LM Studio, only Qwen's package and vLLM-Omni\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-tts-0.5B | Qwen3-TTS 12Hz 1.7B CustomVoice | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 66 | 71 | Qwen3-TTS 12Hz 1.7B CustomVoice +5 |\n| Reach | 25% (25 this run) | 55 | 60 | Qwen3-TTS 12Hz 1.7B CustomVoice +5 |\n| Risk | 20% (20 this run) | 55 | 45 | Ming-3.0-tts-0.5B +10 |\n| Agent interface | 10% (10 this run) | 55 | 55 | even |\n| Documentation and upkeep | 10% (10 this run) | 44 | 36 | Ming-3.0-tts-0.5B +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **57.8 · C** | **58 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-tts-0.5B | Qwen3-TTS 12Hz 1.7B CustomVoice |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | Alibaba (Qwen team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-01-29 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**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.\n\n**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.\n\n## Before you call either\n\n### Ming-3.0-tts-0.5B\n\n1. 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\n2. 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`\n3. Pass `--no_text_normalization` unless pynini 2.1.6 is installed, since text normalisation imports it\n4. Pick one task per call and pass only that task's controls, since controls from another task are rejected\n5. Run `python tools/preflight.py --model_path \u003cdir\u003e` to check the checkpoint before the first call\n6. 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\n7. On Apple silicon, mlx-audio 0.6.1 or later loads mlx-community's conversion, which isn't inclusionAI's\n8. Clone only voices you have consent for and label the audio as synthetic, as the card asks\n\n### Qwen3-TTS 12Hz 1.7B CustomVoice\n\n1. 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)`\n2. Call `generate_custom_voice(text=..., language=..., speaker=..., instruct=...)`; it returns `(wavs, sr)`, write `wavs[0]` with soundfile\n3. 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\n4. Pass lists of texts, languages, speakers and instructions to batch several utterances in one call\n5. 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\n6. FlashAttention 2 lowers memory and needs float16 or bfloat16\n7. For cloning from a reference clip use Qwen3-TTS-12Hz-1.7B-Base instead\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-tts-0.5B or Qwen3-TTS 12Hz 1.7B CustomVoice?\n\nQwen3-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.\n\n\n## For agents\n\n- 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\n- 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`\n- 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\n\n## Other comparisons with Ming-3.0-tts-0.5B or Qwen3-TTS 12Hz 1.7B CustomVoice\n\n- [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)\n- [Ming-3.0-tts-0.5B vs OmniVoice](https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-omnivoice.md)\n- [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)\n- [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)\n- [OmniVoice vs Qwen3-TTS 12Hz 1.7B CustomVoice](https://www.anchorterminal.com/compare/omnivoice-vs-qwen3-tts-1-7b-customvoice.md)\n- [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)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice", "url": "" } ], "description": "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.", "facts": [ "Ming-3.0-tts-0.5B C 57.8", "Qwen3-TTS 12Hz 1.7B CustomVoice C 58", "scores" ], "h1": "Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice.png", "path": "/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-tts-0.5B vs Qwen3-TTS 12Hz 1.7B CustomVoice (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-tts-0-5b-vs-qwen3-tts-1-7b-customvoice" }, "tokens": { "markdown": 2150, "slim": 480 }, "version": 1 }