{
  "data": {
    "a": {
      "slug": "north-mini-code-1-0",
      "name": "North Mini Code 1.0",
      "vendor": "Cohere",
      "vendorUrl": "https://cohere.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "North Mini Code 1.0 is Cohere's open-weight coding agent model, a mixture of experts of 30B parameters with 3B active, trained for tool use, with interleaved thinking and a 256K context. Apache 2.0. Cohere's 4-bit build is 19 GB.",
      "url": "https://www.anchorterminal.com/tools/north-mini-code-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json",
      "dns": "north-mini-code-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-fp8"
        },
        {
          "registry": "huggingface",
          "name": "CohereLabs/North-Mini-Code-1.0-w4a16"
        }
      ],
      "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. The card calls it a research release. Running it yourself takes 19.4 GB at 4 bits (w4a16), 32.1 GB in FP8 or 61.0 GB in BF16 (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/CohereLabs/North-Mini-Code-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "agentic",
        "reasoning",
        "tool-calling",
        "long-context",
        "apache-2.0",
        "safetensors",
        "fp8",
        "4-bit",
        "self-hosted"
      ],
      "lastRelease": "2026-07-29",
      "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": 63,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 60,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.",
        "bestFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "strengths": [
          "Trained for tool use in coding agents, with parsers in released vLLM",
          "19 GB at 4 bits from Cohere, and a 256K context",
          "Apache 2.0, ungated",
          "Four released runtimes carry the architecture",
          "Tool use documented with a worked example"
        ],
        "weaknesses": [
          "No limitations, safety results, languages or data description on the card",
          "No reply from Cohere on the repository's discussions",
          "The card's instructions still point at unreleased vLLM and Transformers",
          "No GGUF from Cohere",
          "Text only",
          "No hosted API named"
        ],
        "agentNotes": [
          "Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed",
          "Pass the model's thinking back with its tool calls on every turn. The card says results drop without it",
          "Describe tools in JSON schema and append tool results as a dictionary with the tool role",
          "Use temperature 1.0 and top_p 0.95",
          "For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's",
          "It's text only. Don't send images"
        ],
        "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": 60,
          "upkeep": 36
        },
        "provenanceScore": 75,
        "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 \"vllm\u003e=0.33.0\" \"cohere_melody\u003e=0.9.0\"\nvllm serve CohereLabs/North-Mini-Code-1.0 -tp 2 --max-model-len 320000 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Use temperature 1.0 and top_p 0.95,\n# and send the model's earlier thinking back with each turn"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/north-mini-code-1-0"
      },
      "sameCompany": [
        "cohere-chat",
        "cohere-embed",
        "cohere-transcribe-03-2026",
        "cohere-north"
      ],
      "weights": {
        "trainedBy": "Cohere and Cohere Labs",
        "params": "30B",
        "active": "3B",
        "context": 256000,
        "goodFor": "A local coding agent: repository work, terminal tasks and long tool-calling sessions, on one 24 GB GPU at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "North-Mini-Code-1.0-w4a16",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-w4a16",
            "who": "publisher",
            "precision": "w4a16",
            "sizeGB": 19.38,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM"
            ],
            "note": "Cohere's 4-bit weight build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (UD-Q4_K_M)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q4_k_m",
            "sizeGB": 19.2,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 19.20 GB of UD-Q4_K_M weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0-fp8",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0-fp8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 32.06,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Cohere's FP8 build"
          },
          {
            "name": "unsloth North-Mini-Code-1.0-GGUF (Q8_0)",
            "url": "https://huggingface.co/unsloth/North-Mini-Code-1.0-GGUF",
            "who": "unsloth",
            "precision": "q8_0",
            "sizeGB": 32.44,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From Unsloth, a third party's conversion and not Cohere's. 32.44 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "North-Mini-Code-1.0 (BF16 safetensors)",
            "url": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.97,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "49 files"
          }
        ],
        "run": "vllm serve CohereLabs/North-Mini-Code-1.0-w4a16 --tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice",
        "runBuild": "North-Mini-Code-1.0-w4a16",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Cohere (Cohere Labs)",
        "family": "North Mini Code",
        "url": "https://www.anchorterminal.com/open-weights/#cohere",
        "olderCount": 29
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Cohere Inc.",
        "domain": "cohere.com",
        "domainRegistered": "2000-03-07",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/CohereLabs/North-Mini-Code-1.0/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The card says Developed by Cohere and Cohere Labs. Legal entity, domain and security facts are carried from our other Cohere listings.",
          "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, 8 commits from 5 to 15 June 2026."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/north-mini-code-1-0.json"
    },
    "answer": "North Mini Code 1.0 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 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": 20,
        "category": "reach",
        "leads": "north-mini-code-1-0",
        "name": "Reach",
        "reasons": {
          "a": "Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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. 25 + 20 + 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": 10,
        "category": "risk",
        "leads": "north-mini-code-1-0",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.",
          "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
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Cohere",
        "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",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-29",
        "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": "North Mini Code 1.0 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories.",
        "question": "Which is better for AI agents, North Mini Code 1.0 or Ornith-1.5-397B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 65",
          "Risk, 60 against 50"
        ],
        "also": null,
        "goodFor": "Developers who want a coding agent on their own GPU with a permissive licence",
        "slug": "north-mini-code-1-0",
        "watchFor": "No limitations, safety results, languages or data description on the card"
      },
      {
        "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"
    },
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        "title": "Agents-A1-4B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "Apertus v1.5 8B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "Apodex-1.1-mini vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "Arete-27B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "BigBang-v1 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-north-mini-code-1-0"
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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-north-mini-code-1-0.json",
        "title": "Carbon-X vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-north-mini-code-1-0"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.json",
        "title": "Cola2 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.json",
        "title": "Cola2 vs Ornith-1.5-397B",
