{
  "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": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface.",
    "b": {
      "slug": "glm-5-3-flash",
      "name": "GLM-5.3-Flash",
      "vendor": "Z.ai",
      "vendorUrl": "https://z.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "GLM-5.3-Flash is Z.ai's open-weight multimodal mixture-of-experts model, 320B parameters with 18B active and a 1,048,576-token context, published on Hugging Face in FP8 and BF16 under the MIT licence.",
      "url": "https://www.anchorterminal.com/tools/glm-5-3-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.json",
      "repo": "https://huggingface.co/zai-org/GLM-5.3-Flash",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-Flash"
        },
        {
          "registry": "huggingface",
          "name": "zai-org/GLM-5.3-Flash-BF16"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. Z.ai also sells hosted access as glm-5.3-flash on its own API, which isn't graded here. Running the weights yourself costs the hardware, 328.4 GB for the FP8 files and 642.7 GB for BF16 (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "mit",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-08-26",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 65.1,
        "grade": "B",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1197,
        "categoryRank": 13,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 75,
          "interface": 70,
          "reach": 60,
          "risk": 55,
          "upkeep": 58
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.",
        "bestFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider",
        "strengths": [
          "MIT weights with no use policy, downloadable with no account",
          "Model code in released llama.cpp b11768, vLLM 0.32.0, SGLang 0.5.22 and Transformers 5.16.1",
          "Safetensors only, no remote code, files in Z.ai's own organisation with a SHA-256 per shard",
          "1,048,576 tokens of native context and several tool calls per turn in the chat template",
          "Chat template changed as recently as 15 October 2026, and hosted by Z.ai and six other providers for agents without the hardware"
        ],
        "weaknesses": [
          "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists",
          "No safety evaluation, agent-use guidance or security contact",
          "Thinking is always on, and reasoning_effort only lowers it to low",
          "The tool-call format lives only in the chat template, with the parser named by the runtimes",
          "The card leaves out intended use, limitations, context length and knowledge cut-off, and no zai-org reply on discussions since 28 September"
        ],
        "agentNotes": [
          "Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)",
          "Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs",
          "Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max",
          "Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block",
          "Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt",
          "On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "B",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 65.1
          }
        ],
        "editorialScores": {
          "capability": 75,
          "interface": 70,
          "reach": 60,
          "risk": 55,
          "upkeep": 58
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "capability": "2026-10-21",
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM 0.32.0 or later, per vLLM's recipe (FP8, about 386 GB of GPU memory)\nvllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 \\\n  --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"zai-org/GLM-5.3-Flash\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"low\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/glm-5-3-flash"
      },
      "sameCompany": [
        "glm-4-5-air",
        "glm-4-7-flash",
        "glm-5-3"
      ],
      "weights": {
        "trainedBy": "Z.ai",
        "params": "320B",
        "active": "18B",
        "context": 1048576,
        "goodFor": "Multimodal agent and coding model: text, images and video in, text out, with tool calling and always-on reasoning.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "GLM-5.3-Flash-FP4-Local",
            "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-FP4-Local",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 177.5,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "GLM-5.3-Flash-NVFP4",
            "url": "https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4",
            "who": "NVIDIA",
            "precision": "nvfp4",
            "sizeGB": 204.4,
            "memoryGB": 256,
            "format": "safetensors",
            "note": "NVIDIA's own FP4 build (Model Optimizer), made for NVIDIA Blackwell GPUs such as DGX Spark and RTX PRO 6000; FP4 kernels don't run on the AMD reference machine. Not the publisher's build, so it doesn't count toward Reach's memory item. Size summed from the Hub's file list on 21 October 2026."
