{
  "data": {
    "a": {
      "slug": "ming-3-0-asr-tiny",
      "name": "Ming-3.0-asr-tiny",
      "vendor": "inclusionAI (Ant Group)",
      "vendorUrl": "https://www.inclusion-ai.org",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Ming-3.0-asr-tiny is inclusionAI's open-weight speech recognition model for Chinese, English and Chinese dialects, an audio encoder on the Ling-tiny-v3 mixture of experts with 8.9B parameters counted from the files, that takes hotwords or dialogue context as hints, under MIT.",
      "url": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny",
      "markdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json",
      "repo": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "inclusionAI/Ming-3.0-asr-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). The cookbook and evaluation tools are in a public GitHub repository.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. inclusionAI sells no per-call access to these weights. Running them costs the hardware, about 17.7 GB of BF16 safetensors on one CUDA GPU; the publisher validated it on an NVIDIA L20Y 80 GB (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-asr",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "speech.languages",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "speech-to-text",
        "asr",
        "chinese",
        "dialects",
        "contextual-asr",
        "mit",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 52.5,
        "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": 21,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.",
        "bestFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter",
        "strengths": [
          "MIT with nothing added, ungated, files hashed in inclusionAI's own organisation",
          "Hotwords, a topic or dialogue context can be added to the prompt to bias names and terms",
          "17.7 GB of BF16 safetensors, about 21 GB in memory, which fits a 24 GB GPU",
          "Input format, output format and parser are documented, with an OpenAI-compatible transcription endpoint through SGLang-Omni",
          "Actively updated, with the last change on 20 October 2026"
        ],
        "weaknesses": [
          "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU",
          "Not in any released version of vLLM, SGLang, llama.cpp, Ollama, LM Studio or Transformers, and no quantised build from the publisher",
          "Plain text output with no word timestamps or speaker diarisation, Chinese and English only",
          "An 8,192-token context shared by audio and transcript, so long recordings must be chunked",
          "No training data or safety results, no security route of its own, and no publisher reply in its repository yet"
        ],
        "agentNotes": [
          "Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`",
          "To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field",
          "Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab",
          "Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline",
          "Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template",
          "Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens",
          "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": "low",
            "grade": "D",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 52.5
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 46
        },
        "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": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny\ncd Ming-3.0-asr-tiny\npip install -r requirements.txt   # Python 3.12, CUDA, Transformers 5.12.1, PyTorch 2.11.0\npython examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en",
        "http": "# after: sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny\ncurl -X POST http://127.0.0.1:8000/v1/audio/transcriptions \\\n  -F model=inclusionAI/Ming-3.0-asr-tiny \\\n  -F file=@/path/to/speech.wav \\\n  -F language=en \\\n  -F response_format=json"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/ming-3-0-asr-tiny"
      },
      "sameCompany": [
        "ming-3-0-audio-flash",
        "ming-3-0-audio-tiny",
        "ming-3-0-tts-tiny",
        "ling-3-0-tiny"
      ],
      "weights": {
        "trainedBy": "inclusionAI (Ant Group)",
        "builtOn": "Ling-tiny-v3",
        "params": "8.87B",
        "goodFor": "Speech recognition for Chinese, English and Chinese dialects, taking hotwords or dialogue context as hints.",
        "runtimes": [],
        "builds": [
          {
            "name": "Ming-3.0-asr-tiny",
            "url": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.7,
            "memoryGB": 24,
            "format": "safetensors",
            "note": "Needs CUDA for its KDA kernels, per the publisher"
          }
        ],
        "run": "hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny \u0026\u0026 cd Ming-3.0-asr-tiny \u0026\u0026 pip install -r requirements.txt \u0026\u0026 python examples/transcribe.py --model . --audio /path/to/speech.wav --language-hint en",
        "runBuild": "Ming-3.0-asr-tiny",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-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"
          }
        ],
        "olderCount": 54
      },
      "area": "voice",
      "provenance": {
        "legalEntity": "inclusionAI, Ant Group (named in the site footer of inclusion-ai.org; the LICENSE copyright line reads inclusionAI)",
        "domain": "inclusion-ai.org",
        "domainRegistered": "2025-04-03",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/inclusionAI/Ming-3.0-asr-tiny/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "inclusion-ai.org links to huggingface.co/inclusionAI and github.com/inclusionAI, and the GitHub organisation's profile describes inclusionAI as Ant Group's open-source projects. The footer reads \"© 2026 inclusionAI, Ant Group\". No registered company name was found.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the Hugging Face commit history, 41 commits from 14 to 20 October 2026 whose messages say what changed (pinned dependencies, a unified request format, the validated audio-only weights on 18 October, the SGLang-Omni instructions on 20 October).",
          "inclusion-ai.org/.well-known/security.txt answered 404 on 21 October 2026. The domain registration date is carried from the RDAP check made for ming-3-0-tts-tiny on 21 October 2026.",
          "Ant Group runs a company-wide security response centre, AntSRC (https://security.alipay.com/, read on 21 October 2026). Nothing on inclusionAI's card or sites links it."
