{ "data": { "a": { "slug": "ming-3-0-audio-flash", "name": "Ming-3.0-audio-flash", "vendor": "inclusionAI (Ant Group)", "vendorUrl": "https://www.inclusion-ai.org", "kind": "weights", "category": "speech-to-text", "summary": "Ming-3.0-audio-flash is inclusionAI's open-weight audio-language model for Chinese and English. It takes audio with text instructions and returns transcripts, answers about the audio, or spoken replies, 126B parameters with 6.5B active, under MIT.", "url": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash", "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.json", "repo": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "inclusionAI/Ming-3.0-audio-flash" } ], "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). ModelScope carries the same model under inclusionAI's organisation.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT. inclusionAI sells no per-call access to these weights. Running them costs the hardware, 259.4 GB of BF16 files and eight 80 GB GPUs in inclusionAI's 128K serving profile (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-audio-flash", "capabilities": [ "speech.stt", "speech.batch", "speech.languages", "voice.speech-to-speech", "model.open-weights" ], "tags": [ "open-weights", "speech-to-text", "audio-understanding", "speech-to-speech", "voice-cloning", "moe", "mit", "safetensors", "chinese", "english", "self-hosted", "new" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 42.5, "grade": "E", "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": 24, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 65, "reach": 25, "risk": 50, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.", "bestFor": "Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model", "strengths": [ "MIT with no use policy, downloadable with no account or gate", "Transcription, audio question answering and spoken replies from one model, with 484 built-in voices and voice cloning", "Request and response format documented for its vLLM-Omni and SGLang servers, with example clients and streamed audio", "131,072-token context with no position scaling and thinking switched on per request", "Repository, examples and runtime branch updated daily up to 20 October 2026" ], "weaknesses": [ "259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB", "No named runtime runs it from released code, it needs inclusionAI's fork branches or trust_remote_code", "No hosted provider, so an agent without the hardware can't call it", "No safety results, training data, limitations or knowledge cut-off published", "No timestamps or JSON transcript output, and where reasoning text returns isn't documented" ], "agentNotes": [ "Start it with inclusionAI's recipe, `hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"`, run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash", "The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine", "Use `MING_OUTPUT_MODE=asr` with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab", "Use `MING_OUTPUT_MODE=text` for audio questions and the Talker profile with `modalities=[\"text\", \"audio\"]` for spoken replies", "Send audio as a base64 data URL in an `audio_url` item for vLLM-Omni, or as `input_audio` for SGLang", "Pick a voice with `audio.voice` and an id, or clone one with `ref_audio` and its exact `prompt_text`, never both", "Turn thinking on per request with `chat_template_kwargs` `{\"enable_thinking\": true}`, it's off by default and the reasoning comes back in think tags", "Tool calls follow the template's `\u003ctool_call\u003e` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, and no parser is named, so parse them yourself", "Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "E", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 42.5 } ], "editorialScores": { "interface": 65, "reach": 25, "risk": 50, "upkeep": 49 }, "provenanceScore": 57, "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": "# inclusionAI's recipe, from github.com/inclusionAI/Ming-3.0-audio-flash (eight 80 GB GPUs for 128K)\nhf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash\nexport MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\"\npip install -r requirements-vllm-omni.txt\nMING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"", "http": "python examples/vllm_omni_audio_chat.py examples/audio/introduce_ant_group.wav \\\n --task asr \\\n --prompt 'Please recognize the language of this speech and transcribe it, placing a `\\t` between the identified language and the transcribe.' \\\n --max-tokens 256 --no-repeat-ngram-size 10" }, "letme": { "capability": "https://letme.dev/speech.stt", "tool": "https://letme.dev/ming-3-0-audio-flash" }, "sameCompany": [ "ming-3-0-asr-tiny", "ming-3-0-audio-tiny", "ming-3-0-tts-tiny", "ling-3-0-tiny" ], "weights": { "trainedBy": "inclusionAI (Ant Group)", "params": "126B", "active": "6.5B", "context": 131072, "goodFor": "Audio-language model for Chinese and English: speech to text, questions about audio and spoken replies.", "runtimes": [], "builds": [ { "name": "Ming-3.0-audio-flash", "url": "https://huggingface.co/inclusionAI/Ming-3.0-audio-flash", "who": "publisher", "precision": "bf16", "sizeGB": 