{
  "data": {
    "a": {
      "slug": "ornith-1-5-122b",
      "name": "Ornith-1.5-122B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json",
      "dns": "ornith-1-5-122b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:122b-q4_K_M"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 64,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
        "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published",
          "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`",
          "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends",
          "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-122b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it",
        "params": "125B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-122B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 77.7,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-122B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 75.8,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-122B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 69.7,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:122b-q4_K_M",
            "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 77,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build"
          },
          {
            "name": "Ornith-1.5-122B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 102.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 88.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 133,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 132.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 130.7,
            "memoryGB": 192,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 249.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 245.2,
            "memoryGB": 512,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:122b-q4_K_M",
        "runBuild": "ollama ornith-1.5:122b-q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. The commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json"
    },
    "answer": "Ornith-1.5-122B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on reach, risk, agent interface and documentation and upkeep.",
    "b": {
      "slug": "recursive-flint",
      "name": "Recursive Flint",
      "vendor": "Recursive",
      "vendorUrl": "https://www.recursive.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Recursive Flint is Recursive's open-weight mixture-of-experts coding model for agents, 39.8B total and about 2.5B active parameters, with reasoning, tool calling and a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/recursive-flint",
      "markdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/recursive-flint.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/recursive-flint.json",
      "dns": "recursive-flint.grade.anchorterminal.com",
      "repo": "https://huggingface.co/RecursiveSI/flint",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "RecursiveSI/flint"
        },
        {
          "registry": "huggingface",
          "name": "RecursiveSI/flint-int4"
        },
        {
          "registry": "huggingface",
          "name": "RecursiveSI/flint-int4-gguf"
        },
        {
          "registry": "huggingface",
          "name": "RecursiveSI/flint-nvfp4-gguf"
        },
        {
          "registry": "ollama",
          "name": "flint"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and all three quantised builds (checked 2026-10-22).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 79.6 GB for the BF16 files and 22.6 GB for Recursive's smallest 4-bit build (MLX INT4). OpenRouter lists a free endpoint from Recursive, recursive/flint-v1.0:free, which wasn't serving on 3 November 2026 (checked 2026-11-03).",
      "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 2026-10-22).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/RecursiveSI/flint",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "coding",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mlx",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-21",
      "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": 44,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 67
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.",
        "bestFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a sparse coder with documented tool calls and a plain licence.",
        "strengths": [
          "Plain Apache 2.0 with no gate, account or use policy",
          "Recursive's own MLX INT4, Q4_1 GGUF and NVFP4 GGUF builds, the smallest 22.6 GB",
          "Tool format, parser, parallel calls and JSON Schema output documented by Recursive",
          "262,144 tokens of context with no position scaling",
          "Safetensors and GGUF only, with a checksum on every file in Recursive's organisation",
          "Released support in llama.cpp, Ollama, LM Studio, Transformers v5.21 and vLLM v0.33.0"
        ],
        "weaknesses": [
          "No independent benchmark results yet, every published score is Recursive's",
          "No reasoning switch or budget, so hard prompts can spend tens of thousands of tokens thinking",
          "The BF16 route on the card still needs one 141 GB GPU and Recursive's SGLang plugin, and released SGLang doesn't run it",
          "No safety results and no trained instruction hierarchy",
          "Recursive answered one of 31 discussions, on release day"
        ],
        "agentNotes": [
          "Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp",
          "With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on",
          "Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out",
          "Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask",
          "Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call",
          "Message content must be a string, flatten content-part arrays first",
          "Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key",
