{
  "data": {
    "a": {
      "slug": "bigbang-v1",
      "name": "BigBang-v1",
      "vendor": "The Endless Frontier (endless-frontier)",
      "vendorUrl": "https://huggingface.co/endless-frontier",
      "kind": "weights",
      "category": "open-weights",
      "summary": "BigBang-v1 is The Endless Frontier's general-purpose fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active), post-trained on about 10,000 synthetic research and technical problems, published on Hugging Face in BF16 under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/bigbang-v1",
      "markdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/bigbang-v1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json",
      "dns": "bigbang-v1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/endless-frontier/BigBang-v1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "endless-frontier/BigBang-v1"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 71.9 GB for the BF16 files, the only build from the publisher (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/endless-frontier/BigBang-v1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-08-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 56.3,
        "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": 87,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 60,
          "risk": 55,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.",
        "bestFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.",
        "strengths": [
          "Apache 2.0 like its base, not gated",
          "Same architecture, config and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama 0.40.14, vLLM, SGLang and Transformers run it",
          "Safetensors only with a SHA-256 for every weight file",
          "262,144 tokens of context and several tool calls a turn"
        ],
        "weaknesses": [
          "BF16 is the only build from the publisher, needing about 86 GB",
          "No report, safety results, agent guidance or security contact",
          "Training data described only as about 10,000 synthetic examples",
          "No reply from the publisher to any of 7 discussions, and no change to the files or card since 6 August 2026",
          "Every benchmark figure is the publisher's own, with no set-up stated"
        ],
        "agentNotes": [
          "On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`",
          "The card says to keep at least 128K of context so its thinking isn't cut short",
          "Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output",
          "For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only",
          "Validate JSON output yourself or use the runtime's guided decoding"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 56.3
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 60,
          "risk": 55,
          "upkeep": 46
        },
        "provenanceScore": 19,
        "checked": {
          "categories": {
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "vllm serve endless-frontier/BigBang-v1 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/endless-frontier_BigBang-v1-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"endless-frontier/BigBang-v1\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/bigbang-v1"
      },
      "weights": {
        "trainedBy": "The Endless Frontier (endless-frontier)",
        "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text. Identical config.json, parameter count and chat template, and the repository's LICENSE is Qwen's Apache 2.0 file. BigBang is also Apache 2.0",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "General-purpose mixture-of-experts model tuned for scientific research, search and coding tasks with thinking on, text and images in.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 22.76,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Endless Frontier's. 21.86 GB of Q4_K_M weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "bartowski endless-frontier_BigBang-v1-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/endless-frontier_BigBang-v1-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 38.71,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Endless Frontier's. 37.81 GB of Q8_0 weights plus the 0.90 GB F16 projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "BigBang-v1",
            "url": "https://huggingface.co/endless-frontier/BigBang-v1",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "BigBang-v1",
        "checked": "2026-11-04",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "The Endless Frontier (endless-frontier)",
        "family": "BigBang",
        "url": "https://www.anchorterminal.com/open-weights/#endless-frontier",
        "olderCount": 2,
        "unrestrictedCopies": 3
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity, website, paper or GitHub repository is named on the card. The Hugging Face organisation endless-frontier (fullname The Endless Frontier, 17 members, three models) isn't verified.",
          "Nothing is hosted by the publisher, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The commit messages are uploads and benchmark-table fixes.",
          "No security.txt, since there is no domain."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/bigbang-v1.json"
    },
    "answer": "Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.",
    "b": {
      "slug": "ornith-1-5-122b",
      "name": "Ornith-1.5-122B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-122B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 125B parameters on Qwen3.5's architecture with a 262,144-token context, published in BF16, FP8, NVFP4, GGUF and MLX and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-122b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json",
      "dns": "ornith-1-5-122b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-122B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:122b-q4_K_M"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's six build repositories for this size are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 250 GB for the BF16 files, 69.7 GB for Ornith's MLX 4-bit build or 75.8 GB for its Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-24",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 64,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.",
        "bestFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "4-bit builds from Ornith itself in GGUF, MLX and NVFP4, at 70 to 78 GB, plus Ollama's Q4_K_M",
