{
  "data": {
    "a": {
      "slug": "glm-4-5-air",
      "name": "GLM-4.5-Air",
      "vendor": "Z.ai (Zhipu AI)",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-4.5-Air is Z.ai's open-weight mixture-of-experts model for English and Chinese, 106B total and 12B active parameters, with thinking that turns off per request, tool calling and a 128K context, published in BF16 and FP8 under MIT.",
      "url": "https://www.anchorterminal.com/tools/glm-4-5-air",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json",
      "repo": "https://huggingface.co/zai-org/GLM-4.5-Air",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.5-Air"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-4.5-Air-FP8"
        }
      ],
      "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 MIT licence named on the card. Running it yourself costs the hardware, 221.0 GB for the BF16 files and 112.6 GB for Z.ai's FP8 build. Z.ai's own API also serves GLM-4.5-Air (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://github.com/zai-org/GLM-4.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "chinese",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2025-08-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.1,
        "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": 51,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 85,
          "reach": 65,
          "risk": 65,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.",
        "bestFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.",
        "strengths": [
          "Released support in llama.cpp, vLLM, SGLang and Transformers",
          "Plain MIT with no gate, no account and no use policy",
          "Safetensors only, with a checksum on every shard in the publisher's own organisation",
          "Z.ai documents the glm45 tool and reasoning parsers, and thinking turns off per request",
          "Hosted by Z.ai and Novita for agents without the hardware"
        ],
        "weaknesses": [
          "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own",
          "No limitations, architecture, context or knowledge cut-off on the card, and no training cut-off anywhere",
          "No agent guidance or security contact",
          "No reply from Z.ai in its discussions since 30 July 2025",
          "Structured output isn't documented for the weights"
        ],
        "agentNotes": [
          "Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context",
          "Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7",
          "Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block",
          "Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON",
          "Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072",
          "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.1
          }
        ],
        "editorialScores": {
          "capability": 50,
          "interface": 85,
          "reach": 65,
          "risk": 65,
          "upkeep": 38
        },
        "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": "# per Z.ai's FP8 card (4 H100 for the full 128K context):\npython3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 \\\n  --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"glm-4.5-air\",\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-4-5-air"
      },
      "sameCompany": [
        "glm-4-7-flash",
        "glm-5-3",
        "glm-5-3-flash"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "106B",
        "active": "12B",
        "context": 131072,
        "goodFor": "Mid-size mixture-of-experts reasoning model for English and Chinese agent, coding and tool-calling work, with thinking that turns off per request.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "GLM-4.5-Air-FP8",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 112.6,
            "memoryGB": 192,
            "format": "safetensors",
            "note": "112.6 GB × 1.2 = 135.1 GB, more than the reference machine's 107.4 GB GPU share"
          },
          {
            "name": "GLM-4.5-Air",
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 221,
            "memoryGB": 512,
            "format": "safetensors"
          }
        ],
        "run": "python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --port 8000",
        "runBuild": "GLM-4.5-Air-FP8",
        "checked": "2026-10-21",
        "minMemoryGB": 192
      },
      "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-4.5V",
            "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"
          },
          {
            "name": "GLM-4.7-Flash",
            "url": "https://huggingface.co/zai-org/GLM-4.7-Flash",
            "params": "30B-A3B",
            "listing": "https://www.anchorterminal.com/tools/glm-4-7-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, per the first batch's check on 21 October 2026)",
        "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-4.5-Air/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "Z.ai's GitHub README links the zai-org organisation on Hugging Face for every GLM-4.5 checkpoint. Hugging Face hasn't verified the organisation.",
          "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, 10 commits from 20 July to 11 August 2025, the upload and card updates.",
          "z.ai/.well-known/security.txt, docs.z.ai/.well-known/security.txt and www.zhipuai.cn/.well-known/security.txt answered 404 on 21 October 2026, and bigmodel.cn answers that path with its web app."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.json"
    },
    "answer": "Ornith-1.5-9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep.",
    "b": {
      "slug": "ornith-1-5-9b",
      "name": "Ornith-1.5-9B",
      "vendor": "Ornith",
      "vendorUrl": "https://ornith.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.",
      "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json",
      "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "ornith-ai/Ornith-1.5-9B-NVFP4"
        },
        {
          "registry": "ollama",
          "name": "ornith-1.5:9b"
        }
      ],
      "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 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 19 GB for the BF16 files or 5.8 GB for Ornith's Q4_K_M GGUF (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "coding",
        "mit",
        "safetensors",
        "gguf",
        "mlx",
        "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": 37,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 95,
          "risk": 50,
          "upkeep": 31
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.",