          "Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026",
          "vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.5,
        "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": 90,
          "reach": 85,
          "risk": 60,
          "upkeep": 67
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run flint\n# or, with llama.cpp b11803 or later:\nllama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 262144 -np 1 -fa on --jinja \\\n  --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"flint\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/recursive-flint"
      },
      "weights": {
        "trainedBy": "Recursive",
        "params": "39.8B total, about 2.5B active",
        "context": 262144,
        "goodFor": "Agentic coding on a 32 to 48 GB machine, investigating codebases, fixing bugs and writing tests with multi-turn tool calls and reasoning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "LM Studio",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "flint-int4",
            "url": "https://huggingface.co/RecursiveSI/flint-int4",
            "who": "publisher",
            "precision": "int4 (group-64 affine, INT8 embeddings)",
            "sizeGB": 22.6,
            "memoryGB": 32,
            "format": "mlx",
            "runsOn": [
              "rec",
              "Ollama"
            ],
            "note": "For Apple Silicon. Recursive's rec engine asks for an M4 Pro or newer with 48 GB"
          },
          {
            "name": "flint-int4-gguf Q4_1",
            "url": "https://huggingface.co/RecursiveSI/flint-int4-gguf",
            "who": "publisher",
            "precision": "q4_1",
            "sizeGB": 26,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "flint-int4-q4_1.gguf, 25,992,966,976 bytes, the INT4 export's trained codes stored as Q4_1. The card targets Metal and asks for a Mac with at least 48 GB at full context"
          },
          {
            "name": "flint-nvfp4-gguf",
            "url": "https://huggingface.co/RecursiveSI/flint-nvfp4-gguf",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 22.9,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "flint-nvfp4.gguf, 22,863,084,160 bytes, for NVIDIA GPUs with CUDA. The card recommends at least 48 GB at full context"
          },
          {
            "name": "ollama flint:latest",
            "url": "https://ollama.com/library/flint:latest",
            "who": "ollama",
            "precision": "4-bit",
            "sizeGB": 23,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Digest 905c74dfd56b, shown as 23 GB (Ollama rounds to whole GB). The library also has qat-int4 (23 GB), q4_1 (26 GB), gguf-nvfp4 (23 GB) and mlx-bf16 (80 GB) tags. Ollama 0.40.8 runs Flint in its MLX runner; its bundled llama.cpp (b11738) predates Flint, so the GGUF tags on Linux weren't confirmed in a release"
          },
          {
            "name": "lmstudio-community flint-GGUF Q4_K_M",
            "url": "https://huggingface.co/lmstudio-community/flint-GGUF",
            "who": "lmstudio",
            "precision": "q4_k_m",
            "sizeGB": 24.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "LM Studio",
              "llama.cpp"
            ],
            "note": "flint-Q4_K_M.gguf, 24,166,841,184 bytes, the build behind LM Studio's recursivesi/flint catalogue page. Also Q6_K (32.7 GB, 48 GB of memory) and Q8_0 (42.4 GB, 64 GB)"
          },
          {
            "name": "flint",
            "url": "https://huggingface.co/RecursiveSI/flint",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 79.6,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "SGLang 0.5.16 with Recursive's flint_sglang kit"
            ],
            "note": "Recursive's SGLang kit needs one GPU with at least 141 GB (B300, B200 or H200). Transformers support isn't in a release yet"
          }
        ],
        "run": "llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0 --port 8080",
        "runBuild": "flint-int4-gguf Q4_1",
        "checked": "2026-11-03",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Recursive",
        "family": "Flint",
        "url": "https://www.anchorterminal.com/open-weights/#recursive"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Recursive Superintelligence, Inc. (the Hugging Face organisation's full name)",
        "domain": "recursive.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "unknown",
        "checked": "2026-10-22",
        "notes": [
          "The Hugging Face organisation RecursiveSI is named Recursive Superintelligence, Inc. and isn't verified by Hugging Face. Recursive's rec repository, which carries Recursive's logo and links recursive.com, downloads the INT4 build from RecursiveSI/flint-int4 at a pinned commit.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The card links www.recursive.com, docs.recursive.com and github.com/recursive-oss/rec. Neither recursive.com host could be read on 22 October 2026, so security.txt and the domain's registration weren't checked.",
          "The card names a security reporting address at recursive.com, and rec's SECURITY.md asks for private reports through GitHub's vulnerability reporting."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/recursive-flint.json",
      "live": {
        "slug": "recursive-flint",
        "securityTxt": {
          "url": "https://recursive.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:19.880436818Z"
        },
        "domain": {
          "domain": "recursive.com",
          "registered": "1995-08-24",
          "source": "https://rdap.verisign.com/com/v1/domain/recursive.com",
          "checkedAt": "2026-11-08T15:12:33.104118016Z"
        },
        "updatedAt": "2026-11-10T16:07:19.880436818Z"
      }
    },
    "edges": [
      {
        "by": 31,
        "category": "upkeep",
        "leads": "recursive-flint",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.",
          "b": "Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits."
