{
  "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": "Parakeet TDT 0.6B v3 scores 66.9 (B) 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 agent interface.",
    "b": {
      "slug": "parakeet-tdt-0-6b-v3",
      "name": "Parakeet TDT 0.6B v3",
      "vendor": "NVIDIA",
      "vendorUrl": "https://github.com/NVIDIA/NeMo",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Parakeet TDT 0.6B v3 is NVIDIA's open-weight speech recognition model for 25 European languages, a 0.6B-parameter FastConformer encoder with a TDT decoder that detects the language and returns punctuated text with word and segment timestamps, under CC BY 4.0.",
      "url": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3",
      "markdownUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/parakeet-tdt-0-6b-v3.json",
      "repo": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3",
      "license": "CC-BY-4.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/parakeet-tdt-0.6b-v3"
        }
      ],
      "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).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under CC BY 4.0, which asks for attribution. Running it costs the hardware; the card says at least 2 GB of RAM to load it. The files are 2.5 GB in FP32 safetensors or 0.7 GB as NVIDIA's Q8_0 GGUF (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/nvidia/parakeet-tdt-0.6b-v3",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "speech.streaming",
        "speech.languages",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "speech-to-text",
        "asr",
        "multilingual",
        "timestamps",
        "fastconformer",
        "tdt",
        "nemo",
        "cc-by-4.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-08-05",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 66.9,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 11,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 74,
          "interface": 50,
          "reach": 65,
          "risk": 60,
          "upkeep": 77
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.",
        "bestFor": "Agents that need cheap local transcription with timestamps in English and other European languages",
        "strengths": [
          "Small enough for any machine, 0.71 GB as NVIDIA's Q8_0 GGUF or 2.5 GB in FP32",
          "25 European languages detected automatically, with punctuation, capitalisation and word, segment and char timestamps",
          "CC BY 4.0, ungated, files hashed in NVIDIA's own organisation",
          "Training data listed by corpus with hours, much of it published as Granary",
          "Kept current, with Transformers support added in 2026 and NVIDIA's C++ runtime changing this month"
        ],
        "weaknesses": [
          "Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't",
          "The NeMo archive, the card's main path, loads a PyTorch pickle",
          "No language hint or hotwords, and an open report's title says it can drift into other languages without one",
          "No safety results and only general cautions",
          "NVIDIA answered 9 of the 40 latest questions, none since February 2026"
        ],
        "agentNotes": [
          "For the smallest setup, `hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models`, then `nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf` with NeMo-Speech.cpp",
          "In Python, `pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')` works on Transformers 5.10.0 or later, despite the card's note to install from source",
          "Resample to 16 kHz mono first; it takes wav or flac",
          "Ask for timestamps with `timestamps=True` in NeMo, or pass `durations=output.durations` to `processor.decode` in Transformers",
          "For audio longer than about 24 minutes, switch NeMo to local attention with `change_attention_model(\"rel_pos_local_attn\", [256, 256])`",
          "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 4,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 66.9
          }
        ],
        "editorialScores": {
          "capability": 74,
          "interface": 50,
          "reach": 65,
          "risk": 60,
          "upkeep": 77
        },
        "provenanceScore": 73,
        "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 \"transformers\u003e=5.10\" torch\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')('speech.wav'))\"\n# or with NVIDIA's C++ runtime, NeMo-Speech.cpp:\nhf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models\nnemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf",
        "http": "# after starting NeMo-Speech.cpp's server with this model (it binds 127.0.0.1:8080)\ncurl http://127.0.0.1:8080/v1/audio/transcriptions -F file=@speech.wav"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/parakeet-tdt-0-6b-v3"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "cosmos-reason2-2b",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-nano-omni-30b-a3b-reasoning",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "params": "0.6B",
        "goodFor": "Fast multilingual transcription in 25 European languages with automatic language detection, punctuation and word-level timestamps.",
        "runtimes": [
          "Transformers"
        ],
        "builds": [
          {
            "name": "parakeet-tdt-0.6b-v3 (safetensors)",
            "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 2.5,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ]
          },
          {
            "name": "parakeet-tdt-0.6b-v3.nemo",
            "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/parakeet-tdt-0.6b-v3.nemo",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 2.5,
            "memoryGB": 8,
            "format": "nemo",
            "runsOn": [
              "NeMo"
            ],
            "note": "A NeMo archive holding a PyTorch checkpoint (pickle), loaded by NeMo's ASRModel.from_pretrained"
          },
          {
            "name": "parakeet-tdt-0.6b-v3.q8_0.gguf",
            "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/blob/main/parakeet-tdt-0.6b-v3.q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 0.71,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "NeMo-Speech.cpp"
            ],
            "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp, which has no TDT decoder"
          }
        ],
        "run": "nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf",
        "runBuild": "parakeet-tdt-0.6b-v3.q8_0.gguf",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Parakeet",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "parakeet-rnnt-1.1b",
            "url": "https://huggingface.co/nvidia/parakeet-rnnt-1.1b",
            "params": "1.1B"
          },
          {
            "name": "parakeet-ctc-1.1b",
            "url": "https://huggingface.co/nvidia/parakeet-ctc-1.1b",
            "params": "1.1B"
          },
          {
            "name": "parakeet-rnnt-0.6b",
            "url": "https://huggingface.co/nvidia/parakeet-rnnt-0.6b",
            "params": "0.6B"
          },
          {
            "name": "parakeet-ctc-0.6b",
            "url": "https://huggingface.co/nvidia/parakeet-ctc-0.6b",
            "params": "0.6B"
          },