        "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "strengths": [
          "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build",
          "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 Qwen3.5 and Gemma 4 work it builds on",
          "No safety evaluation, agent-use guidance or security contact",
          "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note",
          "Ornith answered none of the 15 discussions",
          "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented"
        ],
        "agentNotes": [
          "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file",
          "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 with presence_penalty 1.5 for general tasks, per the card",
          "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-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": 95,
          "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-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --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-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/ornith-1-5-9b"
      },
      "sameCompany": [
        "ornith-1-5-35b-a3b",
        "ornith-1-5-397b"
      ],
      "weights": {
        "trainedBy": "Ornith",
        "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture",
        "params": "9B",
        "context": 262144,
        "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Ornith-1.5-9B-Q5_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf",
            "who": "publisher",
            "precision": "q5_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q4_K_M",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 5.8,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input"
          },
          {
            "name": "Ornith-1.5-9B-MLX-4bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit",
            "who": "publisher",
            "precision": "4bit",
            "sizeGB": 5,
            "memoryGB": 8,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "ollama ornith-1.5:9b",
            "url": "https://ollama.com/library/ornith-1.5:9b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M with a BF16 vision projector, 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; the Linux build is the llama.cpp variant"
          },
          {
            "name": "Ornith-1.5-9B-Q8_0",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 9.8,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-Q6_K",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 7.6,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-MLX-8bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit",
            "who": "publisher",
            "precision": "8bit",
            "sizeGB": 9.5,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-MLX-6bit",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit",
            "who": "publisher",
            "precision": "6bit",
            "sizeGB": 7.3,
            "memoryGB": 12,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          },
          {
            "name": "Ornith-1.5-9B-NVFP4",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 8.8,
            "memoryGB": 12,
            "format": "safetensors",
            "note": "NVFP4 needs NVIDIA Blackwell"
          },
          {
            "name": "Ornith-1.5-9B",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 19.3,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          },
          {
            "name": "Ornith-1.5-9B-BF16",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
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          },
          {
            "name": "Ornith-1.5-9B-MLX",
            "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 17.9,
            "memoryGB": 24,
            "format": "mlx",
            "note": "MLX runs on Apple silicon only"
          }
        ],
        "run": "ollama run ornith-1.5:9b",
        "runBuild": "ollama ornith-1.5:9b",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "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-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"
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          {
            "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 blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai isn't verified.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.",
          "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads 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, since RDAP answered 429 on 21 October 2026."
        ],
        "score": 13
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.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": "MIT",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-08-11",
        "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": "Ornith-1.5-9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-4.5-Air or Ornith-1.5-9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 50",
          "Documentation and upkeep, 38 against 31"
        ],
        "also": [
          "Rated higher by the reviewer agents, 2.8 against 2.3 out of 5"
        ],
        "goodFor": "Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.",
        "slug": "glm-4-5-air",
        "watchFor": "Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own"
      },
      {
        "aheadOn": [
          "Reach, 95 against 65"
        ],
        "also": null,
        "goodFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required",
        "slug": "ornith-1-5-9b",
        "watchFor": "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json",
        "title": "Agents-A1-4B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json",
        "title": "Apertus v1.5 8B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json",
        "title": "Apertus v1.5 8B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.json",
        "title": "DeepSeek V4.1 Flash vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.json",
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 12B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.json",
        "title": "Gemma 4 12B IT vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 26B A4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.json",
        "title": "Gemma 4 26B A4B IT vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 31B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 E4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.json",
        "title": "GLM-4.5-Air vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-120b.json",
