{
  "data": {
    "a": {
      "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",
      "dns": "nemotron-3-nano-omni-30b-a3b-reasoning.grade.anchorterminal.com",
      "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": "2026-11-03"
      },
      "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": 61.2,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 27,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A local multimodal model 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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61.2
          }
        ],
        "editorialScores": {
          "capability": 34,
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 70
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-03",
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "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",
        "nemotron-3-5-transcribe-30b-a4b",
        "parakeet-tdt-0-6b-v3",
        "parakeet-unified-en-0-6b",
        "nemotron-3-5-parse",
        "cosmos-reason2-2b",
        "nemotron-3-5-bolt-e4b-4gb",
        "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 3.5 Bolt E4B 4GB",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-4GB",
            "params": "8B total, 4B effective",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-bolt-e4b-4gb"
          },
          {
            "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"
          },
          {
            "name": "Nemotron 3.5 Parse",
            "url": "https://huggingface.co/nvidia/Nemotron-3.5-Parse",
            "params": "0.631B (630,800,496 trainable parameters per the card)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-parse"
          },
          {
            "name": "Nemotron 3.5 Transcribe 30B-A4B",
            "url": "https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Transcribe-30B-A4B",
            "params": "30B total, about 4.3B active (33,697,781,029 by the Hub's safetensors count)",
            "listing": "https://www.anchorterminal.com/tools/nemotron-3-5-transcribe-30b-a4b"
          }
        ],
        "olderCount": 47,
        "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": 72
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json",
      "live": {
        "slug": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "securityTxt": {
          "url": "https://nvidia.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:00.271598775Z"
        },
        "domain": {
          "domain": "nvidia.com",
          "registered": "1993-04-20",
          "source": "https://rdap.verisign.com/com/v1/domain/nvidia.com",
          "checkedAt": "2026-11-08T15:03:11.895084282Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:28.830648628Z",
            "changedAt": "2026-11-10T16:23:28.830648628Z",
            "fingerprint": "dd3f3ac7e8f2"
          },
          {
            "url": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/",
            "kind": "terms",
            "status": 304,
            "checkedAt": "2026-11-10T16:43:27.067322287Z",
            "changedAt": "2026-10-29T05:25:34.28731257Z",
            "fingerprint": "35918cb1a524"
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        "updatedAt": "2026-11-10T16:43:27.067322287Z"
      }
    },
    "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning and Ornith-1.5-122B score within a point of each other on agent readiness, 61.2 (C) and 61 (C).",
    "b": {
      "slug": "ornith-1-5-122b",
      "name": "Ornith-1.5-122B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json",
      "dns": "ornith-1-5-122b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:122b-q4_K_M"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (checked 2026-11-10).",
      "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 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "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": 1518,
        "categoryRank": 64,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
        "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published",
          "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`",
          "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends",
          "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-122b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it",
        "params": "125B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-122B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 77.7,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-122B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 75.8,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-122B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 69.7,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:122b-q4_K_M",
            "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 77,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build"
          },
          {
            "name": "Ornith-1.5-122B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 102.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 88.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 133,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 132.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 130.7,
            "memoryGB": 192,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 249.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 245.2,
            "memoryGB": 512,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:122b-q4_K_M",
        "runBuild": "ollama ornith-1.5:122b-q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. The commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json"
    },
    "edges": [
      {
        "by": 34,
        "category": "upkeep",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.",
          "b": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36."
        },
        "scores": {
          "a": 70,
          "b": 36
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.",
          "b": "Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Risk",
        "reasons": {
          "a": "File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.",
          "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact."