          {
            "name": "parakeet-tdt-1.1b",
            "url": "https://huggingface.co/nvidia/parakeet-tdt-1.1b",
            "params": "1.1B"
          },
          {
            "name": "parakeet-tdt_ctc-1.1b",
            "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-1.1b",
            "params": "1.1B"
          },
          {
            "name": "parakeet-tdt_ctc-0.6b-ja",
            "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja",
            "params": "0.6B"
          },
          {
            "name": "parakeet-tdt_ctc-110m",
            "url": "https://huggingface.co/nvidia/parakeet-tdt_ctc-110m",
            "params": "110M"
          },
          {
            "name": "parakeet-tdt-0.6b-v2",
            "url": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2",
            "params": "0.6B"
          },
          {
            "name": "parakeet-rnnt-110m-da-dk",
            "url": "https://huggingface.co/nvidia/parakeet-rnnt-110m-da-dk",
            "params": "110M"
          },
          {
            "name": "parakeet_realtime_eou_120m-v1",
            "url": "https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1",
            "params": "120M"
          },
          {
            "name": "multitalker-parakeet-streaming-0.6b-v1",
            "url": "https://huggingface.co/nvidia/multitalker-parakeet-streaming-0.6b-v1",
            "params": "0.6B"
          },
          {
            "name": "parakeet-ctc-0.6b-Vietnamese",
            "url": "https://huggingface.co/nvidia/parakeet-ctc-0.6b-Vietnamese",
            "params": "0.6B"
          },
          {
            "name": "parakeet-unified-en-0.6b",
            "url": "https://huggingface.co/nvidia/parakeet-unified-en-0.6b",
            "params": "0.6B"
          }
        ],
        "olderCount": 43
      },
      "area": "voice",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "https://creativecommons.org/licenses/by/4.0/legalcode.en",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The model sits in NVIDIA's Hugging Face organisation and the card's governing terms are CC BY 4.0. The domain registration date is carried from the RDAP check made for nemotron-3-nano-30b-a3b on 21 October 2026.",
          "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 28 commits from 4 August 2025 to 5 August 2026. Recent messages say what changed (Transformers integration on 16 April and 19 May 2026, an eos_token for vLLM on 19 May, Open ASR Leaderboard results on 29 June, the Q8_0 GGUF on 5 August).",
          "www.nvidia.com/.well-known/security.txt answered 403 at the 21 October 2026 check for nemotron-3-nano-30b-a3b. The card links NVIDIA's security vulnerability form, and NeMo-Speech.cpp ships a SECURITY.md pointing to NVIDIA PSIRT."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.json"
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    "facts": [
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        "b": "Open-weight model",
        "name": "Kind"
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        "a": "inclusionAI (Ant Group)",
        "b": "NVIDIA",
        "name": "Vendor"
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      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
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        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT",
        "b": "CC-BY-4.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-08-05",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
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        "a": "",
        "b": "",
        "name": "Customer content may train models"
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        "a": "",
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        "name": "Terms restrict benchmarking"
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      },
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        "a": "",
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        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "4/5 (4)",
        "name": "Agent reviews"
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    ],
    "faq": [
      {
        "answer": "Parakeet TDT 0.6B v3 scores 66.9 (B) 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 agent interface.",
        "question": "Which is better for AI agents, Ming-3.0-audio-tiny or Parakeet TDT 0.6B v3?"
      }
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        "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, 65 against 45",
          "Risk, 60 against 50",
          "Documentation and upkeep, 77 against 46"
        ],
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          "Rated higher by the reviewer agents, 4.0 against 2.5 out of 5"
        ],
        "goodFor": "Agents that need cheap local transcription with timestamps in English and other European languages",
        "slug": "parakeet-tdt-0-6b-v3",
        "watchFor": "Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't"
      }
    ],
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      "name": "Speech-to-text"
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      {
        "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-parakeet-tdt-0-6b-v3.json",
        "title": "Cohere Transcribe 03-2026 vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "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-parakeet-tdt-0-6b-v3.json",
        "title": "Ming-3.0-asr-tiny vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "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-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-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-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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3"
      },
      {
        "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/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.json",
        "title": "Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.json",
        "title": "Parakeet TDT 0.6B v3 vs Underdog Bark 3.0",
        "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "parakeet-tdt-0-6b-v3",
        "key": "reach",
        "ming-3-0-audio-tiny": 45,
        "name": "Reach",
        "parakeet-tdt-0-6b-v3": 65,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "parakeet-tdt-0-6b-v3",
        "key": "risk",
        "ming-3-0-audio-tiny": 50,
        "name": "Risk",
        "parakeet-tdt-0-6b-v3": 60,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "ming-3-0-audio-tiny",
        "key": "interface",
        "ming-3-0-audio-tiny": 65,
        "name": "Agent interface",
        "parakeet-tdt-0-6b-v3": 50,
        "weight": 10
      },
      {
        "by": 31,
        "edge": "parakeet-tdt-0-6b-v3",
        "key": "upkeep",
        "ming-3-0-audio-tiny": 46,
        "name": "Documentation and upkeep",
        "parakeet-tdt-0-6b-v3": 77,
        "weight": 10
      }
    ],
    "summary": "Parakeet TDT 0.6B v3 scores 66.9 (B) 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 agent interface. 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.",
      "parakeet-tdt-0-6b-v3": "A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint."