          "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions",
          "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example",
          "262,144 tokens of native context",
          "Safetensors and GGUF only, no gate and no code in the repository"
        ],
        "weaknesses": [
          "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The card says a single 80 GB GPU serves it, which its 250 GB of BF16 files don't fit, and names a Mobile variant that isn't published",
          "Ornith answered none of the 5 discussions, and the MLX builds were uploaded again with no note",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`",
          "Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model",
          "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed",
          "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it",
          "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends",
          "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 75,
          "risk": 50,
          "upkeep": 36
        },
        "provenanceScore": 19,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run ornith-1.5:122b-q4_K_M\n# or, with llama.cpp and Ornith's own GGUF:\nllama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144\n# or, with the card's vLLM command (abridged):\nvllm serve ornith-ai/Ornith-1.5-122B --served-model-name Ornith-1.5-122B --max-model-len 262144 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"ornith-1.5:122b-q4_K_M\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-122b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-122B-A10B, inferred. Not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-122B-A10B (Apache 2.0); the card says Ornith-1.0 was built on Qwen3.5 and Gemma 4 and that 1.5 extends it",
        "params": "125B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, the largest Ornith that fits a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-122B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 77.7,
            "memoryGB": 96,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-122B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 75.8,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-122B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 69.7,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:122b-q4_K_M",
            "url": "https://ollama.com/library/ornith-1.5:122b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 77,
            "memoryGB": 96,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (76 GB) with a BF16 vision projector (912 MB), as Ollama's tag page shows it, rounded to whole GB. The 73 GB ornith-1.5:122b tag is marked for MLX and llama.cpp and its quantisation wasn't established, so it isn't listed as a build"
          },
          {
            "name": "Ornith-1.5-122B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 102.4,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 88.7,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-122B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 133,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 132.6,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 130.7,
            "memoryGB": 192,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 250.2,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-GGUF/blob/main/Ornith-1.5-122B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 249.4,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Too big for the 128 GB reference machine"
          },
          {
            "name": "Ornith-1.5-122B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 245.2,
            "memoryGB": 512,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:122b-q4_K_M",
        "runBuild": "ollama ornith-1.5:122b-q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Ornith",
        "family": "Ornith-1",
        "url": "https://www.anchorterminal.com/open-weights/#ornith",
        "others": [
          {
            "name": "Ornith-1.0-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B",
            "params": "9B (dense)"
          },
          {
            "name": "Ornith-1.0-35B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B",
            "params": "35B (MoE)"
          },
          {
            "name": "Ornith-1.0-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B",
            "params": "397B (MoE)"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "params": "9B (dense)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b"
          },
          {
            "name": "Ornith-1.5-9B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "params": "35B total, 3B active (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b"
          },
          {
            "name": "Ornith-1.5-35B-A3B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-397B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B",
            "params": "397B (MoE)",
            "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b"
          },
          {
            "name": "Ornith-1.5-397B-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash",
            "params": "1.29B (Hub safetensors count)"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team. Ollama's copy of the licence names deepreinforce.ai as the copyright holder.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. The commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 19
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-122b.json"
    },
    "edges": [
      {
        "by": 25,
        "category": "interface",
        "leads": "ornith-1-5-122b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "b": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 60,
          "b": 85
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "ornith-1-5-122b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.",
          "b": "Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75."
        },
        "scores": {
          "a": 60,
          "b": 75
        }
      },
      {
        "by": 10,
        "category": "upkeep",
        "leads": "bigbang-v1",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.",
          "b": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36."
        },
        "scores": {
          "a": 46,
          "b": 36
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "bigbang-v1",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.",
          "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact."
