{ "data": { "a": { "slug": "ming-3-0-audio-tiny", "name": "Ming-3.0-audio-tiny", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "speech-to-text", "summary": "Ming-3.0-audio-tiny is inclusionAI's open-weight audio-language model for Chinese and English speech recognition, audio understanding and spoken replies, 9.3B parameters with 2.7B active, published on Hugging Face under MIT.", "url": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-audio-tiny" } ], "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). inclusionAI also publishes it on ModelScope.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT. One bundled file, the fastText language-identification model in talker/data, is under CC BY-SA 3.0. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 20.8 GB of BF16 files (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://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "speech.voices", "voice.speech-to-speech", "voice.tools", "voice.clone", "model.open-weights" ], "tags": [ "open-weights", "speech-to-text", "audio-understanding", "spoken-dialogue", "voice-cloning", "moe", "tool-calling", "chinese", "mit", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 49.8, "grade": "D", "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": 22, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 65, "reach": 45, "risk": 50, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.", "bestFor": "Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free", "strengths": [ "MIT weights in safetensors, downloadable with no account", "Speech recognition, audio question answering and spoken replies in one 9.3B model with 2.7B active, one GPU for text or ASR", "About 480 built-in voices or a cloned voice from a reference WAV, with streamed speech through vLLM-Omni", "A Jinja chat template with tools, several calls per turn and a per-request thinking switch", "131,072 positions in config.json with no scaling" ], "weaknesses": [ "No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches", "Every loading path runs repository code, and 40 compiled .pyc files ship under talker/", "BF16 only from inclusionAI, about 25 GB with headroom, and no inference provider", "No tool-call parser named, no JSON or timestamps documented, and no word on where reasoning text comes back", "No safety evaluation, agent guidance, training-data description, limitations or cut-off, and no security route of its own" ], "agentNotes": [ "Fastest start is SGLang-Omni from main, `sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0`, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions", "For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text", "Parse tool calls yourself from `\u003ctool_call\u003ename` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, since no runtime parser is named", "Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the `\u003cthink\u003e` block from the reply yourself", "In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording", "Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 49.8 } ], "editorialScores": { "interface": 65, "reach": 45, "risk": 50, "upkeep": 46 }, "provenanceScore": 31, "checked": { "categories": { "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": "# SGLang-Omni (main branch has the Ming-3.0 audio pipeline); install it per https://sgl-project.github.io/sglang-omni/\nsgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0", "http": "curl http://127.0.0.1:8000/v1/audio/transcriptions \\\n -F model=inclusionAI/Ming-3.0-audio-tiny \\\n -F file=@input.wav \\\n -F temperature=0" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/ming-3-0-audio-tiny" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-flash", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "params": "9.3B", "active": "2.7B", "context": 131072, "goodFor": "Small audio-language model for Chinese and English speech recognition, audio understanding and spoken replies.", "runtimes": [], "builds": [ { "name": "Ming-3.0-audio-tiny", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny", "who": "publisher", "precision": "bf16", "sizeGB": 20.7, "memoryGB": 32, "format": "safetensors", "note": "Main shards 17.7 GB plus the Talker and VAE 3.0 GB; runs through SGLang-Omni or inclusionAI's code" } ], "run": "sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0", "runBuild": "Ming-3.0-audio-tiny", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "inclusionAI (Ant Group)", "family": "Ming 3.0", "url": "https://www.anchorterminal.com/open-weights/#inclusionai", "others": [ { "name": "Ming-3.0-tts-0.5B", "url": "https://huggingface.co/inclusionAI/Ming-3.0-tts-0.5B", "params": "0.5B (dense)", "listing": "https://www.anchorterminal.com/tools/ming-3-0-tts-0-5b" }, { "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-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-agents" ], "area": "voice", "provenance": { "legalEntity": "", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-audio-tiny/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card names inclusionAI as the developer. inclusionAI's GitHub profile describes it as the series of open-source projects from Ant Group. No registered legal entity is named on the card, the repository or the site.