        ],
        "score": 57
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.json"
    },
    "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.",
    "b": {
      "slug": "nemotron-3-5-asr-streaming-0-6b",
      "name": "Nemotron 3.5 ASR Streaming 0.6B",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "speech-to-text",
      "summary": "Nemotron 3.5 ASR Streaming 0.6B is NVIDIA's open-weight streaming speech recognition model, a 600M-parameter FastConformer with an RNN-T decoder, transcribing 32 language-locales with punctuation and an optional detected-language tag at chunk sizes from 80 ms to 1.12 s, under OpenMDW-1.1.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json",
      "repo": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
      "license": "OpenMDW-1.1",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/nemotron-3.5-asr-streaming-0.6b"
        }
      ],
      "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 OpenMDW-1.1 (the Linux Foundation's permissive model licence). NVIDIA sells no per-call access to these weights in this listing. Running them costs the hardware, about 2.6 GB of F32 safetensors or a 0.74 GB Q8_0 GGUF. fal, Together and DeepInfra serve it as inference providers on Hugging Face (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/docs/transformers/en/model_doc/nemotron3_5_asr",
      "capabilities": [
        "speech.stt",
        "speech.streaming",
        "speech.batch",
        "speech.languages",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "speech-to-text",
        "asr",
        "streaming",
        "multilingual",
        "rnnt",
        "fastconformer",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-09-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 17,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 54,
          "interface": 50,
          "reach": 70,
          "risk": 60,
          "upkeep": 64
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.",
        "bestFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop",
        "strengths": [
          "Tiny, 0.64B parameters, with a 0.74 GB Q8_0 GGUF from NVIDIA and F32 safetensors of 2.55 GB",
          "True cache-aware streaming with chunk sizes from 80 ms to 1.12 s chosen at run time",
          "One model for 32 working locales with automatic language detection and a language tag in the output",
          "Native in Transformers from v5.13.0, no remote code, and hosted by fal, Together and DeepInfra",
          "OpenMDW-1.1, permissive with no use policy, ungated, and NVIDIA answers most discussions"
        ],
        "weaknesses": [
          "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp",
          "A pickled NeMo checkpoint sits beside the safetensors and NeMo's documented path loads it",
          "Plain text out; timestamps and confidence scores are claimed in a subcard but not documented",
          "Publisher WERs of 23% to 26% across the 13 broad-coverage locales, and 8 locales need fine-tuning",
          "No safety results, and training data without a cut-off"
        ],
        "agentNotes": [
          "Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else",
          "With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`",
          "Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it",
          "Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate",
          "Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends",
          "Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle",
          "Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.8
          }
        ],
        "editorialScores": {
          "capability": 54,
          "interface": 50,
          "reach": 70,
          "risk": 60,
          "upkeep": 64
        },
        "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 \"transformers\u003e=5.13.0\"\npython -c \"from transformers import pipeline; print(pipeline('automatic-speech-recognition', model='nvidia/nemotron-3.5-asr-streaming-0.6b')('speech.wav'))\""
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/nemotron-3-5-asr-streaming-0-6b"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "parakeet-tdt-0-6b-v3",
        "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",
        "builtOn": "nvidia/nemotron-speech-streaming-en-0.6b (NVIDIA's own English model, extended to 40 locales)",
        "params": "0.64B",
        "goodFor": "Low-latency streaming transcription in 32 languages from one small model, with automatic language detection, for voice agents and many parallel streams.",
        "runtimes": [
          "Transformers"
        ],
        "builds": [
          {
            "name": "nemotron-3.5-asr-streaming-0.6b (safetensors)",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/model.safetensors",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 2.55,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "Transformers"
            ]
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b.nemo",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.nemo",
            "who": "publisher",
            "precision": "fp32",
            "sizeGB": 2.37,
            "memoryGB": 8,
            "format": "nemo",