        },
        "scores": {
          "a": 55,
          "b": 50
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "The Endless Frontier (endless-frontier)",
        "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": "2026-08-06",
        "b": "2026-10-24",
        "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": "2.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, BigBang-v1 or Ornith-1.5-122B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 55 against 50",
          "Documentation and upkeep, 46 against 36"
        ],
        "also": null,
        "goodFor": "Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.",
        "slug": "bigbang-v1",
        "watchFor": "BF16 is the only build from the publisher, needing about 86 GB"
      },
      {
        "aheadOn": [
          "Reach, 75 against 60",
          "Agent interface, 85 against 60"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a 128 GB unified-memory machine or a 96 GB GPU setup, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-122b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "history": {
      "bigbang-v1": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.9,
          "from": 54.4,
          "fromGrade": "C",
          "to": 56.3,
          "toGrade": "C"
        }
      ],
      "ornith-1-5-122b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json",
        "title": "Agents-A1-4B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1"
      },
      {
        "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/apertus-v1-5-8b-vs-bigbang-v1.json",
        "title": "Apertus v1.5 8B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1"
      },
      {
        "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/apodex-1-1-mini-vs-bigbang-v1.json",
        "title": "Apodex-1.1-mini vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-bigbang-v1"
      },
      {
        "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/arete-27b-vs-bigbang-v1.json",
        "title": "Arete-27B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1"
      },
      {
        "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/bigbang-v1-vs-carbon-x.json",
        "title": "BigBang-v1 vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2.json",
        "title": "BigBang-v1 vs Cola2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b.json",
        "title": "BigBang-v1 vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus.json",
        "title": "BigBang-v1 vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2.json",
        "title": "BigBang-v1 vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.json",
        "title": "BigBang-v1 vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731.json",
        "title": "BigBang-v1 vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2.json",
        "title": "BigBang-v1 vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it.json",
        "title": "BigBang-v1 vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b.json",
        "title": "BigBang-v1 vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it.json",
        "title": "BigBang-v1 vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it.json",
        "title": "BigBang-v1 vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it.json",
        "title": "BigBang-v1 vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it.json",
        "title": "BigBang-v1 vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.json",
        "title": "BigBang-v1 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash.json",
        "title": "BigBang-v1 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3.json",
        "title": "BigBang-v1 vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json",
        "title": "BigBang-v1 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b.json",
        "title": "BigBang-v1 vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b.json",
        "title": "BigBang-v1 vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b.json",
        "title": "BigBang-v1 vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b.json",
        "title": "BigBang-v1 vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b.json",
        "title": "BigBang-v1 vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-granite-4-2-8b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-122b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b.json",
        "title": "North Mini Code 1.0 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b.json",
        "title": "occamy-1.0 vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b.json",
        "title": "Ornith-1.5-122B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b.json",
        "title": "Ornith-1.5-122B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-122B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it.json",
        "title": "Ornith-1.5-122B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Ornith-1.5-122B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-122B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-122B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-122B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-122B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-122B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-122B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-122B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-122B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-122B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "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-recursive-flint.json",
        "title": "Ornith-1.5-122B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-recursive-flint"
      },
      {
        "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"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "bigbang-v1": 60,
        "by": 15,
        "edge": "ornith-1-5-122b",
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-122b": 75,
        "weight": 25
      },
      {
        "bigbang-v1": 55,
        "by": 5,
        "edge": "bigbang-v1",
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-122b": 50,
        "weight": 20
      },
      {
        "bigbang-v1": 60,
        "by": 25,
        "edge": "ornith-1-5-122b",
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-122b": 85,
        "weight": 10
      },
      {
        "bigbang-v1": 46,
        "by": 10,
        "edge": "bigbang-v1",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-122b": 36,
        "weight": 10
      }
    ],
    "summary": "Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "bigbang-v1": "A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.",
      "ornith-1-5-122b": "The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b",
    "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.min.md"
  },
