{
  "data": {
    "a": {
      "slug": "glm-4-7-flash",
      "name": "GLM-4.7-Flash",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-4.7-Flash is Z.ai's open-weight mixture-of-experts reasoning model, 30B parameters with about 3B active per token, for English and Chinese, published on Hugging Face in BF16 under the MIT licence.",
      "url": "https://www.anchorterminal.com/tools/glm-4-7-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.json",
      "dns": "glm-4-7-flash.grade.anchorterminal.com",
      "repo": "https://huggingface.co/zai-org/GLM-4.7-Flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.7-Flash"
        },
        {
          "registry": "ollama",
          "name": "glm-4.7-flash"
        }
      ],
      "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 the MIT licence named on the card. Running it yourself costs the hardware, 62.5 GB for the BF16 files and 19 GB for Ollama's default build. Z.ai's own API also serves GLM-4.7-Flash, which its docs describe as completely free (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-02"
      },
      "docsUrl": "https://huggingface.co/zai-org/GLM-4.7-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-01-29",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63,
        "grade": "B",
        "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": 20,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-02"
        },
        "negative": 0,
        "verdict": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.",
        "bestFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "strengths": [
          "MIT weights with no use policy, downloadable with no account",
          "Model code in released vLLM, SGLang, Transformers and llama.cpp, and a local Ollama build",
          "Ollama's 19 GB build fits a 24 GB GPU, and BF16 fits our reference machine",
          "Z.ai documents the glm47 tool parser, OpenAI-style tool declarations and turning thinking off per request",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard"
        ],
        "weaknesses": [
          "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January",
          "No quantised build from Z.ai and no LICENSE file in the repository",
          "No safety evaluation, agent-use guidance or security contact",
          "The card leaves out intended use, limitations, context length, knowledge cut-off and training data",
          "config.json and tokenizer_config.json disagree on context length"
        ],
        "agentNotes": [
          "On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`",
          "Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build",
          "Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text",
          "Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026",
          "Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default",
          "Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench",
          "Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 63
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 80,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-02",
            "interface": "2026-11-02",
            "reach": "2026-11-02",
            "risk": "2026-11-02",
            "upkeep": "2026-11-02"
          },
          "last": "2026-11-02",
          "oldest": "2026-11-02",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run glm-4.7-flash\n# or, per Z.ai's card:\nvllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 \\\n  --enable-auto-tool-choice --served-model-name glm-4.7-flash\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-4.7-Flash-GGUF:Q4_K --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-4.7-flash\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-4-7-flash"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-5-3",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "30B",
        "active": "3B",
        "context": 202752,
        "goodFor": "Small mixture-of-experts reasoning model for English and Chinese with tool calling, a local coding and agent model.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama glm-4.7-flash:latest",
            "url": "https://ollama.com/library/glm-4.7-flash:latest",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Also the smallest Ollama build. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "ggml-org GLM-4.7-Flash-GGUF (Q4_K)",
            "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k",
            "sizeGB": 18.24,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 18.24 GB of Q4_K weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "ggml-org GLM-4.7-Flash-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 31.84,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 31.84 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "GLM-4.7-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 62.4,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-4.7-Flash --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash",
        "runBuild": "GLM-4.7-Flash",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-4",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-4-9B",
            "url": "https://huggingface.co/zai-org/glm-4-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-Chat-1M",
            "url": "https://huggingface.co/zai-org/glm-4-9b-chat-1m",
            "params": "9B"
          },
          {
            "name": "GLM-4V-9B",
            "url": "https://huggingface.co/zai-org/glm-4v-9b",
            "params": "9B"
          },
          {
            "name": "GLM-4-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-4-32B-Base-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-Base-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-4-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-9B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-9B-0414",
            "params": "9B"
          },
          {
            "name": "GLM-Z1-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-Z1-Rumination-32B-0414",
            "url": "https://huggingface.co/zai-org/GLM-Z1-Rumination-32B-0414",
            "params": "32B"
          },
          {
            "name": "GLM-4.1V-9B-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Base",
            "params": "9B"
          },
          {
            "name": "GLM-4.1V-9B-Thinking",
            "url": "https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking",
            "params": "9B"
          },
          {
            "name": "GLM-4.5-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Base",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5",
            "url": "https://huggingface.co/zai-org/GLM-4.5",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.5-Air-Base",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-Base",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.5-Air",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air",
            "params": "106B-A12B",
            "listing": "https://www.anchorterminal.com/tools/glm-4-5-air"
          },
          {
            "name": "GLM-4.5V",
            "url": "https://huggingface.co/zai-org/GLM-4.5V",
            "params": "106B-A12B"
          },
          {
            "name": "GLM-4.6",
            "url": "https://huggingface.co/zai-org/GLM-4.6",
            "params": "355B-A32B"
          },
          {
            "name": "GLM-4.6V",
            "url": "https://huggingface.co/zai-org/GLM-4.6V",
            "params": "106B"
          },
          {
            "name": "GLM-4.6V-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.6V-Flash",
            "params": "9B"
          },
          {
            "name": "GLM-4.7",
            "url": "https://huggingface.co/zai-org/GLM-4.7",
            "params": "355B-A32B"
          }
        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "JINGSHENG HENGXING TECHNOLOGY PTE.LTD (operator of Z.ai per its terms of use, and registrant of z.ai)",
        "domain": "z.ai",
        "domainRegistered": "2017-12-15",
        "domainNote": "z.ai was registered in 2017 and transferred in October 2024, so its age says little about the company's.",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Z.ai's terms of use name JINGSHENG HENGXING TECHNOLOGY PTE.LTD as the operator of Z.ai, and RDAP names the same company as the registrant of z.ai. The weights' card names no legal entity and the repository has no LICENSE file.",