        },
        "scores": {
          "a": 36,
          "b": 67
        }
      },
      {
        "by": 10,
        "category": "reach",
        "leads": "recursive-flint",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.",
          "b": "Memory 20 of 30, Recursive's MLX INT4 build is 22.6 GB, or 27.1 GB with a fifth added, which fits 32 GB, and every other 4-bit build lands in the same band. Runtimes 25 of 25, released llama.cpp (b11803), Ollama 0.40.8, LM Studio's catalogue, Transformers v5.21 and vLLM v0.33.0 run it, while SGLang has only a development image. Builds 15, MLX INT4 plus Q4_1 and NVFP4 GGUFs from Recursive. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, OpenRouter lists a free endpoint with Recursive as its provider, but it wasn't serving on 3 November 2026, with 0 per cent uptime over the last day."
        },
        "scores": {
          "a": 75,
          "b": 85
        }
      },
      {
        "by": 10,
        "category": "risk",
        "leads": "recursive-flint",
        "name": "Risk",
        "reasons": {
          "a": "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.",
          "b": "File safety 15 of 25, safetensors and GGUF only, but the card's documented BF16 route installs the wheel shipped in the repository. Provenance 15, the files are in the RecursiveSI organisation that Recursive's own engine downloads from, with a checksum on every file. Safety evaluation 0, none on the cards or in the announcement post. Agent guidance 10, the card and serving README say to enforce permissions around tool use and to strip tool tags from untrusted tool output, but there's no trained instruction hierarchy. Licence risk 15, plain Apache 2.0. Security programme 5, a reporting address on the card and a private reporting route for the engine, with no public advisories yet."
        },
        "scores": {
          "a": 50,
          "b": 60
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "recursive-flint",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.",
          "b": "Chat template 20, a Jinja file in the repository and inside both GGUFs. Tool-calling format 25, Recursive's serving README documents how tools go in, how calls come back and the flint parser. Trained tool calling 20, several calls a turn. Structured output 15, response_format with JSON Schema is documented. Reasoning control 0, the model decides when to reason and Recursive says there's no switch or budget. Context 10, 262,144 tokens with no position scaling."