          },
          {
            "name": "GLM-5.3-Flash",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 328.3,
            "memoryGB": 512,
            "format": "safetensors"
          },
          {
            "name": "GLM-5.3-Flash-BF16",
            "url": "https://huggingface.co/zai-org/GLM-5.3-Flash-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 642.7,
            "memoryGB": 1024,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47",
        "runBuild": "GLM-5.3-Flash",
        "checked": "2026-10-21",
        "minMemoryGB": 256
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      "family": {
        "lab": "Z.ai (Zhipu AI)",
        "family": "GLM-5",
        "url": "https://www.anchorterminal.com/open-weights/#zai",
        "others": [
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            "name": "GLM-5",
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            "params": "744B-A40B"
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          {
            "name": "GLM-5.1",
            "url": "https://huggingface.co/zai-org/GLM-5.1",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.2",
            "url": "https://huggingface.co/zai-org/GLM-5.2",
            "params": "744B-A40B"
          },
          {
            "name": "GLM-5.3",
            "url": "https://huggingface.co/zai-org/GLM-5.3",
            "params": "744B-A40B",
            "listing": "https://www.anchorterminal.com/tools/glm-5-3"
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        ],
        "olderCount": 19,
        "unrestrictedCopies": 5
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      "area": "models",
      "provenance": {
        "legalEntity": "Z.AI Co., Ltd (named in the licence's copyright line)",
        "domain": "z.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/zai-org/GLM-5.3-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The LICENSE file reads Copyright (c) 2026 Z.AI Co., Ltd. The registration date of z.ai couldn't be read, since the RDAP service answered 429 on 21 October 2026.",
          "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history. Early messages say only \"update\"; later ones say what changed (a chat template fix on 4 September, evaluation results on 7 September and 12 October, a chat template speed-up on 15 October).",
          "z.ai/.well-known/security.txt and docs.z.ai/.well-known/security.txt answered 404 on 21 October 2026."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/glm-5-3-flash.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "Z.ai",
        "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": "2026-08-26",
        "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": "2.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface.",
        "question": "Which is better for AI agents, GLM-4.5-Air or GLM-5.3-Flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 65 against 60",
          "Risk, 65 against 55",
          "Agent interface, 85 against 70"
        ],
        "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": [
          "Capability, 75 against 50",
          "Documentation and upkeep, 58 against 38"
        ],
        "also": null,
        "goodFor": "Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider",
        "slug": "glm-5-3-flash",
        "watchFor": "Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists"
      }
    ],
    "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",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json",
        "title": "Agents-A1-4B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash"
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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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json",
        "title": "Apertus v1.5 8B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash"
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      {
        "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-glm-5-3-flash.json",
        "title": "Apodex-1.1-mini vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-glm-5-3-flash"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash.json",
        "title": "Cosmos-Reason2-2B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-glm-5-3-flash"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash.json",
        "title": "Darwin-36B-Opus vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-glm-5-3-flash"
      },
      {
        "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-glm-5-3-flash.json",
        "title": "DeepSeek-V3.2 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-glm-5-3-flash"
      },
      {
        "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-glm-5-3-flash.json",
        "title": "DeepSeek V4.1 Flash vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash"
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        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-4-5-air.json",
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        "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-glm-5-3-flash.json",
        "title": "DiffusionGemma 26B A4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-glm-5-3-flash"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash.json",
        "title": "Gemma 4 12B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-glm-5-3-flash"
      },
      {
        "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-glm-5-3-flash.json",
        "title": "Gemma 4 26B A4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-glm-5-3-flash"
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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-glm-5-3-flash.json",
        "title": "Gemma 4 31B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-glm-5-3-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air.json",
        "title": "Gemma 4 E2B IT vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-glm-5-3-flash"
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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-glm-5-3-flash.json",
        "title": "Gemma 4 E4B IT vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-glm-5-3-flash"
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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.json",
        "title": "GLM-4.5-Air vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3"
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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/glm-4-5-air-vs-gpt-oss-20b.json",
        "title": "GLM-4.5-Air vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-gpt-oss-20b"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-granite-4-2-3b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.json",
        "title": "GLM-4.5-Air vs Hy3",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling.json",
        "title": "GLM-4.5-Air vs Inkling",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small.json",