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, some of whose messages say what changed (ASR prompt and generation settings on 5 September, voice selection on 19 October, SGLang-Omni serving on 20 October). The GitHub repository inclusionAI/Ming-3.0-audio-flash has a fuller history.", "www.inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. The domain was registered on 3 April 2025 (Public Interest Registry RDAP).", "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": 31 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.json" }, "answer": "Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on documentation and upkeep.", "b": { "slug": "qwen3-asr-0-6b", "name": "Qwen3-ASR-0.6B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "speech-to-text", "summary": "Qwen3-ASR-0.6B is Alibaba's compact open-weight speech recognition model for 30 languages and 22 Chinese dialects, with language identification, context hints and streaming, published on Hugging Face in BF16 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.json", "repo": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3-ASR-0.6B" }, { "registry": "huggingface", "name": "Qwen/Qwen3-ASR-0.6B-hf" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Both Qwen repositories report gated false (checked 2026-10-21). The qwen-asr package and reference code are public on GitHub.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it costs the hardware, 1.9 GB of BF16 files in the original repository and 1.6 GB in the Transformers-native -hf repository. Alibaba's Model Studio sells a hosted Qwen3-ASR-Flash, a separately named model that isn't graded here (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-ASR", "capabilities": [ "speech.stt", "speech.batch", "speech.streaming", "speech.languages", "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "speech-to-text", "asr", "multilingual", "chinese-dialects", "contextual-asr", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-07-22", "graded": true, "anchor": { "graded": true, "score": 61.2, "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": 15, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 59, "interface": 75, "reach": 75, "risk": 55, "upkeep": 33 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-21" }, "negative": 0, "verdict": "A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has no publisher replies or safety results.", "bestFor": "Agents that need fast, cheap local transcription in many languages on a small GPU, including realtime streaming", "strengths": [ "Apache 2.0 with nothing added, ungated, safetensors only in Qwen's own organisation", "1.6 to 1.9 GB of BF16 files, about 2 GB in memory, small enough for a laptop GPU", "Released vLLM, SGLang, llama.cpp and Transformers run it, and vLLM has a realtime architecture for this size", "Language identification, forced language and free-text context hints, with the output format and parser documented", "Independent Open ASR Leaderboard results for its -hf build, average WER 6.24 on the leaderboard's English short-form sets, which Capability uses (6.60 on the nine public sets alone)" ], "weaknesses": [ "No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library", "Less accurate than Qwen3-ASR-1.7B on the same leaderboard sets (6.24 against 5.4 average WER on the English short-form sets)", "Timestamps need a second model, Qwen3-ForcedAligner-0.6B, and streaming works only through vLLM without batching or timestamps", "No Qwen member has replied in its Hugging Face discussions", "No safety results, agent guidance or security contact" ], "agentNotes": [ "Start it with `vllm serve Qwen/Qwen3-ASR-0.6B` (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions", "For vLLM's realtime streaming, start it with `--hf-overrides '{\"architectures\":[\"Qwen3ASRRealtimeGeneration\"]}'`, which vLLM doesn't pick from config.json", "Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language \u003cname\u003e\u003casr_text\u003e` before the transcript", "For Transformers, load Qwen/Qwen3-ASR-0.6B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`", "Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it", "For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner=\"Qwen/Qwen3-ForcedAligner-0.6B\"` with `return_time_stamps=True`", "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions" ], "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": 61.2 } ], "editorialScores": { "capability": 59, "interface": 75, "reach": 75, "risk": 55, "upkeep": 33 }, "provenanceScore": 49, "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-asr[vllm]\"\nqwen-asr-serve Qwen/Qwen3-ASR-0.6B --host 0.0.0.0 --port 8000\n# or, with vLLM itself:\nvllm serve Qwen/Qwen3-ASR-0.6B", "http": "curl http://localhost:8000/v1/audio/transcriptions \\\n -F model=Qwen/Qwen3-ASR-0.6B \\\n -F file=@/path/to/speech.wav" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/qwen3-asr-0-6b" }, "sameCompany": [ "qwen3-embedding-0-6b", "qwen3-embedding-4b", "qwen3-embedding-8b", "qwen3-vl-embedding-2b", "qwen3-vl-embedding-8b", "qwen3-asr-1-7b", "qwen3-tts-1-7b-customvoice", "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)", "builtOn": "Qwen3-Omni (the foundation model the card names; the technical report gives Qwen3-0.6B as its language model)", "params": "0.78B", "context": 65536, "goodFor": "Fast, small multilingual and Chinese-dialect transcription with language identification, context hints and realtime streaming through vLLM.