        },
        "scores": {
          "a": 85,
          "b": 90
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Ornith",
        "b": "Recursive",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "MIT",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-24",
        "b": "2026-10-21",
        "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": "3.5/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Ornith-1.5-122B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on reach, risk, agent interface and documentation and upkeep.",
        "question": "Which is better for AI agents, Ornith-1.5-122B or Recursive Flint?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-122b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      },
      {
        "aheadOn": [
          "Reach, 85 against 75",
          "Risk, 60 against 50",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 67 against 36"
        ],
        "also": null,
        "goodFor": "Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a sparse coder with documented tool calls and a plain licence.",
        "slug": "recursive-flint",
        "watchFor": "No independent benchmark results yet, every published score is Recursive's"
      }
    ],
    "history": {
      "ornith-1-5-122b": null,
      "recursive-flint": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.json",
        "title": "Agents-A1-4B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.json",
        "title": "Apertus v1.5 8B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b.json",
        "title": "Apodex-1.1-mini vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.json",
        "title": "Apodex-1.1-mini vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.json",
        "title": "Arete-27B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.json",
        "title": "Arete-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.json",
        "title": "BigBang-v1 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint.json",
        "title": "BigBang-v1 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b.json",
        "title": "Carbon-X vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.json",
        "title": "Carbon-X vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json",
        "title": "Cola2 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint.json",
        "title": "Cola2 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b.json",
        "title": "Cosmos-Reason2-2B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint.json",
        "title": "Cosmos-Reason2-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b.json",
        "title": "Darwin-36B-Opus vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint.json",
        "title": "Darwin-36B-Opus vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint.json",
        "title": "DeepSeek-V3.2 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b.json",
        "title": "DeepSeek V4.1 Flash vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-122b"
      },
      {
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-122B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview.json",
        "title": "Ornith-1.5-122B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview.json",
        "title": "Ornith-1.5-122B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-122B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-122B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash.json",
        "title": "Ornith-1.5-122B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-122B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-122B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2.json",
        "title": "Ornith-1.5-122B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b.json",
        "title": "Ornith-1.5-122B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b.json",
        "title": "Ornith-1.5-122B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b.json",
        "title": "Ornith-1.5-122B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-zaya1-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint.json",
        "title": "Ornith-1.5-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint.json",
        "title": "Ornith-1.5-397B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint.json",
        "title": "Ornith-1.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint.json",
        "title": "OTel-2.0-LLM-31B-IT vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint.json",
        "title": "Qwen-AgentWorld-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen-agentworld-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint.json",
        "title": "Qwen3-30B-A3B-Instruct-2507 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint.json",
        "title": "Qwen3-4B-Instruct-2507 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint.json",
        "title": "Qwen3.5-0.8B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint.json",
        "title": "Qwen3.5-122B-A10B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint.json",
        "title": "Qwen3.5-2B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint.json",
        "title": "Qwen3.5-397B-A17B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint.json",
        "title": "Qwen3.5-4B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint.json",
        "title": "Qwen3.5-9B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint.json",
        "title": "Qwen3.6-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint.json",
        "title": "Qwen3.6-35B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint.json",
        "title": "Qwen3.8-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint.json",
        "title": "Qwen3.8-Flash-Next vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint.json",
        "title": "Qwen3-Coder-Next vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-2B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-30B-A3B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-32B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-4B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint.json",