259.3, "memoryGB": 512, "format": "safetensors", "note": "Main checkpoint 256.4 GB plus the Talker and VAE 3.0 GB" } ], "run": "hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"", "runBuild": "Ming-3.0-audio-flash", "checked": "2026-10-21", "minMemoryGB": 512 }, "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-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-agents" ], "area": "voice", "provenance": { "legalEntity": "inclusionAI, Ant Group (named in the inclusion-ai.org footer; no registered company named)", "domain": "inclusion-ai.org", "domainRegistered": "2025-04-03", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/inclusionAI/Ming-3.0-audio-flash/commits/main", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "inclusion-ai.org (inclusionai.github.io redirects there) describes inclusionAI as founded by Ant Group and links its Hugging Face and GitHub organisations. Its footer reads © 2026 inclusionAI, Ant Group. RDAP gives the domain's registration as 3 April 2025.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the GitHub repository's commit history, whose messages mostly say what changed (a vLLM-Omni fix branch, voice selection, deployment profiles). The Hugging Face history mixes these with card layout edits.", "inclusion-ai.org/.well-known/security.txt answers with the site's 404 page, so there is none.", "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": 57 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-audio-flash.json" }, "answer": "Ming-3.0-audio-tiny scores 49.8 (D) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 1 of 4 scored categories.", "b": { "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" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "inclusionAI (Ant Group)", "b": "inclusionAI (Ant Group)", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-10-20", "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": "2.3/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ming-3.0-audio-tiny scores 49.8 (D) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 1 of 4 scored categories.", "question": "Which is better for AI agents, Ming-3.0-audio-flash or Ming-3.0-audio-tiny?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model", "slug": "ming-3-0-audio-flash", "watchFor": "259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB" }, { "aheadOn": [ "Reach, 45 against 25" ], "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" } ], "job": { "capability": "speech.stt", "name": "Speech-to-text" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.json", "title": "Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash" }, { "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/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash.json", "title": "Ming-3.0-asr-tiny vs Ming-3.0-audio-flash", "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash" }, { "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-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.json", "title": "Ming-3.0-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.json", "title": "Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3" }, { "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-flash-vs-qwen3-asr-1-7b.json", "title": "Ming-3.0-audio-flash vs Qwen3-ASR-1.7B", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.json", "title": "Ming-3.0-audio-flash vs Underdog Bark 3.0", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3" }, { "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602.json", "title": "Ming-3.0-audio-flash vs Voxtral Mini 4B Realtime 2602", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602" }, { "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-0-6b.json", "title": "Ming-3.0-audio-tiny vs Qwen3-ASR-0.6B", "url": "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-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" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 20, "edge": "ming-3-0-audio-tiny", "key": "reach", "ming-3-0-audio-flash": 25, "ming-3-0-audio-tiny": 45, "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "key": "risk", "ming-3-0-audio-flash": 50, "ming-3-0-audio-tiny": 50, "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "ming-3-0-audio-flash": 65, "ming-3-0-audio-tiny": 65, "name": "Agent interface", "weight": 10 }, { "by": 3, "edge": "ming-3-0-audio-flash", "key": "upkeep", "ming-3-0-audio-flash": 49, "ming-3-0-audio-tiny": 46, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Ming-3.0-audio-tiny scores 49.8 (D) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 1 of 4 scored categories. Both do speech-to-text.", "tutorials": [], "verdicts": { "ming-3-0-audio-flash": "Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "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-ming-3-0-audio-tiny.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.min.md" }, "markdown": "Ming-3.0-audio-tiny scores 49.8 (D) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 1 of 4 scored categories. Both do speech-to-text.