{
  "data": {
    "a": {
      "slug": "nanbeige-4-2-3b",
      "name": "Nanbeige4.2-3B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.2-3B is a small agent model from BOSS Zhipin's Nanbeige lab, 4.2B parameters with layers reused in a loop, with tool calling, a thinking switch and a 256K context. Apache 2.0. Released SGLang and llama.cpp run it.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json",
      "dns": "nanbeige-4-2-3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.2-3B-GPTQ-Int8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0, per the card's metadata. Running it yourself costs little: 5.2 GB in FP8 or 8-bit from Nanbeige, 8.3 GB in BF16, and 2.7 GB for bartowski's Q4_K_M conversion (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/Nanbeige/Nanbeige4.2-3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "small",
        "looped-transformer",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-09-11",
      "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": 62,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 55,
          "upkeep": 72
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
        "bestFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "strengths": [
          "Trained for multi-step tool use, with parsers in released SGLang",
          "A 256K context in a model that fits 8 GB",
          "Apache 2.0, ungated",
          "FP8 and 8-bit builds from Nanbeige",
          "Nanbeige replied in most of the recent discussions"
        ],
        "weaknesses": [
          "Its own architecture: Transformers loads it only with the repository's code",
          "No GGUF from Nanbeige, so llama.cpp users rely on a third party's conversion",
          "The card's serve instructions still point at Nanbeige's forks",
          "No safety results on the card",
          "Training data described only in general terms",
          "Not changed since September"
        ],
        "agentNotes": [
          "Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`",
          "Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`",
          "Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat",
          "Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card",
          "For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's",
          "If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it"
        ],
        "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": 85,
          "risk": 55,
          "upkeep": 72
        },
        "provenanceScore": 58,
        "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": "pip install \"sglang\u003e=0.5.23\"\npython -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "http": "# SGLang serves an OpenAI-compatible API at http://localhost:30000/v1. Pass tools in the request, and\n# chat_template_kwargs {\"enable_thinking\": false} for a direct answer"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-2-3b"
      },
      "sameCompany": [
        "nanbeige-4-3-33b"
      ],
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige4.2-3B-Base, per the card and the metadata",
        "params": "4.2B",
        "context": 262144,
        "goodFor": "A small local agent for tool use, coding and office tasks, with thinking you can turn off and a long context.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang"
        ],
        "builds": [
          {
            "name": "Nanbeige4.2-3B-FP8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's FP8 build"
          },
          {
            "name": "Nanbeige4.2-3B-GPTQ-Int8",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-GPTQ-Int8",
            "who": "publisher",
            "precision": "int8",
            "sizeGB": 5.19,
            "memoryGB": 8,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Nanbeige's 8-bit GPTQ build"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 2.68,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. Its base_model tag names Nanbeige/Nanbeige4.2-3B, from the Hub's model information on 10 November 2026"
          },
          {
            "name": "bartowski Nanbeige_Nanbeige4.2-3B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Nanbeige_Nanbeige4.2-3B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 4.43,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nanbeige's. From the Hub's model information on 10 November 2026"
          },
          {
            "name": "Nanbeige4.2-3B (BF16 safetensors)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 8.34,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Two shards"
          }
        ],
        "run": "python -m sglang.launch_server --model-path Nanbeige/Nanbeige4.2-3B --reasoning-parser nanbeige --tool-call-parser nanbeige",
        "runBuild": "Nanbeige4.2-3B (BF16 safetensors)",
        "checked": "2026-11-10",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.2",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.2-3B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed",
        "domain": "zhipin.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.2-3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The publisher's identity, domain and security facts are carried from our Nanbeige4.3-33B listing: the lab's report for this model names Nanbeige LLM Lab, BOSS Zhipin, and the card's contact address is on kanzhun.com.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 29 commits from 21 July to 11 September 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-2-3b.json"
    },
    "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "ornith-1-5-397b",
      "name": "Ornith-1.5-397B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json",
      "dns": "ornith-1-5-397b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-397B-GGUF"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:397b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are not gated (checked 2026-10-21).",
      "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 807 GB for the BF16 files, 418 GB for FP8 or 244 GB for Ornith's Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-04"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59,
        "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": 77,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.",
        "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions",
          "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated",
          "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 code in the repository"
        ],
        "weaknesses": [
          "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine",
          "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",
          "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference",
          "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters"
        ],
        "agentNotes": [
          "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory",
          "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",
          "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards",
          "Pin a revision when you download, since Ornith has replaced files in place",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --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-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-397b"
      },
      "sameCompany": [
        "ornith-1-5-122b",
        "ornith-1-5-35b-a3b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)",
        "params": "397B",
        "context": 262144,
        "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-397B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 418.4,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 319.5,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB"
          },
          {
            "name": "Ornith-1.5-397B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 331,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 286.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 244.3,
            "memoryGB": 512,
            "format": "gguf",
            "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine"
          },
          {
            "name": "ollama ornith-1.5:397b",
            "url": "https://ollama.com/library/ornith-1.5:397b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 242,
            "memoryGB": 512,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 806.8,
            "memoryGB": 1024,
            "format": "safetensors",
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-397B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 428.5,
            "memoryGB": 1024,
            "format": "gguf",
            "note": "Too big for the 128 GB reference machine"
          }
        ],
        "run": "ollama run ornith-1.5:397b",
        "runBuild": "ollama ornith-1.5:397b",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "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-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-122b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ],
        "unrestrictedCopies": 4
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.",
          "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. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and 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-1-5-397b.json",
      "live": {
        "slug": "ornith-1-5-397b",
        "securityTxt": {
          "url": "https://ornith.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:27.802812649Z"
        },
        "domain": {
          "domain": "ornith.ai",
          "registered": "2026-06-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/ornith.ai",
          "checkedAt": "2026-11-08T14:47:46.372430234Z"
        },
        "updatedAt": "2026-11-10T16:07:27.802812649Z"
      }
    },
    "edges": [
      {
        "by": 36,
        "category": "upkeep",
        "leads": "nanbeige-4-2-3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.",
          "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, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use."
