{
  "data": {
    "a": {
      "slug": "glm-5-3",
      "name": "GLM-5.3",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-5.3 is Z.ai's open-weight mixture-of-experts model for coding and long-horizon agent work, 744B parameters with 40B active and a 1M-token context, published on Hugging Face in FP8 and BF16 under its own licence.",
      "url": "https://www.anchorterminal.com/tools/glm-5-3",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3.json",
      "repo": "https://huggingface.co/zai-org/GLM-5.3",
      "license": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-BF16"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the GLM-5.3 License. A licensee running a model-as-a-service business with more than US$10 billion of revenue in 12 months must pass Z.AI's security review before commercial use. Running it yourself costs the hardware, 755.7 GB of FP8 files (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/zai-org/GLM-5.3",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "long-context",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-10-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 64.6,
        "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": 1197,
        "categoryRank": 14,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 80,
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.",
        "bestFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider",
        "strengths": [
          "Model code in released llama.cpp b11768, vLLM 0.28.0, SGLang 0.5.22 and Transformers 5.12.0",
          "1,048,576 tokens of native context and three reasoning budgets per request",
          "Template renders several tool calls per turn, parsed by glm47 in vLLM and SGLang",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard",
          "Downloadable with no account, and hosted by Z.ai and five live providers on Hugging Face"
        ],
        "weaknesses": [
          "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build",
          "No safety evaluation, agent-use guidance or security contact",
          "Thinking is always on, and reasoning_effort only lowers it",
          "Commercial use by model-as-a-service operators above US$10 billion revenue needs Z.AI's security review",
          "The card leaves out limitations, architecture, parameters and cut-off, and zai-org answered 2 of 16 discussions, none since 8 September"
        ],
        "agentNotes": [
          "Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)",
          "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text",
          "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max",
          "Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default",
          "Don't send `enable_thinking: false`; the template ignores it and always opens a think block",
          "Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API",
          "No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 64.6
          }
        ],
        "editorialScores": {
          "capability": 80,
          "interface": 70,
          "reach": 55,
          "risk": 50,
          "upkeep": 58
        },
        "provenanceScore": 68,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM's recipe, FP8 on one 8xH200 node\nuv pip install \"vllm\u003e=0.28.0\" --torch-backend=auto\nvllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 \\\n  --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 \\\n  --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice \\\n  --served-model-name glm-5.3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-5.3\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n    \"chat_template_kwargs\": {\"reasoning_effort\": \"low\", \"clear_thinking\": true}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-5-3"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-4-7-flash",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "builtOn": "GLM-5.2's base model (same base, new post-training)",
        "params": "744B",
        "active": "40B",
        "context": 1048576,
        "goodFor": "Large model for coding and long-horizon agent work with tool calling and always-on reasoning, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "GLM-5.3",
            "url": "https://huggingface.co/zai-org/GLM-5.3",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 755.6,
            "memoryGB": 1024,
            "format": "safetensors"
          },
          {
            "name": "GLM-5.3-BF16",
            "url": "https://huggingface.co/zai-org/GLM-5.3-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 1506.7,
            "memoryGB": 2048,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3",
        "runBuild": "GLM-5.3",
        "checked": "2026-10-21",
        "minMemoryGB": 1024
      },
      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-5",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
          {
            "name": "GLM-5",
            "url": "https://huggingface.co/zai-org/GLM-5",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.1",
            "url": "https://huggingface.co/zai-org/GLM-5.1",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.2",
            "url": "https://huggingface.co/zai-org/GLM-5.2",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.3-Flash",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash",
            "params": "320B-A18B",
            "listing": "https://www.anchorterminal.com/tools/glm-5-3-flash"
          }
        ],
        "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). The licence names 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-5.3/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' LICENSE names Z.AI as the copyright holder.",
          "Nothing is hosted for this listing, so the GLM-5.3 License stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, 11 commits from 27 August to 14 October 2026, whose messages say what changed (two chat template fixes, on 4 September and 14 October, and evaluation results).",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3.json"
    },
    "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 1 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.",