    }
  },
  "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-parakeet-tdt-0-6b-v3",
    "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "Parakeet TDT 0.6B v3 scores 66.9 (B) 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 agent interface. 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- Parakeet TDT 0.6B v3: grade B, 66.9/100, rank #13 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/parakeet-tdt-0-6b-v3.md · JSON https://www.anchorterminal.com/api/v1/tools/parakeet-tdt-0-6b-v3.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- Agent interface, 65 against 50\n\nWatch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches\n\n### Parakeet TDT 0.6B v3 (B)\n\nGood for: Agents that need cheap local transcription with timestamps in English and other European languages\n\nAhead on:\n- Reach, 65 against 45\n- Risk, 60 against 50\n- Documentation and upkeep, 77 against 46\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 4.0 against 2.5 out of 5\n\nWatch for: Among the six runtimes only Transformers runs it; vLLM, SGLang, llama.cpp, Ollama and LM Studio don't\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-tiny | Parakeet TDT 0.6B v3 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 45 | 65 | Parakeet TDT 0.6B v3 +20 |\n| Risk | 20% (20 this run) | 50 | 60 | Parakeet TDT 0.6B v3 +10 |\n| Agent interface | 10% (10 this run) | 65 | 50 | Ming-3.0-audio-tiny +15 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 77 | Parakeet TDT 0.6B v3 +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.8 · D** | **66.9 · B** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-tiny | Parakeet TDT 0.6B v3 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | inclusionAI (Ant Group) | NVIDIA |\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 | CC-BY-4.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-08-05 |\n| Terms last updated | 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) | 4/5 (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**Parakeet TDT 0.6B v3.** A small, fast transcription cousin for 25 European languages that detects the language and returns punctuated text with word timestamps. It fits in under 1 GB as NVIDIA's GGUF and runs in Transformers, NeMo or NVIDIA's C++ runtime, under CC BY 4.0. Few other runtimes load it, and it takes no language hint.\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### Parakeet TDT 0.6B v3\n\n1. For the smallest setup, `hf download nvidia/parakeet-tdt-0.6b-v3 parakeet-tdt-0.6b-v3.q8_0.gguf --local-dir models`, then `nemo-speech transcribe speech.wav --model models/parakeet-tdt-0.6b-v3.q8_0.gguf` with NeMo-Speech.cpp\n2. In Python, `pipeline('automatic-speech-recognition', model='nvidia/parakeet-tdt-0.6b-v3')` works on Transformers 5.10.0 or later, despite the card's note to install from source\n3. Resample to 16 kHz mono first; it takes wav or flac\n4. Ask for timestamps with `timestamps=True` in NeMo, or pass `durations=output.durations` to `processor.decode` in Transformers\n5. For audio longer than about 24 minutes, switch NeMo to local attention with `change_attention_model(\"rel_pos_local_attn\", [256, 256])`\n6. 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 Parakeet TDT 0.6B v3?\n\nParakeet TDT 0.6B v3 scores 66.9 (B) 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 agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-parakeet-tdt-0-6b-v3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-tiny\", \"b\": \"parakeet-tdt-0-6b-v3\"}`. From a terminal: `anchor compare ming-3-0-audio-tiny parakeet-tdt-0-6b-v3`\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/parakeet-tdt-0-6b-v3.json\n\n## Other comparisons with Ming-3.0-audio-tiny or Parakeet TDT 0.6B v3\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 Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-parakeet-tdt-0-6b-v3.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 Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-parakeet-tdt-0-6b-v3.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 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-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 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- [Nemotron 3.5 ASR Streaming 0.6B vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-parakeet-tdt-0-6b-v3.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-parakeet-tdt-0-6b-v3.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- [Parakeet TDT 0.6B v3 vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-asr-1-7b.md)\n- [Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Parakeet TDT 0.6B v3 vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-underdog-bark-3.md)\n- [Parakeet TDT 0.6B v3 vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/parakeet-tdt-0-6b-v3-vs-voxtral-mini-4b-realtime-2602.md)\n",
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