  "markdown": "Ornith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- BigBang-v1: grade C, 56.3/100, rank #116 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/bigbang-v1.md · JSON https://www.anchorterminal.com/api/v1/tools/bigbang-v1.json\n- Ornith-1.5-122B: grade C, 61/100, rank #81 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-122b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-122b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### BigBang-v1 (C)\n\nGood for: Agents with 96 GB or more that want an Apache 2.0 mixture-of-experts model tuned for research, search and coding, and can do without publisher support.\n\nAhead on:\n- Risk, 55 against 50\n- Documentation and upkeep, 46 against 36\n\nWatch for: BF16 is the only build from the publisher, needing about 86 GB\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\nAhead on:\n- Reach, 75 against 60\n- Agent interface, 85 against 60\n\nWatch for: No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Agent interface: Ornith-1.5-122B +25\n\n- BigBang-v1 (60): Chat template 20, the base's template in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser and nothing more. Trained tool calling 20, tuned for tool use with several calls a turn. Structured output 0. Reasoning control 0, the card doesn't say how to turn thinking off or budget it. Context 10, 262,144 tokens. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- Ornith-1.5-122B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template reads enable_thinking per request and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n\n### Reach: Ornith-1.5-122B +15\n\n- BigBang-v1 (60): Memory 10 of 30, the only build from the publisher is BF16 at 71.9 GB, 86.3 GB with a fifth added, which fits the reference machine but not 64 GB. Runtimes 25, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama 0.40.14 runs through the llama.cpp build it pins. Builds 0, no quantised build from the publisher. Terms 15, Apache 2.0 like its base. Download 10, not gated. Hosted 0, one inference provider, Featherless AI, where the line needs two. 10 + 25 + 0 + 15 + 10 + 0 = 60.\n- Ornith-1.5-122B (75): Memory 10, since Ornith's own MLX 4-bit build is 69.7 GB and needs about 83.6 GB with the fifth added, over 64 GB and inside the reference machine's 107.4 GB, as do its Q4_K_M GGUF, its NVFP4 build and Ollama's build. Runtimes 25, the most the line gives, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2, llama.cpp's qwen35moe architecture, Ollama's library and LM Studio's catalogue for the qwen35moe architecture. Builds 15, Ornith publishes FP8, NVFP4, GGUF and MLX at several precisions. Terms 15, MIT as named in the card's metadata, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and no inference provider serves it. 10 + 25 + 15 + 15 + 10 + 0 = 75.\n\n### Documentation and upkeep: BigBang-v1 +10\n\n- BigBang-v1 (46): Model card 11 of 20, limitations, architecture and a cut-off are missing. Training data 5, a general description of about 10,000 synthetic examples. Last change 15, the card last changed on 6 August 2026, 90 days before this check. Responsiveness 0, no publisher reply to any of 7 questions. Version policy 5, a first release. File health 10, the files agree with the card and the base. 11 + 5 + 15 + 0 + 5 + 10 = 46.\n- Ornith-1.5-122B (36): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 20, a folder upload to the MLX builds on 24 October 2026, 17 days ago. Responsiveness 0, Ornith answered none of the 5 questions and reports. Version policy 0, the MLX builds were uploaded a second time four days after the first with no note. File health 0, the card links a missing LICENSE, states hardware its own files don't fit and names a variant that doesn't exist, and the two chat templates differ. 11 + 5 + 20 + 0 + 0 + 0 = 36.\n\n### Risk: BigBang-v1 +5\n\n- BigBang-v1 (55): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the publisher's own organisation with a SHA-256 for every weight file. Safety evaluation 0. Agent guidance 0. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no website or security contact. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- Ornith-1.5-122B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n## Score by category\n\n| Category | Weight | BigBang-v1 | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 75 | Ornith-1.5-122B +15 |\n| Risk | 20% (20 this run) | 55 | 50 | BigBang-v1 +5 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Ornith-1.5-122B +25 |\n| Documentation and upkeep | 10% (10 this run) | 46 | 36 | BigBang-v1 +10 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **56.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | BigBang-v1 | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | The Endless Frontier (endless-frontier) | 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 | 2026-08-06 | 2026-10-24 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.3/5 (4) | none |\n\n## How the scores have moved\n\n### BigBang-v1\n\n- +1.9 on 4 November 2026: correction\n\n### Ornith-1.5-122B\n\nNo score change since it was listed.\n\n## Verdicts\n\n**BigBang-v1.** A Qwen3.6-35B-A3B fine-tuned on synthetic research problems, under plain Apache 2.0, for search, science and coding work. The publisher ships BF16 only, about 86 GB to run, and has no report, safety results or replies to questions, so smaller builds and support come from others.\n\n**Ornith-1.5-122B.** The largest Ornith that fits a 128 GB machine, a coding and terminal-agent cousin on Qwen3.5-122B-A10B's architecture with XML tool calls and 262,144 tokens of context. Its 4-bit builds need 84 to 93 GB. The main risk is an absent publisher, with no licence file, safety results or replies, and a card that misstates the hardware.\n\n## Before you call either\n\n### BigBang-v1\n\n1. On the reference machine use `vllm serve endless-frontier/BigBang-v1 --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`\n2. The card says to keep at least 128K of context so its thinking isn't cut short\n3. Thinking control isn't documented, try the base template's `enable_thinking` false in chat_template_kwargs and check the output\n4. For Ollama or llama.cpp you'll need someone else's GGUF; the publisher ships BF16 safetensors only\n5. Validate JSON output yourself or use the runtime's guided decoding\n\n### Ornith-1.5-122B\n\n1. Start it with `ollama run ornith-1.5:122b-q4_K_M`, or `llama-server -hf ornith-ai/Ornith-1.5-122B-GGUF:Q4_K_M --jinja -c 262144`\n2. Budget about 92 GB for the Q4_K_M GGUF with a working context, and less context if the machine has under 100 GB for the model\n3. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n4. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n5. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n6. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 for general tasks, as the card recommends\n7. Add mmproj-Ornith-1.5-122B-BF16.gguf (0.9 GB) for image input in llama.cpp, and validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, BigBang-v1 or Ornith-1.5-122B?\n\nOrnith-1.5-122B scores 61 (C) on agent readiness against BigBang-v1's 56.3 (C), and leads in 2 of 4 scored categories. BigBang-v1 leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"bigbang-v1\", \"b\": \"ornith-1-5-122b\"}`. 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