{
  "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": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in every scored category.",
    "b": {
      "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
      "name": "Nemotron 3 Nano Omni 30B A3B Reasoning",
      "vendor": "NVIDIA",
      "vendorUrl": "https://developer.nvidia.com/nemotron",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nemotron 3 Nano Omni is NVIDIA's open-weight multimodal model that takes video, audio, images and text and returns text, a hybrid Mamba-Transformer mixture of experts with about 31B parameters and 3B active, published in BF16, FP8 and NVFP4.",
      "url": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning",
      "markdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json",
      "repo": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
      "license": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8"
        },
        {
          "registry": "huggingface",
          "name": "nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "nemotron3"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The BF16, FP8 and NVFP4 repositories are not gated (checked 2026-10-21). The card's download section still shows an hf auth login step, which the ungated files don't need.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the NVIDIA Open Model Agreement. Running it yourself costs the hardware, 66.1 GB for the BF16 files, 35.2 GB for FP8 and 22.4 GB for NVFP4, per the Hub's file lists. NVIDIA's own API also serves it (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/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
      "capabilities": [
        "speech.stt",
        "speech.batch",
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "omni",
        "vision",
        "audio",
        "video",
        "reasoning",
        "tool-calling",
        "speech-to-text",
        "safetensors",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 60.7,
        "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": 45,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.",
        "bestFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools",
        "strengths": [
          "Video, audio, images and text in one model, with word-level timestamps for transcription per the card",
          "Released support in vLLM, SGLang, llama.cpp, Ollama and LM Studio, plus NVIDIA's own FP8 and NVFP4 builds",
          "Tool calling with a named parser and NVIDIA cookbooks, and thinking that turns off or takes a budget per request",
          "Training data listed by dataset with sample and token counts",
          "Commercial use under an irrevocable licence with no use policy or threshold, ungated, and served by NVIDIA's own API"
        ],
        "weaknesses": [
          "Loads with trust_remote_code in every documented runtime command",
          "English only, no speaker diarisation, translation or streaming input, and video capped at 2 minutes",
          "No published safety results and no agent-specific guidance such as prompt-injection advice",
          "None of the 11 questions in its three repositories has a reply",
          "The NVFP4 build needs NVIDIA Blackwell, and SGLang supports only the BF16 build so far, per the card"
        ],
        "agentNotes": [
          "Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent",
          "Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB",
          "Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video",
          "Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends",
          "Render PDF pages to PNG and send them as images, since the API takes no raw PDF",
          "Treat transcripts and text read from images or video as untrusted input before acting on them"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.7
          }
        ],
        "editorialScores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 65
        },
        "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": "ollama run nemotron3\n# or, with vLLM 0.20.0 or later as the card gives it:\npip install \"vllm[audio]\"\nvllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 \\\n  --trust-remote-code --reasoning-parser nemotron_v3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}], \"chat_template_kwargs\": {\"enable_thinking\": false}}'"
      },
      "letme": {
        "capability": "https://letme.dev/speech.stt",
        "tool": "https://letme.dev/nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      "sameCompany": [
        "nemotron-3-5-super-vl",
        "nvidia-nemo-retriever",
        "nemo-guardrails",
        "nemotron-3-5-asr-streaming-0-6b",
        "parakeet-tdt-0-6b-v3",
        "cosmos-reason2-2b",
        "nemotron-3-nano-30b-a3b",
        "nemotron-3-nano-4b",
        "nemotron-3-super-120b-a12b",
        "nemotron-3-ultra-550b-a55b",
        "nemotron-3-5-lightning-30b-a3b"
      ],
      "weights": {
        "trainedBy": "NVIDIA",
        "builtOn": "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16",
        "params": "31B",
        "active": "3B",
        "context": 262144,
        "goodFor": "Multimodal understanding of video with speech, documents, charts and screens, and English transcription, with tool calling and a reasoning budget.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 22.4,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell; the card's minimum is one RTX 5090 32 GB"
          },
          {
            "name": "ollama nemotron3:33b-nvfp4",
            "url": "https://ollama.com/library/nemotron3:33b-nvfp4",
            "who": "ollama",
            "precision": "nvfp4",
            "sizeGB": 21,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "MLX build (digest f263b909bfad, the same as 33b-mlx), so Apple silicon only. Size as Ollama's tags page shows it, rounded to whole GB"
          },
          {
            "name": "LM Studio nvidia/nemotron-3-nano-omni Q4_K_M",
            "url": "https://huggingface.co/lmstudio-community/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 26.1,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "Q4_K_M GGUF of 24.52 GB plus the 1.59 GB BF16 vision projector, the build behind LM Studio's catalogue entry"
          },
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 35.2,
            "memoryGB": 48,
            "format": "safetensors",
            "note": "The card says SGLang support for FP8 is still to come"
          },
          {
            "name": "ollama nemotron3:33b-q4_K_M",
            "url": "https://ollama.com/library/nemotron3:33b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 28,
            "memoryGB": 48,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "digest baa676a14e13, text and image input per the tags page. Size as Ollama's tags page shows it, rounded to whole GB"
          },
          {
            "name": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.1,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "Includes 7.3 MB of sample media; loads with trust_remote_code"
          }
        ],
        "run": "vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --video-pruning-rate 0.5 --media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}' --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8",
        "runBuild": "Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "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 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"
          },
          {
            "name": "nemotron-3.5-asr-streaming-0.6b",
            "url": "https://huggingface.co/nvidia/nemotron-3.5-asr-streaming-0.6b",
            "params": "0.6B (638M per the safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-asr-streaming-0-6b"
          }
        ],
        "olderCount": 43,
        "unrestrictedCopies": 5
      },