          "Nothing is hosted for this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 12 commits from 19 to 29 January 2026, mostly card and benchmark updates.",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-7-flash.json",
      "live": {
        "slug": "glm-4-7-flash",
        "securityTxt": {
          "url": "https://z.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:36.154640571Z"
        },
        "domain": {
          "domain": "z.ai",
          "registered": "2017-12-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/z.ai",
          "checkedAt": "2026-11-08T15:05:18.801433245Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:24:00.649563516Z",
            "changedAt": "2026-11-10T16:24:00.649563516Z",
            "fingerprint": "7db42760a7b7"
          }
        ],
        "updatedAt": "2026-11-10T16:24:00.649563516Z"
      }
    },
    "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on 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": 8,
        "category": "upkeep",
        "leads": "ornith-1-5-122b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.",
          "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": 28,
          "b": 36
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "glm-4-7-flash",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.",
          "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": 80,
          "b": 75
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "glm-4-7-flash",
        "name": "Risk",
        "reasons": {
          "a": "File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.",
          "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": "Z.ai (Zhipu AI)",
        "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": "MIT",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-01-29",
        "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": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-4.7-Flash scores 63 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-4.7-Flash or Ornith-1.5-122B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 80 against 75",
          "Risk, 55 against 50"
        ],
        "also": null,
        "goodFor": "Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime",
        "slug": "glm-4-7-flash",
        "watchFor": "No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 36 against 28"
        ],
        "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": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json",
        "title": "Agents-A1-4B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash"
      },
      {
        "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-glm-4-7-flash.json",
        "title": "Apertus v1.5 8B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash"
      },
      {
        "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-glm-4-7-flash.json",
        "title": "Apodex-1.1-mini vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-7-flash"
      },
      {
        "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-glm-4-7-flash.json",
        "title": "Arete-27B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash"
      },
      {
        "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-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-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/carbon-x-vs-glm-4-7-flash.json",
        "title": "Carbon-X vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-7-flash"
      },
      {
        "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/cola2-vs-glm-4-7-flash.json",
        "title": "Cola2 vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwen3-5-9b"
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-qwopus3-8-27b-flash-v2"
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        "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
      },
      {
        "by": 5,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 80,
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-122b": 75,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "glm-4-7-flash",
        "glm-4-7-flash": 55,
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-122b": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "glm-4-7-flash": 85,
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-122b": 85,
        "weight": 10
      },
      {
        "by": 8,
        "edge": "ornith-1-5-122b",
        "glm-4-7-flash": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-122b": 36,
        "weight": 10
      }
    ],
    "summary": "GLM-4.7-Flash scores 63 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "glm-4-7-flash": "A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.",
      "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/glm-4-7-flash-vs-ornith-1-5-122b",
    "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.min.md"
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  "markdown": "GLM-4.7-Flash scores 63 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.7-Flash: grade B, 63/100, rank #26 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-7-flash.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### GLM-4.7-Flash (B)\n\nGood for: Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime\n\nAhead on:\n- Reach, 80 against 75\n- Risk, 55 against 50\n\nWatch for: No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January\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- Documentation and upkeep, 36 against 28\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### Documentation and upkeep: Ornith-1.5-122B +8\n\n- GLM-4.7-Flash (28): The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.\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### Reach: GLM-4.7-Flash +5\n\n- GLM-4.7-Flash (80): Memory earns 25, since Ollama's smallest build is the 19 GB q4_K_M one, about 22.8 GB with the fifth added, which fits 24 GB. Runtimes reach the cap of 25, with model code in released vLLM, SGLang and Transformers, a converter in a released llama.cpp build and a local entry in Ollama's library. Builds earn nothing, because Z.ai publishes only BF16 and every quantised build is someone else's. The MIT licence with nothing added gives the full 15 for terms, the files download with no account or gate for 10, and Z.ai plus three other providers serve it for 5.\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### Risk: GLM-4.7-Flash +5\n\n- GLM-4.7-Flash (55): File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file and no advisories found.\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 | GLM-4.7-Flash | Ornith-1.5-122B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 75 | GLM-4.7-Flash +5 |\n| Risk | 20% (20 this run) | 55 | 50 | GLM-4.7-Flash +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 28 | 36 | Ornith-1.5-122B +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.7-Flash | Ornith-1.5-122B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | 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 | MIT | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-01-29 | 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 | 2.8/5 (4) | none |\n\n## Verdicts\n\n**GLM-4.7-Flash.** A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.\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### GLM-4.7-Flash\n\n1. On our reference machine start it with `vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash`\n2. Or run `ollama run glm-4.7-flash` for the 19 GB q4_K_M build\n3. Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text\n4. Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026\n5. Pass `chat_template_kwargs: {\"enable_thinking\": false}` for short tasks, since thinking is on by default\n6. Set `clear_thinking: false` to keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench\n7. Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000\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, GLM-4.7-Flash or Ornith-1.5-122B?\n\nGLM-4.7-Flash scores 63 (B) on agent readiness against Ornith-1.5-122B's 61 (C), and leads in 2 of 4 scored categories. Ornith-1.5-122B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-122b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-7-flash\", \"b\": \"ornith-1-5-122b\"}`. 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