        "title": "Qwen3-VL-8B-Instruct vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint.json",
        "title": "Qwopus3.8-27B-Flash-V2 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview.json",
        "title": "Recursive Flint vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview.json",
        "title": "Recursive Flint vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b.json",
        "title": "Recursive Flint vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json",
        "title": "Recursive Flint vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash.json",
        "title": "Recursive Flint vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Recursive Flint vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Recursive Flint vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json",
        "title": "Recursive Flint vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b.json",
        "title": "Recursive Flint vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b.json",
        "title": "Recursive Flint vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b.json",
        "title": "Recursive Flint vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 10,
        "edge": "recursive-flint",
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-122b": 75,
        "recursive-flint": 85,
        "weight": 25
      },
      {
        "by": 10,
        "edge": "recursive-flint",
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-122b": 50,
        "recursive-flint": 60,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "recursive-flint",
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-122b": 85,
        "recursive-flint": 90,
        "weight": 10
      },
      {
        "by": 31,
        "edge": "recursive-flint",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-122b": 36,
        "recursive-flint": 67,
        "weight": 10
      }
    ],
    "summary": "Ornith-1.5-122B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
      "recursive-flint": "A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint",
    "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.min.md"
  },
  "markdown": "Ornith-1.5-122B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n- Recursive Flint: grade C, 61/100, rank #54 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/recursive-flint.md · JSON https://www.anchorterminal.com/api/v1/tools/recursive-flint.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### Ornith-1.5-122B (C)\n\nGood for: Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n### Recursive Flint (C)\n\nGood for: Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a sparse coder with documented tool calls and a plain licence.\n\nAhead on:\n- Reach, 85 against 75\n- Risk, 60 against 50\n- Agent interface, 90 against 85\n- Documentation and upkeep, 67 against 36\n\nWatch for: No independent benchmark results yet, every published score is Recursive's\n\n\n## Why the edge\n\n### Documentation and upkeep: Recursive Flint +31\n\n- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n- Recursive Flint (67): Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits.\n\n### Reach: Recursive Flint +10\n\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n- Recursive Flint (85): Memory 20 of 30, Recursive's MLX INT4 build is 22.6 GB, or 27.1 GB with a fifth added, which fits 32 GB, and every other 4-bit build lands in the same band. Runtimes 25 of 25, released llama.cpp (b11803), Ollama 0.40.8, LM Studio's catalogue, Transformers v5.21 and vLLM v0.33.0 run it, while SGLang has only a development image. Builds 15, MLX INT4 plus Q4_1 and NVFP4 GGUFs from Recursive. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, OpenRouter lists a free endpoint with Recursive as its provider, but it wasn't serving on 3 November 2026, with 0 per cent uptime over the last day.\n\n### Risk: Recursive Flint +10\n\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n- Recursive Flint (60): File safety 15 of 25, safetensors and GGUF only, but the card's documented BF16 route installs the wheel shipped in the repository. Provenance 15, the files are in the RecursiveSI organisation that Recursive's own engine downloads from, with a checksum on every file. Safety evaluation 0, none on the cards or in the announcement post. Agent guidance 10, the card and serving README say to enforce permissions around tool use and to strip tool tags from untrusted tool output, but there's no trained instruction hierarchy. Licence risk 15, plain Apache 2.0. Security programme 5, a reporting address on the card and a private reporting route for the engine, with no public advisories yet.\n\n### Agent interface: Recursive Flint +5\n\n- Ornith-1.5-122B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n- Recursive Flint (90): Chat template 20, a Jinja file in the repository and inside both GGUFs. Tool-calling format 25, Recursive's serving README documents how tools go in, how calls come back and the flint parser. Trained tool calling 20, several calls a turn. Structured output 15, response_format with JSON Schema is documented. Reasoning control 0, the model decides when to reason and Recursive says there's no switch or budget. Context 10, 262,144 tokens with no position scaling.\n\n## Score by category\n\n| Category | Weight | Ornith-1.5-122B | Recursive Flint | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 75 | 85 | Recursive Flint +10 |\n| Risk | 20% (20 this run) | 50 | 60 | Recursive Flint +10 |\n| Agent interface | 10% (10 this run) | 85 | 90 | Recursive Flint +5 |\n| Documentation and upkeep | 10% (10 this run) | 36 | 67 | Recursive Flint +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Ornith-1.5-122B | Recursive Flint |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Ornith | Recursive |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | MIT | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-24 | 2026-10-21 |\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 | 3.5/5 (4) |\n\n## How the scores have moved\n\n### Ornith-1.5-122B\n\nNo score change since it was listed.\n\n### Recursive Flint\n\n- no change on 3 November 2026: product\n\n## Verdicts\n\n**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\n\n**Recursive Flint.