\n\n- Ming-3.0-audio-flash: grade E, 42.5/100, rank #98 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-audio-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.json\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- 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-flash (E)\n\nGood for: Teams with a multi-GPU server who want Chinese and English transcription, audio understanding and spoken replies from one MIT model\n\nWatch for: 259.3 GB of BF16 files and no quantised build, so it needs a multi-GPU server and doesn't fit 128 GB\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- Reach, 45 against 25\n\nWatch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-flash | Ming-3.0-audio-tiny | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 45 | Ming-3.0-audio-tiny +20 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 65 | 65 | even |\n| Documentation and upkeep | 10% (10 this run) | 49 | 46 | Ming-3.0-audio-flash +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **42.5 · E** | **49.8 · D** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-flash | Ming-3.0-audio-tiny |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | inclusionAI (Ant Group) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-20 |\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 | 2.3/5 (4) | 2.5/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-audio-flash.** Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.\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## Before you call either\n\n### Ming-3.0-audio-flash\n\n1. Start it with inclusionAI's recipe, `hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash \u0026\u0026 export MODEL_PATH=\"$PWD/inclusionAI/Ming-3.0-audio-flash\" \u0026\u0026 pip install -r requirements-vllm-omni.txt \u0026\u0026 MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh \"$MODEL_PATH\"`, run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash\n2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine\n3. Use `MING_OUTPUT_MODE=asr` with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab\n4. Use `MING_OUTPUT_MODE=text` for audio questions and the Talker profile with `modalities=[\"text\", \"audio\"]` for spoken replies\n5. Send audio as a base64 data URL in an `audio_url` item for vLLM-Omni, or as `input_audio` for SGLang\n6. Pick a voice with `audio.voice` and an id, or clone one with `ref_audio` and its exact `prompt_text`, never both\n7. Turn thinking on per request with `chat_template_kwargs` `{\"enable_thinking\": true}`, it's off by default and the reasoning comes back in think tags\n8. Tool calls follow the template's `\u003ctool_call\u003e` with `\u003carg_key\u003e` and `\u003carg_value\u003e` pairs, and no parser is named, so parse them yourself\n9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code\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## Questions\n\n### Which is better for AI agents, Ming-3.0-audio-flash or Ming-3.0-audio-tiny?\n\nMing-3.0-audio-tiny scores 49.8 (D) on agent readiness against Ming-3.0-audio-flash's 42.5 (E), and leads in 1 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-flash\", \"b\": \"ming-3-0-audio-tiny\"}`. From a terminal: `anchor compare ming-3-0-audio-flash ming-3-0-audio-tiny`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-flash.json and https://www.anchorterminal.com/api/v1/tools/ming-3-0-audio-tiny.json\n\n## Other comparisons with Ming-3.0-audio-flash or Ming-3.0-audio-tiny\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-audio-flash.md)\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- [Ming-3.0-asr-tiny vs Ming-3.0-audio-flash](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-ming-3-0-audio-flash.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-audio-flash vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-5-asr-streaming-0-6b.md)\n- [Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Ming-3.0-audio-flash vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-parakeet-tdt-0-6b-v3.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-flash vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Ming-3.0-audio-flash vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-underdog-bark-3.md)\n- [Ming-3.0-audio-flash vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-voxtral-mini-4b-realtime-2602.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-0.6B](https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-qwen3-asr-0-6b.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", "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-flash vs Ming-3.0-audio-tiny", "url": "" } ], "description": "Ming-3.0-audio-tiny scores 49.8 (D) to Ming-3.0-audio-flash's 42.5 (E) for speech-to-text. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Ming-3.0-audio-flash E 42.5", "Ming-3.0-audio-tiny D 49.8", "scores" ], "h1": "Ming-3.0-audio-flash vs Ming-3.0-audio-tiny", "image": "https://www.anchorterminal.com/assets/og/compare-ming-3-0-audio-flash-vs-ming-3-0-audio-tiny.png", "path": "/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny", "published": "2026-10-01", "section": "tools", "title": "Ming-3.0-audio-flash vs Ming-3.0-audio-tiny for AI agents (2026)", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/ming-3-0-audio-flash-vs-ming-3-0-audio-tiny" }, "tokens": { "markdown": 2700, "slim": 380 }, "version": 1 }