        "title": "GLM-4.5-Air vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-120b"
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        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json",
        "title": "LFM2.5-2.6B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b.json",
        "title": "Ling-3.0-tiny vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b.json",
        "title": "MiniCPM-o 4.5 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json",
        "title": "MiniMax-M2.7 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json",
        "title": "Muse Glimmer 30B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json",
        "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b.json",
        "title": "occamy-1.0 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json",
        "title": "Olmo 3 7B Think vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b"
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        "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-1-5-397b-vs-ornith-1-5-9b.json",
        "title": "Ornith-1.5-397B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.json",
        "title": "Ornith-1.5-9B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json",
        "title": "Ornith-1.5-9B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.json",
        "title": "Ornith-1.5-9B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json",
        "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json",
        "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next.json",
        "title": "Ornith-1.5-9B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.json",
        "title": "Ornith-1.5-9B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json",
        "title": "Ornith-1.5-9B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json",
        "title": "Ornith-1.5-9B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json",
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        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b"
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    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 30,
        "edge": "ornith-1-5-9b",
        "glm-4-5-air": 65,
        "key": "reach",
        "name": "Reach",
        "ornith-1-5-9b": 95,
        "weight": 25
      },
      {
        "by": 15,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "key": "risk",
        "name": "Risk",
        "ornith-1-5-9b": 50,
        "weight": 20
      },
      {
        "by": 0,
        "edge": "",
        "glm-4-5-air": 85,
        "key": "interface",
        "name": "Agent interface",
        "ornith-1-5-9b": 85,
        "weight": 10
      },
      {
        "by": 7,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 38,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "ornith-1-5-9b": 31,
        "weight": 10
      }
    ],
    "summary": "Ornith-1.5-9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "glm-4-5-air": "An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.",
      "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b",
    "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.min.md"
  },
  "markdown": "Ornith-1.5-9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.5-Air: grade C, 59.1/100, rank #62 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-4-5-air.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json\n- Ornith-1.5-9B: grade C, 61/100, rank #44 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.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-4.5-Air (C)\n\nGood for: Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.\n\nAhead on:\n- Risk, 65 against 50\n- Documentation and upkeep, 38 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 FP8 at about 135 GB in use, with no 4-bit build of its own\n\n### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 65\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\n\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | Ornith-1.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 95 | Ornith-1.5-9B +30 |\n| Risk | 20% (20 this run) | 65 | 50 | GLM-4.5-Air +15 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 38 | 31 | GLM-4.5-Air +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.1 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | Ornith-1.5-9B |\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 | 2025-08-11 | 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-4.5-Air.** An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.\n\n**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\n\n## Before you call either\n\n### GLM-4.5-Air\n\n1. Z.ai's FP8 recipe is `python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45`, and 2 H100s are enough below the full 128K context\n2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7\n3. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, which makes the template add /nothink and an empty think block\n4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON\n5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072\n6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented for the weights\n\n### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\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 coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-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-4.5-Air or Ornith-1.5-9B?\n\nOrnith-1.5-9B scores 61 (C) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 1 of 4 scored categories. GLM-4.5-Air leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-ornith-1-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"ornith-1-5-9b\"}`. From a terminal: `anchor compare glm-4-5-air ornith-1-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/glm-4-5-air.