        "title": "GLM-4.5-Air vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-inkling-small"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-internvl3-5-gpt-oss-20b-a4b-preview"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3.json",
        "title": "GLM-4.5-Air vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kimi-k3"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.json",
        "title": "GLM-4.5-Air vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1.json",
        "title": "GLM-4.5-Air vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-m-1"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1.json",
        "title": "GLM-4.5-Air vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-s-2-1"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1.json",
        "title": "GLM-4.5-Air vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-laguna-xs-2-1"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json",
        "title": "GLM-4.5-Air vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny.json",
        "title": "GLM-4.5-Air vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-ling-3-0-tiny"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "GLM-4.5-Air vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-mimo-v2-6-distill-qwen-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5.json",
        "title": "GLM-4.5-Air vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm-o-4-5"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm5-2b.json",
        "title": "GLM-4.5-Air vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minicpm5-2b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5.json",
        "title": "GLM-4.5-Air vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7.json",
        "title": "GLM-4.5-Air vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b.json",
        "title": "GLM-4.5-Air vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-muse-glimmer-30b"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "GLM-5.3-Flash vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507.json",
        "title": "GLM-5.3-Flash vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b.json",
        "title": "GLM-5.3-Flash vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b.json",
        "title": "GLM-5.3-Flash vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b.json",
        "title": "GLM-5.3-Flash vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next.json",
        "title": "GLM-5.3-Flash vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct.json",
        "title": "GLM-5.3-Flash vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2.json",
        "title": "GLM-5.3-Flash vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b.json",
        "title": "GLM-5.3-Flash vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.json",
        "title": "GLM-5.3-Flash vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "GLM-5.3-Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.json",
        "title": "GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash.json",
        "title": "GLM-5.3 vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-glm-5-3-flash"
      }
    ],
    "scores": [
      {
        "by": 25,
        "edge": "glm-5-3-flash",
        "glm-4-5-air": 50,
        "glm-5-3-flash": 75,
        "key": "capability",
        "name": "Capability",
        "weight": 35
      },
      {
        "by": 5,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "glm-5-3-flash": 60,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 10,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "glm-5-3-flash": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 85,
        "glm-5-3-flash": 70,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 20,
        "edge": "glm-5-3-flash",
        "glm-4-5-air": 38,
        "glm-5-3-flash": 58,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface. 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.",
      "glm-5-3-flash": "An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash",
    "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.min.md"
  },
  "markdown": "GLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface. 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- GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.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- Reach, 65 against 60\n- Risk, 65 against 55\n- Agent interface, 85 against 70\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### GLM-5.3-Flash (B)\n\nGood for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider\n\nAhead on:\n- Capability, 75 against 50\n- Documentation and upkeep, 58 against 38\n\nWatch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists\n\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | GLM-5.3-Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 50 | 75 | GLM-5.3-Flash +25 |\n| Reach | 25% (25 this run) | 65 | 60 | GLM-4.5-Air +5 |\n| Risk | 20% (20 this run) | 65 | 55 | GLM-4.5-Air +10 |\n| Agent interface | 10% (10 this run) | 85 | 70 | GLM-4.5-Air +15 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 58 | GLM-5.3-Flash +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.1 · C** | **65.1 · B** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | GLM-5.3-Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | Z.ai |\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 | 2026-08-26 |\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) | 2.8/5 (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**GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.\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### GLM-5.3-Flash\n\n1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)\n2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Pass `chat_template_kwargs: {\"reasoning_effort\": \"low\"}` for short tasks; anything other than low or high falls back to max\n4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block\n5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt\n6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai\n\n## Questions\n\n### Which is better for AI agents, GLM-4.5-Air or GLM-5.3-Flash?\n\nGLM-5.3-Flash scores 65.1 (B) on agent readiness against GLM-4.5-Air's 59.1 (C), and leads in 2 of 5 scored categories. GLM-4.5-Air leads on reach, risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-glm-5-3-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"glm-5-3-flash\"}`. From a terminal: `anchor compare glm-4-5-air glm-5-3-flash`\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/glm-5-3-flash.json\n\n## Other comparisons with GLM-4.5-Air or GLM-5.3-Flash\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 GLM-5.3-Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.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 GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.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 GLM-5.3-Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-glm-5-3-flash.md)\n- [BigBang-v1 vs 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