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Qwen3-ASR-0.6B", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B", "who": "publisher", "precision": "bf16", "sizeGB": 1.88, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ], "note": "Original layout, loaded by vLLM, SGLang and Qwen's qwen-asr package" }, { "name": "Qwen3-ASR-0.6B-hf", "url": "https://huggingface.co/Qwen/Qwen3-ASR-0.6B-hf", "who": "publisher", "precision": "bf16", "sizeGB": 1.58, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Transformers-native layout, published 26 June 2026, for Transformers 5.13.0 and later" }, { "name": "ggml-org Qwen3-ASR-0.6B-GGUF Q8_0", "url": "https://huggingface.co/ggml-org/Qwen3-ASR-0.6B-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 1.02, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "Model 0.80 GB plus Q8_0 audio projector 0.21 GB, from the llama.cpp project's own organisation, named in llama.cpp's multimodal guide. A BF16 pair (1.89 GB) is in the same repository" } ], "run": "vllm serve Qwen/Qwen3-ASR-0.6B", "runBuild": "Qwen3-ASR-0.6B", "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-CustomVoice", "url": "https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice", "params": "1.7B", "listing": "https://www.anchorterminal.com/tools/qwen3-tts-1-7b-customvoice" }, { "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-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 registrant of qwen.ai per RDAP, carried from the Qwen3.8-27B check)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The QwenLM/Qwen3-ASR README links the Qwen organisation's Qwen3-ASR collection and the -hf repositories on Hugging Face.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The README's News section has two dated lines, the release on 29 January 2026 and Transformers-native support on 26 June 2026.", "qwen.ai/.well-known/security.txt answered with a redirect on 21 October 2026, and no SECURITY.md is in the GitHub repository." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-asr-0-6b.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": "2026-10-20", "b": "2026-07-22", "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": "2.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on documentation and upkeep.", "question": "Which is better for AI agents, Ming-3.0-audio-tiny or Qwen3-ASR-0.6B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 46 against 33" ], "also": null, "goodFor": "Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free", "slug": "ming-3-0-audio-tiny", "watchFor": "No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches" }, { "aheadOn": [ "Reach, 75 against 45", "Risk, 55 against 50", "Agent interface, 75 against 65" ], "also": [ "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5" ], "goodFor": "Agents that need fast, cheap local transcription in many languages on a small GPU, including realtime streaming", "slug": "qwen3-asr-0-6b", "watchFor": "No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.json", "title": "Cohere Transcribe 03-2026 vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.json", "title": "Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.json", "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.json", "title": "Ming-3.0-audio-flash vs Ming-3.0-audio-tiny", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b.json", "title": "Ming-3.0-audio-flash vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b.json", "title": "Ming-3.0-audio-tiny vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-tiny vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b.json", "title": "Parakeet TDT 0.6B v3 vs Qwen3-ASR-0.6B", "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b.json", "title": "Qwen3-ASR-0.6B vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.json", "title": "Qwen3-ASR-0.6B vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.json", "title": "Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "qwen3-asr-0-6b", "key": "reach", "ming-3-0-audio-tiny": 45, "name": "Reach", "qwen3-asr-0-6b": 75, "weight": 25 }, { "by": 5, "edge": "qwen3-asr-0-6b", "key": "risk", "ming-3-0-audio-tiny": 50, "name": "Risk", "qwen3-asr-0-6b": 55, "weight": 20 }, { "by": 10, "edge": "qwen3-asr-0-6b", "key": "interface", "ming-3-0-audio-tiny": 65, "name": "Agent interface", "qwen3-asr-0-6b": 75, "weight": 10 }, { "by": 13, "edge": "ming-3-0-audio-tiny", "key": "upkeep", "ming-3-0-audio-tiny": 46, "name": "Documentation and upkeep", "qwen3-asr-0-6b": 33, "weight": 10 } ], "summary": "Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on documentation and upkeep. Both do speech-to-text.", "tutorials": [], "verdicts": { "ming-3-0-audio-tiny": "A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.", "qwen3-asr-0-6b": "A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has no publisher replies or safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b", "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.min.md" }, "markdown": "Qwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on documentation and upkeep. Both do speech-to-text.\n\n- Ming-3.0-audio-tiny: grade D, 49.8/100, rank #95 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json\n- Qwen3-ASR-0.6B: grade C, 61.2/100, rank #32 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-asr-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.json\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n- Best conversational voice-agent APIs: https://www.anchorterminal.com/best/voice-agents/index.md\n- All 388 voice agents comparisons: https://www.anchorterminal.com/compare/voice-agents/index.md\n\n## Which one, for what\n\n### Ming-3.0-audio-tiny (D)\n\nGood for: Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free\n\nAhead on:\n- Documentation and upkeep, 46 against 33\n\nWatch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches\n\n### Qwen3-ASR-0.6B (C)\n\nGood for: Agents that need fast, cheap local transcription in many languages on a small GPU, including realtime streaming\n\nAhead on:\n- Reach, 75 against 45\n- Risk, 55 against 50\n- Agent interface, 75 against 65\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: No quantised build from Alibaba, and nothing in Ollama's or LM Studio's library\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-tiny | Qwen3-ASR-0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 45 | 75 | Qwen3-ASR-0.6B +30 |\n| Risk | 20% (20 this run) | 50 | 55 | Qwen3-ASR-0.6B +5 |\n| Agent interface | 10% (10 this run) | 65 | 75 | Qwen3-ASR-0.6B +10 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 33 | Ming-3.0-audio-tiny +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.8 · D** | **61.2 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-tiny | Qwen3-ASR-0.6B |\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 | 2026-10-20 | 2026-07-22 |\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 | 2.