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json\n\n## Other comparisons with GLM-4.5-Air or Ornith-1.5-9B\n\n- [Agents-A1-4B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.md)\n- [Agents-A1-4B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.md)\n- [Apertus v1.5 8B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.md)\n- [Apertus v1.5 8B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.md)\n- [Apodex-1.1-mini vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.md)\n- [Apodex-1.1-mini vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.md)\n- [BigBang-v1 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.md)\n- [BigBang-v1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.md)\n- [Cosmos-Reason2-2B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.md)\n- [Cosmos-Reason2-2B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.md)\n- [Darwin-36B-Opus vs GLM-4.5-Air](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.md)\n- [Darwin-36B-Opus vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.md)\n- [DeepSeek-V3.2 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.md)\n- [DeepSeek-V3.2 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek-V4-Flash-0731 vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.md)\n- [DeepSeek-V4-Flash-0731 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-ornith-1-5-9b.md)\n- [DiffusionGemma 26B A4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.md)\n- [DiffusionGemma 26B A4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 12B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-4-5-air.md)\n- [Gemma 4 12B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 26B A4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-4-5-air.md)\n- [Gemma 4 26B A4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 31B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-4-5-air.md)\n- [Gemma 4 31B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E2B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.md)\n- [Gemma 4 E2B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-ornith-1-5-9b.md)\n- [Gemma 4 E4B IT vs GLM-4.5-Air](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-4-5-air.md)\n- [Gemma 4 E4B IT vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-ornith-1-5-9b.md)\n- [GLM-4.5-Air vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-4-7-flash.md)\n- [GLM-4.5-Air vs GLM-5.3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3.md)\n- [GLM-4.5-Air vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md)\n- [GLM-4.5-Air vs gpt-oss-120b](https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-120b.md)\n- [GLM-4.5-Air vs gpt-oss-20b](https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b.md)\n- [GLM-4.5-Air vs Granite 4.2 3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b.md)\n- [GLM-4.5-Air vs Hy3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.md)\n- [GLM-4.5-Air vs Inkling](https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling.md)\n- [GLM-4.5-Air vs Inkling-Small](https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small.md)\n- [GLM-4.5-Air vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [GLM-4.5-Air vs Kimi K3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.md)\n- [GLM-4.5-Air vs Kolibri-1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.md)\n- [GLM-4.5-Air vs Laguna M.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1.md)\n- [GLM-4.5-Air vs Laguna S 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.md)\n- [GLM-4.5-Air vs Laguna XS 2.1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.md)\n- [GLM-4.5-Air vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.md)\n- [GLM-4.5-Air vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.md)\n- [GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.md)\n- [GLM-4.5-Air vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.md)\n- [GLM-4.5-Air vs MiniCPM5-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm5-2b.md)\n- [GLM-4.5-Air vs MiniMax-M2.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.md)\n- [GLM-4.5-Air vs MiniMax-M2.7](https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.md)\n- [GLM-4.5-Air vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.md)\n- [GLM-4.5-Air vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [GLM-4.5-Air vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-30b-a3b.md)\n- [GLM-4.5-Air vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-4b.md)\n- [GLM-4.5-Air vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [GLM-4.5-Air vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-nemotron-3-super-120b-a12b.md)\n- [GLM-4.5-Air vs Nemotron 3 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Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-122b-a10b.md)\n- [GLM-4.5-Air vs Qwen3.5-2B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-2b.md)\n- [GLM-4.5-Air vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-397b-a17b.md)\n- [GLM-4.5-Air vs Qwen3.5-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-4b.md)\n- [GLM-4.5-Air vs Qwen3.5-9B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-5-9b.md)\n- [GLM-4.5-Air vs Qwen3.6-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-27b.md)\n- [GLM-4.5-Air vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-6-35b-a3b.md)\n- [GLM-4.5-Air vs Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-8-27b.md)\n- [GLM-4.5-Air vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-8-flash-next.md)\n- [GLM-4.5-Air vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-30b-a3b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-coder-next.md)\n- [GLM-4.5-Air vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-omni-30b-a3b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-2b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-30b-a3b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-32b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-4b-instruct.md)\n- [GLM-4.5-Air vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwen3-vl-8b-instruct.md)\n- [GLM-4.5-Air vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/glm-4-5-air-vs-qwopus3-8-27b-flash-v2.md)\n- [GLM-4.5-Air vs SmolLM3-3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-smollm3-3b.md)\n- [GLM-4.5-Air vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b.md)\n- [GLM-4.5-Air vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-27b.md)\n- [GLM-4.5-Air vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/glm-4-5-air-vs-swift-1-5-qwen3-8-flash-next.md)\n- [GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.7-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3-Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.md)\n- [GLM-5.3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b.md)\n- [gpt-oss-120b vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b.md)\n- [gpt-oss-20b vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-ornith-1-5-9b.md)\n- [Granite 4.2 3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-ornith-1-5-9b.md)\n- [Hy3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.md)\n- [Inkling-Small vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-small-vs-ornith-1-5-9b.md)\n- [Inkling vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/inkling-vs-ornith-1-5-9b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-ornith-1-5-9b.md)\n- [Kimi K3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kimi-k3-vs-ornith-1-5-9b.md)\n- [Kolibri-1 vs 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