{
  "data": {
    "a": {
      "slug": "kolibri-1",
      "name": "Kolibri-1",
      "vendor": "Aleph Alpha",
      "vendorUrl": "https://aleph-alpha.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kolibri-1 is Aleph Alpha's open-weight mixture-of-experts reasoning model for German and English, 78B parameters with 3.46B active per token, published on Hugging Face in FP8 and BF16 under Apache 2.0 and served with vLLM.",
      "url": "https://www.anchorterminal.com/tools/kolibri-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kolibri-1.json",
      "repo": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1"
        },
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1-BF16"
        },
        {
          "registry": "pypi",
          "name": "aleph-alpha-inference"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Aleph Alpha sells no per-call access to these weights. Running them costs the hardware, about 78 GB of accelerator memory for the FP8 weights per the model card (checked 2026-10-20).",
      "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 2026-10-20).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-20"
      },
      "docsUrl": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "german",
        "apache-2.0",
        "safetensors",
        "fp8",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-19",
      "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": 1197,
        "categoryRank": 31,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 60,
          "risk": 65,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.",
        "bestFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.",
        "strengths": [
          "Apache 2.0 with no use policy, downloadable with no account",
          "Safetensors only, in Aleph Alpha's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with the kolibri1 parser for vLLM and a working example",
          "Thinking can be set from none to high per request, with the reasoning returned separately",
          "262,144 tokens trained, with a full model card and a data card that gives the mix, filtering and cut-off"
        ],
        "weaknesses": [
          "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine",
          "No Ollama, LM Studio or SGLang support in a released version, and vLLM only through Aleph Alpha's plugin pinned to vLLM 0.29",
          "No safety evaluation results and no security contact or disclosure policy found",
          "Aleph Alpha answered 2 of 11 discussions, and a report of a broken tool-call parser in its container image has no reply",
          "No hosted API or inference provider, so an agent without the hardware can't call it",
          "Structured output is claimed without saying how to request it"
        ],
        "agentNotes": [
          "Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
          "Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags",
          "The plugin supports one vLLM minor version, 0.29, so don't upgrade vLLM while it's installed",
          "Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks",
          "Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128",
          "Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144",
          "With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking",
          "On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp",
          "Validate tool results in your own code, as the card asks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 90,
          "reach": 60,
          "risk": 65,
          "upkeep": 70
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "lastMoved": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install 'aleph-alpha-inference\u003e=1'\nvllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 \\\n  --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"Aleph-Alpha/Kolibri-1\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kolibri-1"
      },
      "weights": {
        "trainedBy": "Aleph Alpha",
        "params": "78.1B",
        "active": "3.46B",
        "context": 262144,
        "goodFor": "German and English reasoning model with tool calling and adjustable reasoning effort, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Kolibri-1",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 78.8,
            "memoryGB": 96,
            "format": "safetensors"
          },
          {
            "name": "Kolibri-1-BF16",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 156.2,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)"
            ]
          }
        ],
        "run": "pip install 'aleph-alpha-inference\u003e=1' \u0026\u0026 vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
        "runBuild": "Kolibri-1",
        "checked": "2026-10-21",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Aleph Alpha",
        "family": "Kolibri",
        "url": "https://www.anchorterminal.com/open-weights/#aleph-alpha",
        "olderCount": 23,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Aleph Alpha GmbH",
        "domain": "aleph-alpha.com",
        "domainRegistered": "2018-08-11",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-20",
        "notes": [
          "The model card names Aleph Alpha GmbH as the provider and Aleph Alpha Research GmbH as the developer. The imprint names Aleph Alpha GmbH, Heidelberg, commercial register Mannheim HRB 732804.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, whose messages say what changed (Release 2026-10-02, a quantisation config fix on 13 October, transformers_version added on 19 October).",
          "aleph-alpha.com/.well-known/security.txt answered 403 to our reader on 20 October 2026, so it is counted as unchecked rather than absent."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kolibri-1.json",
      "live": {
        "slug": "kolibri-1",
        "versions": [
          {
            "registry": "pypi",
            "name": "aleph-alpha-inference",
            "version": "1.0.0",
            "released": "2026-10-03",
            "seenAt": "2026-10-21T06:04:10.256551317Z"
          }
        ],
        "pypiWeekly": 639,
        "securityTxt": {
          "url": "https://aleph-alpha.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-21T15:59:42.849148653Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-21T00:49:59.261303558Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "037a793cc2d6"
          }
        ],
        "updatedAt": "2026-10-21T15:59:42.849148653Z"
      }
    },
    "answer": "Kolibri-1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on reach.",
    "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",
      "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": "2026-10-21"
      },
      "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": 1197,
        "categoryRank": 53,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "Ornith's flagship coding and terminal-agent cousin, 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 risk is an absent publisher, with no licence file, no safety results 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": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 65,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "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-1-5-35b-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)"
          },
          {
            "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": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Aleph Alpha",
        "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": "2026-10-19",
        "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": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "3.3/5 (4)",
        "b": "2/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
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        "answer": "Kolibri-1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on reach.",
        "question": "Which is better for AI agents, Kolibri-1 or Ornith-1.5-397B?"