        },
        "scores": {
          "a": 60,
          "b": 50
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "name": "Agent interface",
        "reasons": {
          "a": "A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "NVIDIA",
        "b": "Ornith",
        "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": "NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-24",
        "b": "2026-10-24",
        "name": "Last release"
      },
      {
        "a": "no date given",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "not read yet",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nemotron 3 Nano Omni 30B A3B Reasoning and Ornith-1.5-122B score within a point of each other on agent readiness, 61.2 (C) and 61 (C).",
        "question": "Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Ornith-1.5-122B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 75",
          "Risk, 60 against 50",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 70 against 36"
        ],
        "also": null,
        "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"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-122b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "history": {
      "nemotron-3-nano-omni-30b-a3b-reasoning": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-03",
          "delta": 0.5,
          "from": 60.7,
          "fromGrade": "C",
          "to": 61.2,
          "toGrade": "C"
        }
      ],
      "ornith-1-5-122b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b.json",
        "title": "gpt-oss-20b vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Granite 4.2 3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-ornith-1-5-122b.json",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Hy3 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-122b.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Inkling-Small vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-122b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Inkling vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kimi K3.1 Pro vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kimi K3 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-122b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kolibri-1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Kronos vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b.json",
        "title": "Kronos vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Laguna S 2.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LBS-27B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.json",
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LFM3-Audio-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
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        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b.json",
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        "title": "Ling-3.0-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-122b.json",
        "title": "Ling-3.0-tiny vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Ling-3.1-flash-1019 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
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        "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "LongCat-2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiMo-V2.6-Flash-RL vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "title": "MiMo-V2.6-Flash-RL vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM-o 4.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM-V 4.7 1B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 1B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniCPM5-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-122b.json",
        "title": "MiniCPM5-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "MiniMax-M2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "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"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "reach",
        "name": "Reach",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 85,
        "ornith-1-5-122b": 75,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "risk",
        "name": "Risk",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 60,
        "ornith-1-5-122b": 50,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "interface",
        "name": "Agent interface",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 90,
        "ornith-1-5-122b": 85,
        "weight": 10
      },
      {
        "by": 34,
        "edge": "nemotron-3-nano-omni-30b-a3b-reasoning",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nemotron-3-nano-omni-30b-a3b-reasoning": 70,
        "ornith-1-5-122b": 36,
        "weight": 10
      }
    ],
    "summary": "Nemotron 3 Nano Omni 30B A3B Reasoning and Ornith-1.5-122B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "nemotron-3-nano-omni-30b-a3b-reasoning": "A local multimodal model 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.",
      "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b",
    "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.min.md"
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  "markdown": "Nemotron 3 Nano Omni 30B A3B Reasoning and Ornith-1.5-122B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 61.2/100, rank #36 of 160 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- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n- Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md\n- All 386 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md\n\n## Which one, for what\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 75\n- Risk, 60 against 50\n- Agent interface, 90 against 85\n- Documentation and upkeep, 70 against 36\n\nWatch for: Loads with trust_remote_code in every documented runtime command\n\n### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Documentation and upkeep: Nemotron 3 Nano Omni 30B A3B Reasoning +34\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (70): The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.\n- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n\n### Reach: Nemotron 3 Nano Omni 30B A3B Reasoning +10\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (85): Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n\n### Risk: Nemotron 3 Nano Omni 30B A3B Reasoning +10\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (60): File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n### Agent interface: Nemotron 3 Nano Omni 30B A3B Reasoning +5\n\n- Nemotron 3 Nano Omni 30B A3B Reasoning (90): A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).\n- Ornith-1.5-122B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n\n## Score by category\n\n| Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 75 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 |\n| Risk | 20% (20 this run) | 60 | 50 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 |\n| Agent interface | 10% (10 this run) | 90 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 36 | Nemotron 3 Nano Omni 30B A3B Reasoning +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61.2 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | NVIDIA | Ornith |\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 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-24 | 2026-10-24 |\n| Terms last updated | no date given | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Nemotron 3 Nano Omni 30B A3B Reasoning\n\n- +0.5 on 3 November 2026: correction\n\n### Ornith-1.5-122B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal model 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**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\n\n## Before you call either\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### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Ornith-1.5-122B?\n\nNemotron 3 Nano Omni 30B A3B Reasoning and Ornith-1.5-122B score within a point of each other on agent readiness, 61.2 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nemotron-3-nano-omni-30b-a3b-reasoning\", \"b\": \"ornith-1-5-122b\"}`. From a terminal: `anchor compare nemotron-3-nano-omni-30b-a3b-reasoning ornith-1-5-122b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n\n## Other comparisons with Nemotron 3 Nano Omni 30B A3B Reasoning or Ornith-1.5-122B\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- [Agents-A1-4B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.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- [Apertus v1.5 8B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.md)\n- [Apodex-1.1-mini vs Nemotron 3 Nano Omni 30B A3B 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Reasoning](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-122b.md)\n- [GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-4.5-Air vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-122b.md)\n- [GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-4.7-Flash vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.md)\n- [GLM-5.3-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-5.3-Flash vs 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