{
  "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",
      "dns": "glm-4-5-air.grade.anchorterminal.com",
      "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": "2026-11-04"
      },
      "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": 60.3,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 71,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 50,
          "interface": 85,
          "reach": 70,
          "risk": 65,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. 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. Its files date from August 2025, with no Z.ai reply since July 2025.",
        "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": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 60.3
          }
        ],
        "editorialScores": {
          "capability": 50,
          "interface": 85,
          "reach": 70,
          "risk": 65,
          "upkeep": 38
        },
        "provenanceScore": 73,
        "checked": {
          "categories": {
            "capability": "2026-11-04",
            "interface": "2026-11-04",
            "reach": "2026-11-04",
            "risk": "2026-11-04",
            "upkeep": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-11-04",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# 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\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/GLM-4.5-Air-GGUF:Q4_K_M --jinja",
        "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": "ggml-org GLM-4.5-Air-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/ggml-org/GLM-4.5-Air-GGUF",
            "who": "llama.cpp",
            "precision": "q4_k_m",
            "sizeGB": 63.63,
            "memoryGB": 96,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 63.63 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "GLM-4.5-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": "ggml-org GLM-4.5-Air-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/GLM-4.5-Air-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 113.61,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 113.61 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "GLM-4.5-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": 96
      },
      "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": 73
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-4-5-air.json",
      "live": {
        "slug": "glm-4-5-air",
        "securityTxt": {
          "url": "https://z.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:36.154640571Z"
        },
        "domain": {
          "domain": "z.ai",
          "registered": "2017-12-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/z.ai",
          "checkedAt": "2026-11-08T15:05:18.801433245Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/zai-org/GLM-4.5-Air/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:23:58.528767364Z",
            "changedAt": "2026-11-10T16:23:58.528767364Z",
            "fingerprint": "ed03a7a53ba5"
          }
        ],
        "updatedAt": "2026-11-10T16:23:58.528767364Z"
      }
    },
    "answer": "GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep.",
    "b": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "edges": [
      {
        "by": 35,
        "category": "reach",
        "leads": "glm-4-5-air",
        "name": "Reach",
        "reasons": {
          "a": "Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.",
          "b": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35."
        },
        "scores": {
          "a": 70,
          "b": 35
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "glm-4-5-air",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K native.",
          "b": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75."
        },
        "scores": {
          "a": 85,
          "b": 75
        }
      },
      {
        "by": 8,
        "category": "upkeep",
        "leads": "longcat-2-5",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.",
          "b": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46."
        },
        "scores": {
          "a": 38,
          "b": 46
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "glm-4-5-air",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked.",
          "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "Meituan (LongCat)",
        "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": "2026-11-10",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep.",
        "question": "Which is better for AI agents, GLM-4.5-Air or LongCat-2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 70 against 35",
          "Risk, 65 against 60",
          "Agent interface, 85 against 75"
        ],
        "also": null,
        "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": [
          "Documentation and upkeep, 46 against 38"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      }
    ],
    "history": {
      "glm-4-5-air": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.2,
          "from": 59.1,
          "fromGrade": "C",
          "to": 60.3,
          "toGrade": "C"
        }
      ],
      "longcat-2-5": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-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-longcat-2-5.json",
        "title": "Agents-A1-4B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json",
        "title": "Apertus v1.5 8B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air.json",
        "title": "Apodex-1.1-mini vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json",
        "title": "Apodex-1.1-mini vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.json",
        "title": "Arete-27B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air.json",
        "title": "BigBang-v1 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.json",
        "title": "BigBang-v1 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air.json",
        "title": "Carbon-X vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.json",
        "title": "Carbon-X vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air.json",
        "title": "Cola2 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json",
        "title": "Cola2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.json",
        "title": "Cosmos-Reason2-2B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.json",
        "title": "Cosmos-Reason2-2B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air.json",
        "title": "Darwin-36B-Opus vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.json",
        "title": "Darwin-36B-Opus vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air.json",
        "title": "DeepSeek-V3.2 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.json",
        "title": "DeepSeek-V3.2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.json",
        "title": "DeepSeek V4.1 Flash vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air.json",
        "title": "DeepSeek-V4-Flash-0731 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.json",
        "title": "DeepSeek-V4-Flash-0731 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-4-5-air.json",
        "title": "Devstral Small 2 vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5.json",
        "title": "Devstral Small 2 vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.json",
        "title": "DiffusionGemma 26B A4B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.json",
        "title": "DiffusionGemma 26B A4B IT vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-5-air.json",
        "title": "EXAONE 4.5 33B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.json",
        "title": "EXAONE 4.5 33B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5"
      },
      {
        "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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      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-longcat-2-5"
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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-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5"