    "b": {
      "slug": "ornith-1-5-35b-a3b",
      "name": "Ornith-1.5-35B-A3B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-35B-A3B is Ornith's open-weight mixture-of-experts reasoning model for coding and agent work, about 35B parameters with 3B active per token 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-1-5-35b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:35b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all eight of Ornith's build repositories for this size are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 72 GB for the BF16 files, 19.5 GB for Ornith's MLX 4-bit build or 21.7 GB for its Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-08-23",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 36,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 90,
          "risk": 50,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.",
        "bestFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Fits in about 24 GB from Ornith's MLX 4-bit build and 32 GB from its Q4_K_M GGUF, NVFP4 or Ollama's build, with only about 3B parameters active per token",
          "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama 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",
          "A weight shard, every GGUF, an FP8 shard and the NVFP4 shards were replaced in place between 23 and 26 August 2026 with no note",
          "Ornith answered none of the 35 discussions",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it",
          "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 general tasks and temperature 1.0 to reproduce the card's benchmarks",
          "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 90,
          "risk": 50,
          "upkeep": 31
        },
        "provenanceScore": 13,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run ornith-1.5:35b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-35B-A3B --served-model-name Ornith-1.5-35B-A3B --tensor-parallel-size 2 --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-1.5:35b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-35b-a3b"
      },
      "sameCompany": [
        "ornith-1-5-397b",
        "ornith-1-5-9b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Qwen3.5-35B-A3B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-35B-A3B (Apache 2.0)",
        "params": "35B (3B active)",
        "context": 262144,
        "goodFor": "Coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, fast for its size with about 3B active parameters.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-35B-A3B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 19.5,
            "memoryGB": 24,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 23.5,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-35B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 25.3,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.7,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "ollama ornith-1.5:35b",
            "url": "https://ollama.com/library/ornith-1.5:35b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 23,
            "memoryGB": 32,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M (22 GB) with a BF16 vision projector (903 MB), linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB"
          },
          {
            "name": "Ornith-1.5-35B-A3B-FP8",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 39.4,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 37.8,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 29.2,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX-6bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX-6bit",
            "who": "publisher",
            "precision": "6bit",
            "sizeGB": 28.2,
            "memoryGB": 48,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-35B-A3B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-GGUF/blob/main/Ornith-1.5-35B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.1,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-35B-A3B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 69.3,
            "memoryGB": 96,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:35b",
        "runBuild": "ollama ornith-1.5:35b",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "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-DFlash",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash",
            "params": "0.39B (Hub safetensors count)"
          },
          {
            "name": "Ornith-1.5-122B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B",
            "params": "122B (MoE)"
          },
          {
            "name": "Ornith-1.5-397B",
            "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)"
          }
        ],
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "ornith.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity named on the card, the blog or the repository. The citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) isn't verified.",
          "The card's blog badge links deep-reinforce.com/ornith.html, which redirects to Ornith's 1.0 blog post on ornith.ai.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads, deletions and README updates.",
          "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.",
          "unchecked: the domain's registration date."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.json"
    },
    "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": "GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-14",
        "b": "2026-08-23",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "2.8/5 (4)",
        "b": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-5.3 scores 64.6 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 1 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.",
        "question": "Which is better for AI agents, GLM-5.3 or Ornith-1.5-35B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Documentation and upkeep, 58 against 31"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5"
        ],
        "goodFor": "Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider",
        "slug": "glm-5-3",