        "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-north-mini-code-1-0.json",
        "title": "Cosmos-Reason2-2B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-north-mini-code-1-0"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0.json",
        "title": "Darwin-36B-Opus vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-north-mini-code-1-0"
      },
      {
        "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-north-mini-code-1-0.json",
        "title": "DeepSeek-V3.2 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-north-mini-code-1-0"
      },
      {
        "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"
      },
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0.json",
        "title": "DeepSeek V4.1 Flash vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.json",
        "title": "DeepSeek V4.1 Flash vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0.json",
        "title": "DeepSeek-V4-Flash-0731 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0.json",
        "title": "Devstral Small 2 vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.json",
        "title": "Devstral Small 2 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0.json",
        "title": "DiffusionGemma 26B A4B IT vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-north-mini-code-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.json",
        "title": "DiffusionGemma 26B A4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0.json",
        "title": "EXAONE 4.5 33B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-north-mini-code-1-0"
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        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b.json",
        "title": "EXAONE 4.5 33B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0.json",
        "title": "Gemma 4 12B IT vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 12B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0.json",
        "title": "Gemma 4 26B A4B IT vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 31B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0.json",
        "title": "Gemma 4 E2B IT vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0.json",
        "title": "Gemma 4 E4B IT vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-north-mini-code-1-0"
      },
      {
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        "title": "occamy-1.0 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b.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",
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        "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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        "title": "Ornith-1.5-397B vs SmolLM3-3B",
        "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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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json",
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-397B vs WeVisOne-VL-4B",
        "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": "north-mini-code-1-0",
        "key": "reach",
        "name": "Reach",
        "north-mini-code-1-0": 85,
        "ornith-1-5-397b": 65,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "north-mini-code-1-0",
        "key": "risk",
        "name": "Risk",
        "north-mini-code-1-0": 60,
        "ornith-1-5-397b": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "key": "interface",
        "name": "Agent interface",
        "north-mini-code-1-0": 85,
        "ornith-1-5-397b": 85,
        "weight": 10
      },
      {
        "by": 0,
        "edge": "",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "north-mini-code-1-0": 36,
        "ornith-1-5-397b": 36,
        "weight": 10
      }
    ],
    "summary": "North Mini Code 1.0 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "north-mini-code-1-0": "A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.",
      "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/north-mini-code-1-0-vs-ornith-1-5-397b",
    "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.min.md"
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  "markdown": "North Mini Code 1.0 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories. Both do open-weight models you run yourself.\n\n- North Mini Code 1.0: grade C, 61/100, rank #80 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/north-mini-code-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.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### North Mini Code 1.0 (C)\n\nGood for: Developers who want a coding agent on their own GPU with a permissive licence\n\nAhead on:\n- Reach, 85 against 65\n- Risk, 60 against 50\n\nWatch for: No limitations, safety results, languages or data description on the card\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### Reach: North Mini Code 1.0 +20\n\n- North Mini Code 1.0 (85): Memory 25, the smallest build from Cohere is the w4a16 repository, 19.38 GB, and 19.38 GB × 1.2 = 23.3 GB, within 24 GB. Unsloth's GGUF isn't read for this line. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the cohere2_moe model, and llama.cpp b12242 has the cohere2moe architecture. Builds 15, Quantised builds from Cohere at two precisions, FP8 and 4-bit weights (w4a16), each in its own repository. Terms 15, Apache 2.0 in the metadata and on the card, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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. 25 + 20 + 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: North Mini Code 1.0 +10\n\n- North Mini Code 1.0 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in Cohere's verified CohereLabs organisation, which cohere.com links to per our other Cohere listings (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, Cohere's trust centre holds a responsible disclosure policy with a bug bounty, as recorded on our Cohere listings (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\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 | North Mini Code 1.0 | 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 | North Mini Code 1.0 +20 |\n| Risk | 20% (20 this run) | 60 | 50 | North Mini Code 1.0 +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 36 | 36 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | North Mini Code 1.0 | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Cohere | 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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-29 | 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**North Mini Code 1.0.** A coding agent model that fits one 24 GB GPU at 4 bits, trained for tool use, with a 256K context, Apache 2.0 and four released runtimes. It's a research release and reads like one: nothing on limitations, safety or data, the card's run instructions are out of date, and Cohere hasn't answered a question on the repository.\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### North Mini Code 1.0\n\n1. Serve it with vLLM 0.33.0 or later and `--tool-call-parser cohere_command4 --reasoning-parser cohere_command4 --enable-auto-tool-choice`, with `cohere_melody` installed\n2. Pass the model's thinking back with its tool calls on every turn. The card says results drop without it\n3. Describe tools in JSON schema and append tool results as a dictionary with the tool role\n4. Use temperature 1.0 and top_p 0.95\n5. For llama.cpp the GGUF files are Unsloth's or bartowski's conversions, not Cohere's\n6. It's text only. Don't send images\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, North Mini Code 1.0 or Ornith-1.5-397B?\n\nNorth Mini Code 1.0 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 2 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"north-mini-code-1-0\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare north-mini-code-1-0 ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/north-mini-code-1-0.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with North Mini Code 1.0 or Ornith-1.5-397B\n\n- [Agents-A1-4B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-north-mini-code-1-0.md)\n- [Agents-A1-4B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-north-mini-code-1-0.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apodex-1.1-mini vs North Mini Code 1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-north-mini-code-1-0.md)\n- [Apodex-1.1-mini vs 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