            "note": "NeMo checkpoint, a tar archive holding a PyTorch pickle; loads with NeMo from its main branch"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/blob/main/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 0.74,
            "memoryGB": 8,
            "format": "gguf",
            "note": "For NVIDIA's NeMo-Speech.cpp, not llama.cpp (GGUF architecture asr). Re-converted with an embedded SentencePiece tokenizer on 10 September 2026"
          }
        ],
        "run": "hf download nvidia/nemotron-3.5-asr-streaming-0.6b nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --local-dir models \u0026\u0026 nemo-speech transcribe audio.wav --model models/nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto",
        "runBuild": "nemotron-3.5-asr-streaming-0.6b.q8_0.gguf",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "NVIDIA (Nemotron)",
        "family": "Nemotron 3",
        "url": "https://www.anchorterminal.com/open-weights/#nvidia",
        "others": [
          {
            "name": "Nemotron 3 Nano 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
            "params": "30B total, 3B active (31.6B per the safetensors index)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b"
          },
          {
            "name": "Nemotron 3 Nano 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-super-120b-a12b"
          },
          {
            "name": "Nemotron 3 Super 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3 Nano 4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16",
            "params": "4B",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-4b"
          },
          {
            "name": "Nemotron 3 Nano Omni 30B-A3B Reasoning",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16",
            "params": "550B total, 55B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-ultra-550b-a55b"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Ultra 550B-A55B GenRM",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16",
            "params": "30B total, 3B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-lightning-30b-a3b"
          },
          {
            "name": "Nemotron 3.5 Lightning 30B-A3B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-Base-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16",
            "params": "120B total, 12B active",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-super-vl"
          },
          {
            "name": "Nemotron 3.5 Super VL 120B-A12B Base",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16",
            "params": "120B total, 12B active"
          },
          {
            "name": "Nemotron 3.5 Bolt 74B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16",
            "params": "74B total, 9B active"
          },
          {
            "name": "Nemotron Labs 3 Elastic 30B-A3B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
            "params": "30B total, 3B active"
          },
          {
            "name": "Nemotron Labs 3 Puzzle 75B-A9B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-BF16",
            "params": "75B total, 9B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math SFT",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-SFT",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron 3 Labs Ultra Math RL",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Labs-Ultra-Math-RL",
            "params": "550B total, 55B active"
          },
          {
            "name": "Nemotron Labs 3 Competitive Coding 550B-A55B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4",
            "params": "550B total, 55B active"
          }
        ],
        "olderCount": 43
      },
      "area": "voice",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-21",
        "notes": [
          "The card says the model was developed by NVIDIA and the files sit in NVIDIA's verified nvidia organisation on Hugging Face, which developer.nvidia.com links to. The domain registration date is carried from the RDAP check made for nemotron-3-super-120b-a12b on 21 October 2026.",
          "Nothing is hosted in this listing, so the OpenMDW-1.1 licence stands in for terms of service and no privacy policy is scored. The privacy subcard links NVIDIA's privacy policy.",
          "The changelog is the repository's commit history, 36 commits from 15 May to 10 September 2026, the last re-converting the GGUF with an embedded SentencePiece tokenizer.",
          "www.nvidia.com/.well-known/security.txt was not requested again; earlier NVIDIA listings record a 403. The card links NVIDIA's vulnerability submission page."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "inclusionAI (Ant Group)",
        "b": "NVIDIA",
        "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": "OpenMDW-1.1",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-09-10",
        "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": "3/5 (4)",
        "b": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.",
        "question": "Which is better for AI agents, Ming-3.0-asr-tiny or Nemotron 3.5 ASR Streaming 0.6B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 60 against 50"
        ],