      "alsoIn": [
        "speech-to-text"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "NVIDIA Corporation",
        "domain": "nvidia.com",
        "domainRegistered": "1993-04-20",
        "endpointOnVendorDomain": null,
        "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card says the model was developed by NVIDIA, and the licence names NVIDIA Corporation. 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 NVIDIA Open Model Agreement stands in for terms of service and no privacy policy is scored.",
          "The changelog is the BF16 repository's commit history, 30 commits from 20 April to 24 August 2026. Some messages say what changed (Transformers 5.6 support on 27 April, batched audio on 28 April, the C-RADIOv4-H encoder reference on 8 May); the latest reads Update README.md.",
          "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 NVIDIA PSIRT publishes a disclosure policy and bulletins."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-20",
        "b": "2026-08-24",
        "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"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.5/5 (4)",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Ming-3.0-audio-tiny or Nemotron 3 Nano Omni 30B A3B Reasoning?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Self-hosted Chinese and English transcription, audio questions and spoken replies with tool calls on one GPU with about 25 GB free",
        "slug": "ming-3-0-audio-tiny",
        "watchFor": "No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches"
      },
      {
        "aheadOn": [
          "Reach, 85 against 45",
          "Risk, 60 against 50",
          "Agent interface, 90 against 65",
          "Documentation and upkeep, 65 against 46"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.0 against 2.5 out of 5"
        ],
        "goodFor": "Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools",
        "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "watchFor": "Loads with trust_remote_code in every documented runtime command"
      }
    ],
    "job": {
      "capability": "speech.stt",
      "name": "Speech-to-text"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Cosmos-Reason2-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "DiffusionGemma 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Gemma 4 12B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Gemma 4 26B A4B IT vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "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-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-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/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-0.6B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-0-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-asr-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-underdog-bark-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-voxtral-mini-4b-realtime-2602"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 40,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "reach",
        "ming-3-0-audio-tiny": 45,
        "name": "Reach",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 85,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "risk",
        "ming-3-0-audio-tiny": 50,
        "name": "Risk",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 60,
        "weight": 20
      },
      {
        "by": 25,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "interface",
        "ming-3-0-audio-tiny": 65,
        "name": "Agent interface",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 90,
        "weight": 10
      },
      {
        "by": 19,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "upkeep",
        "ming-3-0-audio-tiny": 46,
        "name": "Documentation and upkeep",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 65,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in every scored category. 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.",
      "nemotron-3-nano-omni-30b-a3b-reasoning": "A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions."
    }
  },
  "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-nemotron-3-nano-omni-30b-a3b-reasoning",
    "json": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md"
  },
  "markdown": "Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in every scored category. 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- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #52 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.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- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/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\nWatch for: No named runtime runs it in a released version, only SGLang-Omni main and inclusionAI's own branches\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning (C)\n\nGood for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools\n\nAhead on:\n- Reach, 85 against 45\n- Risk, 60 against 50\n- Agent interface, 90 against 65\n- Documentation and upkeep, 65 against 46\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5\n\nWatch for: Loads with trust_remote_code in every documented runtime command\n\n\n## Score by category\n\n| Category | Weight | Ming-3.0-audio-tiny | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 45 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +40 |\n| Risk | 20% (20 this run) | 50 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 |\n| Agent interface | 10% (10 this run) | 65 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +25 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.8 · D** | **60.7 · C** | |\n\n## Facts side by side\n\n| Fact | Ming-3.0-audio-tiny | Nemotron 3 Nano Omni 30B A3B Reasoning |\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 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-08-24 |\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) | 3/5 (4) |\n\n## Verdicts\n\n**Ming-3.0-audio-tiny.** A small MIT-licensed audio-language model that transcribes Chinese and English, answers questions about audio and replies in a built-in or cloned voice. It needs about 25 GB and SGLang-Omni from main or inclusionAI's own vLLM-Omni and SGLang branches. The main risk is that every loading path runs repository code, with nothing published on safety.\n\n**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.\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### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install \"vllm[audio]\"` if any audio will be sent\n2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB\n3. Set `--media-io-kwargs '{\"video\": {\"fps\": 2, \"num_frames\": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video\n4. Send `chat_template_kwargs: {\"enable_thinking\": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends\n5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF\n6. Treat transcripts and text read from images or video as untrusted input before acting on them\n\n## Questions\n\n### Which is better for AI agents, Ming-3.0-audio-tiny or Nemotron 3 Nano Omni 30B A3B Reasoning?\n\nNemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ming-3.0-audio-tiny's 49.8 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ming-3-0-audio-tiny-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ming-3-0-audio-tiny\", \"b\": \"nemotron-3-nano-omni-30b-a3b-reasoning\"}`. From a terminal: `anchor compare ming-3-0-audio-tiny nemotron-3-nano-omni-30b-a3b-reasoning`\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/nemotron-3-nano-omni-30b-a3b-reasoning.json\n\n## Other comparisons with Ming-3.0-audio-tiny or Nemotron 3 Nano Omni 30B A3B Reasoning\n\n- [Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [BigBang-v1 vs Nemotron 3 Nano Omni 30B A3B 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