        },
        "scores": {
          "a": 72,
          "b": 36
        }
      },
      {
        "by": 20,
        "category": "reach",
        "leads": "nanbeige-4-2-3b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.",
          "b": "Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, 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."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "nanbeige-4-2-3b",
        "name": "Risk",
        "reasons": {
          "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.",
          "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": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nanbeige LLM Lab (BOSS Zhipin)",
        "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": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-09-11",
        "b": "2026-08-23",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "none",
        "b": "2/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Nanbeige4.2-3B or Ornith-1.5-397B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 65",
          "Risk, 55 against 50",
          "Documentation and upkeep, 72 against 36"
        ],
        "also": null,
        "goodFor": "Local agents that need tool calling and a long context on a laptop or a small GPU",
        "slug": "nanbeige-4-2-3b",
        "watchFor": "Its own architecture: Transformers loads it only with the repository's code"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-397b",
        "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b.json",
        "title": "Agents-A1-4B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b.json",
        "title": "Apertus v1.5 8B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b.json",
        "title": "Apodex-1.1-mini vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.json",
        "title": "Arete-27B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.json",
        "title": "Arete-27B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b.json",
        "title": "BigBang-v1 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b.json",
        "title": "BigBang-v1 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b.json",
        "title": "Carbon-X vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-397b.json",
        "title": "Carbon-X vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b.json",
        "title": "Cola2 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-2-3b.json",
        "title": "Cosmos-Reason2-2B vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-2-3b.json",
        "title": "Darwin-36B-Opus vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-2-3b.json",
        "title": "DeepSeek-V3.2 vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b.json",
        "title": "DeepSeek V4.1 Flash vs Nanbeige4.2-3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-2-3b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-2-3b"
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        "title": "Nanbeige4.2-3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-zaya1-8b"
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        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-397b.json",
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        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-397B",
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-397b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-397B",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-30B-A3B-Instruct",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint.json",
        "title": "Ornith-1.5-397B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview.json",
        "title": "Ornith-1.5-397B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ripple-65b-preview"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-sensenova-loop-9b-preview"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
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        "title": "Ornith-1.5-397B vs Step 3.7 Flash",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-397B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-397B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b.json",
        "title": "Ornith-1.5-397B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "nanbeige-4-2-3b",
        "key": "reach",
        "name": "Reach",
        "nanbeige-4-2-3b": 85,
        "ornith-1-5-397b": 65,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "nanbeige-4-2-3b",
        "key": "risk",
        "name": "Risk",
        "nanbeige-4-2-3b": 55,
        "ornith-1-5-397b": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "nanbeige-4-2-3b": 85,
        "ornith-1-5-397b": 85,
        "weight": 10
      },
      {
        "by": 36,
        "edge": "nanbeige-4-2-3b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "nanbeige-4-2-3b": 72,
        "ornith-1-5-397b": 36,
        "weight": 10
      }
    ],
    "summary": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "nanbeige-4-2-3b": "A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.",
      "ornith-1-5-397b": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b",
    "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.min.md"
  },