        "also": null,
        "goodFor": "Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter",
        "slug": "ming-3-0-asr-tiny",
        "watchFor": "Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU"
      },
      {
        "aheadOn": [
          "Reach, 70 against 50",
          "Risk, 60 against 55",
          "Documentation and upkeep, 64 against 46"
        ],
        "also": null,
        "goodFor": "Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop",
        "slug": "nemotron-3-5-asr-streaming-0-6b",
        "watchFor": "Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp"
      }
    ],
    "job": {
      "capability": "speech.stt",
      "name": "Speech-to-text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.json",
        "title": "Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.json",
        "title": "Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B",
        "url": "https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b"
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      {
        "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-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "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"
      },
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        "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-asr-tiny-vs-qwen3-asr-1-7b.json",
        "title": "Ming-3.0-asr-tiny vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.json",
        "title": "Ming-3.0-asr-tiny vs Underdog Bark 3.0",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602"
      },
      {
        "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-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/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "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-3-5-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-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "nemotron-3-5-asr-streaming-0-6b",
        "key": "reach",
        "ming-3-0-asr-tiny": 50,
        "name": "Reach",
        "nemotron-3-5-asr-streaming-0-6b": 70,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nemotron-3-5-asr-streaming-0-6b",
        "key": "risk",
        "ming-3-0-asr-tiny": 55,
        "name": "Risk",
        "nemotron-3-5-asr-streaming-0-6b": 60,
        "weight": 20
      },
      {
        "by": 10,
        "edge": "ming-3-0-asr-tiny",
        "key": "interface",
        "ming-3-0-asr-tiny": 60,
        "name": "Agent interface",
        "nemotron-3-5-asr-streaming-0-6b": 50,
        "weight": 10
      },
      {
        "by": 18,
        "edge": "nemotron-3-5-asr-streaming-0-6b",
        "key": "upkeep",
        "ming-3-0-asr-tiny": 46,
        "name": "Documentation and upkeep",
        "nemotron-3-5-asr-streaming-0-6b": 64,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface. Both do speech-to-text.",
    "tutorials": [],
    "verdicts": {
      "ming-3-0-asr-tiny": "A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.",
      "nemotron-3-5-asr-streaming-0-6b": "A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b",
    "json": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.min.md"
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  "markdown": "Nemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface. Both do speech-to-text.\n\n- Ming-3.0-asr-tiny: grade D, 52.5/100, rank #85 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ming-3-0-asr-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json\n- Nemotron 3.5 ASR Streaming 0.6B: grade C, 59.8/100, rank #58 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-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\n## Which one, for what\n\n### Ming-3.0-asr-tiny (D)\n\nGood for: Agents with a CUDA GPU that need local Chinese, English or dialect transcription where names and domain terms matter\n\nAhead on:\n- Agent interface, 60 against 50\n\nWatch for: Loading runs the publisher's Python with trust_remote_code, and the reference path needs a CUDA GPU\n\n### Nemotron 3.5 ASR Streaming 0.6B (C)\n\nGood for: Voice agents and call pipelines that need low-latency multilingual streaming transcription on a small GPU or a laptop\n\nAhead on:\n- Reach, 70 against 50\n- Risk, 60 against 55\n- Documentation and upkeep, 64 against 46\n\nWatch for: Not in vLLM, SGLang, llama.cpp, Ollama or LM Studio; the GGUF needs NVIDIA's NeMo-Speech.cpp\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-asr-tiny | Nemotron 3.5 ASR Streaming 0.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 70 | Nemotron 3.5 ASR Streaming 0.6B +20 |\n| Risk | 20% (20 this run) | 55 | 60 | Nemotron 3.5 ASR Streaming 0.6B +5 |\n| Agent interface | 10% (10 this run) | 60 | 50 | Ming-3.0-asr-tiny +10 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 64 | Nemotron 3.5 ASR Streaming 0.6B +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **52.5 · D** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-asr-tiny | Nemotron 3.5 ASR Streaming 0.6B |\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 | OpenMDW-1.1 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-09-10 |\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 | 3/5 (4) | 2.8/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-asr-tiny.