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    "goodFor": [
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        "aheadOn": [
          "Risk, 65 against 50",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 70 against 36"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 2.0 out of 5"
        ],
        "goodFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.",
        "slug": "kolibri-1",
        "watchFor": "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine"
      },
      {
        "aheadOn": [
          "Reach, 65 against 60"
        ],
        "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
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    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b"
      },
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json",
        "title": "Apertus v1.5 8B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json",
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        "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-kolibri-1.json",
        "title": "Apodex-1.1-mini vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1"
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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"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.json",
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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-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-397b.json",
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        "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-kolibri-1.json",
        "title": "DeepSeek V4.1 Flash vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1"
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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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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-397b.json",
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        "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/gemma-4-12b-it-vs-kolibri-1.json",
        "title": "Gemma 4 12B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-397b.json",
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        "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-kolibri-1.json",
        "title": "Gemma 4 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 26B A4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json",
        "title": "Gemma 4 31B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-397b.json",
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        "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-kolibri-1.json",
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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"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 E4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b.json",
        "title": "Gemma 4 E4B IT vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.json",
        "title": "GLM-4.5-Air vs Kolibri-1",
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-397b"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kolibri-1.json",
        "title": "gpt-oss-120b vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b.json",
        "title": "gpt-oss-120b vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1.json",
        "title": "gpt-oss-20b vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b.json",
        "title": "gpt-oss-20b vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1.json",
        "title": "Granite 4.2 3B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b.json",
        "title": "Granite 4.2 3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.json",
        "title": "Hy3 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json",
        "title": "Hy3 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1.json",
        "title": "Inkling-Small vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b.json",
        "title": "Inkling-Small vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.json",
        "title": "Inkling vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b.json",
        "title": "Inkling vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-397b.json",
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-397B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-397B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-397B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-397B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-8-27b"
      },
      {
        "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"
      },
      {
        "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",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-397B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-397B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-397B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-397B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-397B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "ornith-1-5-397b",
        "key": "reach",
        "kolibri-1": 60,
        "name": "Reach",
        "ornith-1-5-397b": 65,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "kolibri-1",
        "key": "risk",
        "kolibri-1": 65,
        "name": "Risk",
        "ornith-1-5-397b": 50,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "key": "interface",
        "kolibri-1": 90,
        "name": "Agent interface",
        "ornith-1-5-397b": 85,
        "weight": 10
      },
      {
        "by": 34,
        "edge": "kolibri-1",
        "key": "upkeep",
        "kolibri-1": 70,
        "name": "Documentation and upkeep",
        "ornith-1-5-397b": 36,
        "weight": 10
      }
    ],
    "summary": "Kolibri-1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on reach. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "kolibri-1": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.",
      "ornith-1-5-397b": "Ornith's flagship coding and terminal-agent cousin, 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 risk is an absent publisher, with no licence file, no safety results 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/kolibri-1-vs-ornith-1-5-397b",
    "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.min.md"
  },
  "markdown": "Kolibri-1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on reach. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #38 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kolibri-1 (C)\n\nGood for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.\n\nAhead on:\n- Risk, 65 against 50\n- Agent interface, 90 against 85\n- Documentation and upkeep, 70 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.0 out of 5\n\nWatch for: The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine\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\nAhead on:\n- Reach, 65 against 60\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## Score by category\n\n| Category | Weight | Kolibri-1 | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | Ornith-1.5-397B +5 |\n| Risk | 20% (20 this run) | 65 | 50 | Kolibri-1 +15 |\n| Agent interface | 10% (10 this run) | 90 | 85 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 36 | Kolibri-1 +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | 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 | 2026-10-19 | 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| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 3.3/5 (4) | 2/5 (4) |\n\n## Verdicts\n\n**Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.\n\n**Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, 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 risk is an absent publisher, with no licence file, no safety results and files replaced without a note.\n\n## Before you call either\n\n### Kolibri-1\n\n1. Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags\n3. The plugin supports one vLLM minor version, 0.29, so don't upgrade vLLM while it's installed\n4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks\n5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128\n6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144\n7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking\n8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp\n9. Validate tool results in your own code, as the card asks\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, Kolibri-1 or Ornith-1.5-397B?\n\nKolibri-1 scores 61 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in 3 of 4 scored categories. Ornith-1.5-397B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"ornith-1-5-397b\"}`. From a terminal: `anchor compare kolibri-1 ornith-1-5-397b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json\n\n## Other comparisons with Kolibri-1 or Ornith-1.5-397B\n\n- [Agents-A1-4B vs Kolibri-1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.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 Kolibri-1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.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 Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-397b.md)\n- [BigBang-v1 vs 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