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        "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-longcat-2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-longcat-2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-longcat-2-5.json",
        "title": "Gemma 4 E2B IT vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-longcat-2-5"
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b.json",
        "title": "LongCat-2.5 vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.json",
        "title": "LongCat-2.5 vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "LongCat-2.5 vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-30b-a3b-instruct-2507"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-4b-instruct-2507.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-4b-instruct-2507"
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        "title": "LongCat-2.5 vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-0-8b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-2b.json",
        "title": "LongCat-2.5 vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b.json",
        "title": "LongCat-2.5 vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-4b.json",
        "title": "LongCat-2.5 vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-4b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b.json",
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        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b.json",
        "title": "LongCat-2.5 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct"
      },
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct"
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LongCat-2.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2"
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json",
        "title": "LongCat-2.5 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.json",
        "title": "LongCat-2.5 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.json",
        "title": "LongCat-2.5 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.json",
        "title": "LongCat-2.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.json",
        "title": "LongCat-2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json",
        "title": "LongCat-2.5 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json",
        "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.json",
        "title": "LongCat-2.5 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 35,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 70,
        "key": "reach",
        "longcat-2-5": 35,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "key": "risk",
        "longcat-2-5": 60,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 85,
        "key": "interface",
        "longcat-2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 8,
        "edge": "longcat-2-5",
        "glm-4-5-air": 38,
        "key": "upkeep",
        "longcat-2-5": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "glm-4-5-air": "An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. 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. Its files date from August 2025, with no Z.ai reply since July 2025.",
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5",
    "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.min.md"
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  "markdown": "GLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- GLM-4.5-Air: grade C, 60.3/100, rank #90 of 160 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- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### GLM-4.5-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- Reach, 70 against 35\n- Risk, 65 against 60\n- Agent interface, 85 against 75\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### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Documentation and upkeep, 46 against 38\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Reach: GLM-4.5-Air +35\n\n- GLM-4.5-Air (70): Memory 5 of 30, Z.ai's smallest build is FP8 at 112.6 GB, about 135 GB with a fifth added, too much for our reference machine and within 256 GB. Runtimes 25 (the cap), released llama.cpp, vLLM, SGLang and Transformers run it, and Ollama 0.40.14 (2 November 2026) builds llama.cpp b11963, which has the glm4moe architecture, as kolibri-1 and occamy-1-0 are credited for Ollama's pin. Ollama and LM Studio have no library entry. Builds 10, one quantised build (FP8). Terms 15, MIT with nothing added. Download 10, no gate. Hosted 5, Z.ai and Novita serve it on Hugging Face.\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n### Agent interface: GLM-4.5-Air +10\n\n- GLM-4.5-Air (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Z.ai names the glm45 parser for vLLM and SGLang, says to declare tools in OpenAI style and shows calls coming back. Trained tool calling 20, one or more calls per turn. Structured output 0, documented only for Z.ai's hosted API. Reasoning control 10, enable_thinking false turns thinking off per request and the glm45 reasoning parser returns it separately. Context 10, 128K native.\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n\n### Documentation and upkeep: LongCat-2.5 +8\n\n- GLM-4.5-Air (38): Model card 8 of 20, intended use, parameters and languages only. Training data 5, the technical report describes the sources and filtering with no cut-off. Last change 5, the weights' files date from August 2025 (Hub head a24ceef6, 11 August 2025, re-read on 4 November) and Z.ai's GitHub repository last changed on 1 February 2026, 276 days before this check, so within a year. Responsiveness 10, Z.ai answered 4 of the 14 questions, all in the first three days, recounted by script on 4 November. Version policy 5, a first release. File health 5, the config and tokenizer disagree on the context and a tokenisation report has no reply.\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Risk: GLM-4.5-Air +5\n\n- GLM-4.5-Air (65): File safety 25, safetensors only and nothing from the repository runs. Provenance 15, the files are in the organisation Z.ai's GitHub links to, with a SHA-256 on every LFS file. Safety evaluation 10, Z.ai's own SafetyBench results in the technical report. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain MIT. Security programme 0, no security.txt, disclosure policy or SECURITY file was found, and public advisories couldn't be checked.\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 35 | GLM-4.5-Air +35 |\n| Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 |\n| Agent interface | 10% (10 this run) | 85 | 75 | GLM-4.5-Air +10 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 46 | LongCat-2.5 +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.3 · C** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | Meituan (LongCat) |\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 | 2026-11-10 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### GLM-4.5-Air\n\n- +1.2 on 4 November 2026: correction\n\n### LongCat-2.5\n\nNo score change since it was listed.\n\n## Verdicts\n\n**GLM-4.5-Air.** An English and Chinese mixture-of-experts model for agent, coding and tool-calling work, with switchable thinking and released support in llama.cpp, vLLM, SGLang and Transformers. 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. Its files date from August 2025, with no Z.ai reply since July 2025.\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\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### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, GLM-4.5-Air or LongCat-2.5?\n\nGLM-4.5-Air scores 60.3 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. LongCat-2.5 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"longcat-2-5\"}`. From a terminal: `anchor compare glm-4-5-air longcat-2-5`\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/longcat-2-5.json\n\n## Other comparisons with GLM-4.5-Air or LongCat-2.5\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 LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.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 LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.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 LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Arete-27B vs 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