** A local transcription cousin for Chinese, English and Chinese dialects that takes hotwords or dialogue context to get names right, under MIT. It needs one CUDA GPU with about 21 GB and runs only through the publisher's remote code or SGLang-Omni's main branch. The main risk is running the publisher's Python on load, with no safety results.\n\n**Nemotron 3.5 ASR Streaming 0.6B.** A small streaming transcription cousin for live audio in about 32 languages, with language detection and 80 ms to 1.12 s latency. It runs in under 8 GB in Transformers or as a 0.74 GB GGUF in NeMo-Speech.cpp, and several providers host it. The main gaps are no documented timestamps and weak accuracy outside the top 19 locales.\n\n## Before you call either\n\n### Ming-3.0-asr-tiny\n\n1. Start it with `hf download inclusionAI/Ming-3.0-asr-tiny --local-dir Ming-3.0-asr-tiny`, then `pip install -r requirements.txt` in Python 3.12 with CUDA (Transformers 5.12.1, fla-core 0.5.2, PyTorch 2.11.0), then `python examples/transcribe.py --model . --audio speech.wav --language-hint en`\n2. To serve it, `sgl-omni serve --model-path inclusionAI/Ming-3.0-asr-tiny` from SGLang-Omni's main branch, then POST the WAV to /v1/audio/transcriptions with response_format json and read the text field\n3. Parse raw Transformers output with decode_transcript from asr_request.py, which strips the language label before the tab\n4. Pass language zh or en when known, anything else is rejected, and append a topic or hotwords to the base instruction with no newline\n5. Load with dtype torch.bfloat16 rather than casting the model, use greedy decoding, max_new_tokens 256 and no_repeat_ngram_size 10, and don't apply the chat template\n6. Split long recordings yourself or use --audio-chunk-frames, since input and output share 8,192 tokens\n7. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions\n\n### Nemotron 3.5 ASR Streaming 0.6B\n\n1. Quickest local run, `pip install \"transformers\u003e=5.13.0\"`, then `pipeline(\"automatic-speech-recognition\", model=\"nvidia/nemotron-3.5-asr-streaming-0.6b\")`; the pipeline uses en-US, so pass language= to the processor for anything else\n2. With NeMo-Speech.cpp, `nemo-speech transcribe audio.wav --model nemotron-3.5-asr-streaming-0.6b.q8_0.gguf --language auto`\n3. Pass a locale such as de-DE or a bare code such as de, or auto; in auto mode decode with skip_special_tokens=False to keep the \u003cxx-XX\u003e tag, True to drop it\n4. Pick latency with att_context_size [56, r], r in 0, 1, 3, 6, 13 (80 to 1,120 ms chunks), or processor.set_num_lookahead_tokens in Transformers; bigger chunks are more accurate\n5. Use nvidia/nemotron-speech-streaming-en-0.6b instead for English only, as NVIDIA recommends\n6. Prefer model.safetensors or the GGUF over the .nemo file, which is a pickle\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-asr-tiny or Nemotron 3.5 ASR Streaming 0.6B?\n\nNemotron 3.5 ASR Streaming 0.6B scores 59.8 (C) on agent readiness against Ming-3.0-asr-tiny's 52.5 (D), and leads in 3 of 4 scored categories. Ming-3.0-asr-tiny leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-5-asr-streaming-0-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-asr-tiny\", \"b\": \"nemotron-3-5-asr-streaming-0-6b\"}`. From a terminal: `anchor compare ming-3-0-asr-tiny nemotron-3-5-asr-streaming-0-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ming-3-0-asr-tiny.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-5-asr-streaming-0-6b.json\n\n## Other comparisons with Ming-3.0-asr-tiny or Nemotron 3.5 ASR Streaming 0.6B\n\n- [Cohere Transcribe 03-2026 vs Ming-3.0-asr-tiny](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-ming-3-0-asr-tiny.md)\n- [Cohere Transcribe 03-2026 vs Nemotron 3.5 ASR Streaming 0.6B](https://www.anchorterminal.com/compare/cohere-transcribe-03-2026-vs-nemotron-3-5-asr-streaming-0-6b.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-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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-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-asr-tiny vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-asr-1-7b.md)\n- [Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Ming-3.0-asr-tiny vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-underdog-bark-3.md)\n- [Ming-3.0-asr-tiny vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/ming-3-0-asr-tiny-vs-voxtral-mini-4b-realtime-2602.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-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- [Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.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 3.5 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 3.5 ASR Streaming 0.6B vs Qwen3-ASR-1.7B](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-asr-1-7b.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Underdog Bark 3.0](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-underdog-bark-3.md)\n- [Nemotron 3.5 ASR Streaming 0.6B vs Voxtral Mini 4B Realtime 2602](https://www.anchorterminal.com/compare/nemotron-3-5-asr-streaming-0-6b-vs-voxtral-mini-4b-realtime-2602.md)\n",
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