** A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.\n\n## Before you call either\n\n### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n### Recursive Flint\n\n1. Quickest start is `ollama run flint` (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp\n2. With llama.cpp use release b11803 or later: `llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0`, and keep --jinja on\n3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out\n4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask\n5. Strip \u003ctool_call\u003e, \u003ctool_response\u003e, \u003carg_key\u003e and \u003carg_value\u003e from untrusted tool output, the template doesn't escape them and a copied \u003c/tool_call\u003e can add a call\n6. Message content must be a string, flatten content-part arrays first\n7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key\n8. Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026\n9. vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model\n\n## Questions\n\n### Which is better for AI agents, Ornith-1.5-122B or Recursive Flint?\n\nOrnith-1.5-122B and Recursive Flint score within a point of each other on agent readiness, 61 (C) and 61 (C). Recursive Flint leads on reach, risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.json, and with the fewest tokens: https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"ornith-1-5-122b\", \"b\": \"recursive-flint\"}`. From a terminal: `anchor compare ornith-1-5-122b recursive-flint`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json and https://www.anchorterminal.com/api/v1/tools/recursive-flint.json\n\n## Other comparisons with Ornith-1.5-122B or Recursive Flint\n\n- [Agents-A1-4B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-122b.md)\n- [Agents-A1-4B vs Recursive Flint](https://www.anchorterminal.com/compare/agents-a1-4b-vs-recursive-flint.md)\n- [Apertus v1.5 8B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-122b.md)\n- [Apertus v1.5 8B vs Recursive Flint](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-recursive-flint.md)\n- [Apodex-1.1-mini vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-122b.md)\n- [Apodex-1.1-mini vs Recursive Flint](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-recursive-flint.md)\n- [Arete-27B 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Flint](https://www.anchorterminal.com/compare/kolibri-1-vs-recursive-flint.md)\n- [Kronos vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-122b.md)\n- [Kronos vs Recursive Flint](https://www.anchorterminal.com/compare/kronos-vs-recursive-flint.md)\n- [Laguna M.1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-122b.md)\n- [Laguna M.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-m-1-vs-recursive-flint.md)\n- [Laguna S 2.1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-ornith-1-5-122b.md)\n- [Laguna S 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-recursive-flint.md)\n- [Laguna XS 2.1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-ornith-1-5-122b.md)\n- [Laguna XS 2.1 vs Recursive Flint](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-recursive-flint.md)\n- [LBS-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lbs-27b-vs-ornith-1-5-122b.md)\n- [LBS-27B vs Recursive Flint](https://www.anchorterminal.com/compare/lbs-27b-vs-recursive-flint.md)\n- [LFM2.5-2.6B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-122b.md)\n- [LFM2.5-2.6B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-recursive-flint.md)\n- [LFM3-1.7B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-ornith-1-5-122b.md)\n- [LFM3-1.7B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-1-7b-vs-recursive-flint.md)\n- [LFM3-Audio-2B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-122b.md)\n- [LFM3-Audio-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-recursive-flint.md)\n- [LFM3-Omni-2B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b.md)\n- [LFM3-Omni-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint.md)\n- [LFM3-VL-2B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ornith-1-5-122b.md)\n- [LFM3-VL-2B vs Recursive Flint](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-recursive-flint.md)\n- [Ling-3.0-tiny vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-122b.md)\n- [Ling-3.0-tiny vs Recursive Flint](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-recursive-flint.md)\n- [Ling-3.1-flash-1019 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-ornith-1-5-122b.md)\n- [Ling-3.1-flash-1019 vs Recursive Flint](https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-recursive-flint.md)\n- [LongCat-2.5 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.md)\n- [LongCat-2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-122b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Recursive Flint](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-recursive-flint.md)\n- [MiMo-V2.6-Flash-RL vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-122b.md)\n- [MiMo-V2.6-Flash-RL vs Recursive Flint](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.md)\n- [MiniCPM-o 4.5 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-122b.md)\n- [MiniCPM-o 4.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-recursive-flint.md)\n- [MiniCPM-V 4.7 1B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-ornith-1-5-122b.md)\n- [MiniCPM-V 4.7 1B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-recursive-flint.md)\n- [MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-122b.md)\n- [MiniCPM-V 4.7 35B-A3B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-recursive-flint.md)\n- [MiniCPM5-2B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-122b.md)\n- [MiniCPM5-2B vs Recursive Flint](https://www.anchorterminal.com/compare/minicpm5-2b-vs-recursive-flint.md)\n- [MiniMax-M2.5 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-122b.md)\n- [MiniMax-M2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.md)\n- [MiniMax-M2.7 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-122b.md)\n- [MiniMax-M2.7 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-7-vs-recursive-flint.md)\n- [MiniMax-M3.1-Flash vs 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