        "watchFor": "Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build"
      },
      {
        "aheadOn": [
          "Reach, 90 against 55",
          "Agent interface, 85 against 70"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-35b-a3b",
        "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"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json",
        "title": "Apertus v1.5 8B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3.json",
        "title": "Apodex-1.1-mini vs GLM-5.3",
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b.json",
        "title": "DeepSeek-V3.2 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.json",
        "title": "DeepSeek V4.1 Flash vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3.json",
        "title": "DiffusionGemma 26B A4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3.json",
        "title": "Gemma 4 12B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3.json",
        "title": "Gemma 4 E4B IT vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-35b-a3b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-glm-5-3.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-35b-a3b"
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        "title": "GLM-5.3 vs GLM-5.3-Flash",
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        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniCPM5-2B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniMax-M2.5 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b.json",
        "title": "occamy-1.0 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-35B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-35B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-35B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-35B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "ornith-1-5-35b-a3b",
        "glm-5-3": 55,
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-35b-a3b": 90,
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "glm-5-3": 50,
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-35b-a3b": 50,
        "weight": 20
      },
      {
        "by": 15,
        "edge": "ornith-1-5-35b-a3b",
        "glm-5-3": 70,
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-35b-a3b": 85,
        "weight": 10
      },
      {
        "by": 27,
        "edge": "glm-5-3",
        "glm-5-3": 58,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-35b-a3b": 31,
        "weight": 10
      }
    ],
    "summary": "GLM-5.3 scores 64.6 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 1 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "glm-5-3": "A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.",
      "ornith-1-5-35b-a3b": "A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b",
    "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.min.md"
  },
  "markdown": "GLM-5.3 scores 64.6 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 1 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface. Both do open-weight models you run yourself.\n\n- GLM-5.3: grade B, 64.6/100, rank #17 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3.json\n- Ornith-1.5-35B-A3B: grade C, 61/100, rank #43 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-35b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-5.3 (B)\n\nGood for: Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider\n\nAhead on:\n- Documentation and upkeep, 58 against 31\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 2.8 against 2.3 out of 5\n\nWatch for: Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build\n\n### Ornith-1.5-35B-A3B (C)\n\nGood for: Local coding and terminal agents on a 24 to 32 GB GPU or a Mac, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 90 against 55\n- Agent interface, 85 against 70\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## Score by category\n\n| Category | Weight | GLM-5.3 | Ornith-1.5-35B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 55 | 90 | Ornith-1.5-35B-A3B +35 |\n| Risk | 20% (20 this run) | 50 | 50 | even |\n| Agent interface | 10% (10 this run) | 70 | 85 | Ornith-1.5-35B-A3B +15 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 31 | GLM-5.3 +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.6 · B** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-5.3 | Ornith-1.5-35B-A3B |\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 | GLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Agent reviews | 2.8/5 (4) | 2.3/5 (4) |\n\n## Verdicts\n\n**GLM-5.3.** A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.\n\n**Ornith-1.5-35B-A3B.** A mid-size coding and terminal-agent cousin with about 3B active parameters, XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It runs in about 24 GB from Ornith's MLX 4-bit build or 32 GB from its GGUF. The main risk is an absent publisher, with no licence file, safety results or replies to 35 reports.\n\n## Before you call either\n\n### GLM-5.3\n\n1. Start it with `vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3` (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Pass `clear_thinking: true` in chat use, since the template keeps earlier reasoning by default\n5. Don't send `enable_thinking: false`; the template ignores it and always opens a think block\n6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API\n7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe\n\n### Ornith-1.5-35B-A3B\n\n1. Start it with `ollama run ornith-1.5:35b`, or `llama-server -hf ornith-ai/Ornith-1.5-35B-A3B-GGUF --port 8000 -c 262144` as the card gives it\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for general tasks and temperature 1.0 to reproduce the card's benchmarks\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-35B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, GLM-5.3 or Ornith-1.5-35B-A3B?\n\nGLM-5.3 scores 64.6 (B) on agent readiness against Ornith-1.5-35B-A3B's 61 (C), and leads in 1 of 4 scored categories. Ornith-1.5-35B-A3B leads on reach and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-35b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-5-3\", \"b\": \"ornith-1-5-35b-a3b\"}`. 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