  "markdown": "Nanbeige4.2-3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Nanbeige4.2-3B: grade C, 61/100, rank #78 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-2-3b.json\n- Ornith-1.5-397B: grade C, 59/100, rank #100 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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\n## Which one, for what\n\n### Nanbeige4.2-3B (C)\n\nGood for: Local agents that need tool calling and a long context on a laptop or a small GPU\n\nAhead on:\n- Reach, 85 against 65\n- Risk, 55 against 50\n- Documentation and upkeep, 72 against 36\n\nWatch for: Its own architecture: Transformers loads it only with the repository's code\n\n### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Why the edge\n\n### Documentation and upkeep: Nanbeige4.2-3B +36\n\n- Nanbeige4.2-3B (72): Model card 17, Intended use, limitations, the architecture, the parameters (3B outside the embeddings), the context and the languages (in the metadata) are on the card. Training data 5, A general description of how the fine-tuning and reinforcement learning data was built and filtered, with no sources, sizes or cut-off. Last change 15, the last commit, on 11 September 2026, updated modeling_nanbeige.py, 60 days before this check and within 90. Responsiveness 20, Of the 34 most recent discussions that aren't pull requests (21 July to 10 September 2026), 20 have a reply from a Nanbeige member, which is most. Version policy 5, Nanbeige's earlier models (Nanbeige4-3B, 4.1-3B) stay in their own repositories, as the Base, FP8, 8-bit and DSpark builds of this one do. File health 10, Config, tokenizer config, template and card agree on the architecture and the 262,144 tokens. 17 + 5 + 15 + 20 + 5 + 10 = 72.\n- Ornith-1.5-397B (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, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use.\n\n### Reach: Nanbeige4.2-3B +20\n\n- Nanbeige4.2-3B (85): Memory 30, the smallest build from Nanbeige is the FP8 repository (the 8-bit GPTQ one is the same size), 5.19 GB, and 5.19 GB × 1.2 = 6.2 GB, within 16 GB. bartowski's GGUF isn't read for this line. Runtimes 15, Released SGLang 0.5.23 has the model (srt/models/nanbeige.py) with its parsers, llama.cpp b12242 has the nanbeige architecture, and vLLM 0.33.0 names NanbeigeForCausalLM in its registry, served through its Transformers backend. Builds 15, Quantised builds from Nanbeige at two precisions, FP8 and 8-bit GPTQ, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence, with no use policy or other terms on the card. Download 10, the Hub API reports gated false for the model and its FP8 and 8-bit repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and no Nanbeige or BOSS Zhipin API for it was found. 30 + 15 + 15 + 15 + 10 + 0 = 85.\n- Ornith-1.5-397B (65): Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, 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.\n\n### Risk: Nanbeige4.2-3B +5\n\n- Nanbeige4.2-3B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the Nanbeige organisation (Nanbeige LLM Lab), which the lab's arXiv report for this model links, so the publisher's own account, although the Hub hasn't verified it (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitations section says outputs may be inaccurate, biased or harmful. Licence risk 15, Apache 2.0 has no use policy, no threshold and no clause letting the licensor change or revoke it. Security programme 5, BOSS Zhipin runs a security response centre for reporting flaws in its products, as read for the Nanbeige4.3-33B listing (5), though nothing links it to the model. 15 + 15 + 0 + 5 + 15 + 5 = 55.\n- Ornith-1.5-397B (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## Score by category\n\n| Category | Weight | Nanbeige4.2-3B | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 65 | Nanbeige4.2-3B +20 |\n| Risk | 20% (20 this run) | 55 | 50 | Nanbeige4.2-3B +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 72 | 36 | Nanbeige4.2-3B +36 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Nanbeige4.2-3B | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nanbeige LLM Lab (BOSS Zhipin) | 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 | Apache-2.0 (Hub metadata only; the repository has no LICENSE file) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-09-11 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2/5 (4) |\n\n## Verdicts\n\n**Nanbeige4.2-3B.** A small model built for agent work, with tool calling it was trained for, a thinking switch and a 256K context in about 5 GB, under Apache 2.0, from a lab that answers most questions. Its architecture is its own, so check your runtime: SGLang and llama.cpp have it, Transformers needs the repository's code, and Nanbeige ships no GGUF.\n\n**Ornith-1.5-397B.** Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.\n\n## Before you call either\n\n### Nanbeige4.2-3B\n\n1. Serve it with SGLang 0.5.23 or later and pass `--tool-call-parser nanbeige --reasoning-parser nanbeige`\n2. Pass `tools` in the request. The card recommends `tool_call_format=\"xml\"`\n3. Set `preserve_thinking` true for multi-turn tool use and coding, false for plain chat\n4. Use temperature 1.0 for agent and tool work and 0.6 for reasoning and chat, per the card\n5. For llama.cpp use b12242 or later. The GGUF files on the Hub are bartowski's, not Nanbeige's\n6. If you load it with Transformers, read modeling_nanbeige.py first: trust_remote_code runs it\n\n### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\n2. 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\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Nanbeige4.2-3B or Ornith-1.5-397B?\n\nNanbeige4.2-3B scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nanbeige-4-2-3b\", \"b\": \"ornith-1-5-397b\"}`. 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