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-audio-tiny.** A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.\n\n**Qwen3-ASR-0.6B.** A very small local transcription cousin for 30 languages and 22 Chinese dialects, built for throughput and realtime streaming through vLLM, under plain Apache 2.0. Its BF16 files need about 2 GB and run in released vLLM, SGLang, llama.cpp and Transformers. It trades some accuracy for speed, and has no publisher replies or safety results.\n\n## Before you call either\n\n### Ming-3.0-audio-tiny\n\n1. Fastest start is SGLang-Omni from main, `sgl-omni serve --model-path inclusionAI/Ming-3.0-audio-tiny --talker.gpu 0`, which exposes OpenAI-style /v1/audio/transcriptions, /v1/audio/speech and /v1/chat/completions\n2. For transcripts use greedy decoding with 10-gram blocking and the validated ASR prompt, and expect a language label, a tab, then the text\n3. Parse tool calls yourself from `\u003ctool_call\u003ename` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, since no runtime parser is named\n4. Thinking is off by default, turn it on with chat_template_kwargs enable_thinking true and strip the `\u003cthink\u003e` block from the reply yourself\n5. In vLLM-Omni pick a voice with audio.voice id from docs/voices.md, and don't combine it with a reference recording\n6. Review the repository's Python before passing trust_remote_code, and consider deleting the __pycache__ folders under talker/ so only readable source runs\n\n### Qwen3-ASR-0.6B\n\n1. Start it with `vllm serve Qwen/Qwen3-ASR-0.6B` (vLLM v0.32.0 or later), then POST the WAV to /v1/audio/transcriptions or send an audio_url part to /v1/chat/completions\n2. For vLLM's realtime streaming, start it with `--hf-overrides '{\"architectures\":[\"Qwen3ASRRealtimeGeneration\"]}'`, which vLLM doesn't pick from config.json\n3. Split chat-completions output with `parse_asr_output` from qwen-asr, since it returns `language \u003cname\u003e\u003casr_text\u003e` before the transcript\n4. For Transformers, load Qwen/Qwen3-ASR-0.6B-hf with Transformers 5.13.0 or later and use `processor.apply_transcription_request(audio, language=..., prompt=...)`\n5. Put names and domain terms in the system message or `prompt` to bias the transcript, and force the language when you know it\n6. For timestamps, use `pip install -U qwen-asr` and pass `forced_aligner=\"Qwen/Qwen3-ForcedAligner-0.6B\"` with `return_time_stamps=True`\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-audio-tiny or Qwen3-ASR-0.6B?\n\nQwen3-ASR-0.6B scores 61.2 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in 3 of 4 scored categories. Ming-3.0-audio-tiny leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-tiny\", \"b\": \"qwen3-asr-0-6b\"}`. From a terminal: `anchor compare ming-3-0-audio-tiny qwen3-asr-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json and https://www.anchorterminal.com/api/v1/tools/qwen3-asr-0-6b.json\n\n## Other comparisons with Ming-3.0-audio-tiny or Qwen3-ASR-0.6B\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-tiny.md)\n- [Cohere Transcribe 03-2026 vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-asr-tiny vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-tiny.md)\n- [Ming-3.0-asr-tiny vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-audio-flash vs Ming-3.0-audio-tiny](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.md)\n- [Ming-3.0-audio-flash vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-0-6b.md)\n- [Ming-3.0-audio-tiny vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-audio-tiny vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.md)\n- [Ming-3.0-audio-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Ming-3.0-audio-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-tiny vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-voxtral-mini-4b-realtime-2602.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-0-6b.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-ASR-0.6B](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-0-6b.md)\n- [Qwen3-ASR-0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-asr-1-7b.md)\n- [Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Qwen3-ASR-0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-underdog-bark-3.md)\n- [Qwen3-ASR-0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/qwen3-asr-0-6b-vs-voxtral-mini-4b-realtime-2602.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-audio-tiny vs Qwen3-ASR-0.6B", "url": "" } ], "description": "Qwen3-ASR-0.6B scores 61.2 (C) to Ming-3.0-audio-tiny's 49.8 (D) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-audio-tiny D 49.8", "Qwen3-ASR-0.6B C 61.2", "scores" ], "h1": "Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.png", "path": "/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b" }, "tokens": { "markdown": 2600, "slim": 480 }, "version": 1 }