{
  "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": "Hy-Memory-30B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.",
    "b": {
      "slug": "hy-memory-30b-a3b",
      "name": "Hy-Memory-30B-A3B",
      "vendor": "Tencent (Hy team)",
      "vendorUrl": "https://aistudio.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json",
      "dns": "hy-memory-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "agent-memory",
        "specialist",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 54,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.",
        "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "strengths": [
          "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks",
          "Fits a 24 GB card from Tencent's own Q4_K_M GGUF",
          "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases",
          "Plain Apache 2.0, ungated, safetensors and GGUF only",
          "Tencent replied to three of the four discussions"
        ],
        "weaknesses": [
          "Not a general assistant or a tool caller",
          "No limitations, safety results or guidance on untrusted text in what it reads",
          "Training data isn't described, and the card gives no context length",
          "The results are on a 20% subsample, run by Tencent",
          "No inference provider, so it has to be run locally"
        ],
        "agentNotes": [
          "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user",
          "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does",
          "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does",
          "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented",
          "Treat what it stores as unverified. It keeps what a conversation says, true or not"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "provenanceScore": 75,
        "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": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/hy-memory-30b-a3b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy3",
        "wevisone-vl-2b",
        "wevisone-vl-4b"
      ],
      "weights": {
        "trainedBy": "Tencent (Hunyuan)",
        "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3",
        "params": "30.1B",
        "active": "3B",
        "context": 262144,
        "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Hy-Memory-30B-A3B-Q4_K_M",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.24,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed"
          },
          {
            "name": "Hy-Memory-30B-A3B-Q6_K",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 24.71,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-FP8",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 30.58,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-Q8_0",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 31.99,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.13,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja",
        "runBuild": "Hy-Memory-30B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "Hy-Memory",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.",
          "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json"
    },
    "edges": [
      {
        "by": 30,
        "category": "upkeep",
        "leads": "hy-memory-30b-a3b",
        "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 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68."
        },
        "scores": {
          "a": 38,
          "b": 68
        }
      },
      {
        "by": 20,
        "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. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 15,
        "category": "reach",
        "leads": "hy-memory-30b-a3b",
        "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 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85."
        },
        "scores": {
          "a": 70,
          "b": 85
        }
      },
      {
        "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, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Z.ai (Zhipu AI)",
        "b": "Tencent (Hy team)",
        "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": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2025-08-11",
        "b": "2026-10-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Hy-Memory-30B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface.",
        "question": "Which is better for AI agents, GLM-4.5-Air or Hy-Memory-30B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 60",
          "Agent interface, 85 against 65"
        ],
        "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": [
          "Reach, 85 against 70",
          "Documentation and upkeep, 68 against 38"
        ],
        "also": null,
        "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "slug": "hy-memory-30b-a3b",
        "watchFor": "Not a general assistant or a tool caller"
      }
    ],
    "history": {
      "glm-4-5-air": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 1.2,
          "from": 59.1,
          "fromGrade": "C",
          "to": 60.3,
          "toGrade": "C"
        }
      ],
      "hy-memory-30b-a3b": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.json",
        "title": "Agents-A1-4B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json",
        "title": "Apertus v1.5 8B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air"
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      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.json",
        "title": "Apertus v1.5 8B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b"
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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"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.json",
        "title": "Apodex-1.1-mini vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json",
        "title": "Arete-27B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b"
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      {
        "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-hy-memory-30b-a3b.json",
        "title": "BigBang-v1 vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-glm-4-5-air"
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        "json": "https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b"
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.json",
        "title": "Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-bolt-e4b-4gb"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-4b.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-4b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Hy-Memory-30B-A3B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.json",
        "title": "Hy-Memory-30B-A3B vs North Mini Code 1.0",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.json",
        "title": "Hy-Memory-30B-A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think.json",
        "title": "Hy-Memory-30B-A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.json",
        "title": "Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json",
        "title": "Hy-Memory-30B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json",
        "title": "Hy-Memory-30B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b.json",
        "title": "Hy-Memory-30B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Hy-Memory-30B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json",
        "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json",
        "title": "Hy-Memory-30B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 15,
        "edge": "hy-memory-30b-a3b",
        "glm-4-5-air": 70,
        "hy-memory-30b-a3b": 85,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 65,
        "hy-memory-30b-a3b": 60,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "glm-4-5-air",
        "glm-4-5-air": 85,
        "hy-memory-30b-a3b": 65,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 30,
        "edge": "hy-memory-30b-a3b",
        "glm-4-5-air": 38,
        "hy-memory-30b-a3b": 68,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Hy-Memory-30B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface. 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.",
      "hy-memory-30b-a3b": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b",
    "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.min.md"
  },
  "markdown": "Hy-Memory-30B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on risk and agent interface. 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- Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 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- Risk, 65 against 60\n- Agent interface, 85 against 65\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### Hy-Memory-30B-A3B (C)\n\nGood for: Agents with an external memory framework that want a local model for the write path on a 24 GB card\n\nAhead on:\n- Reach, 85 against 70\n- Documentation and upkeep, 68 against 38\n\nWatch for: Not a general assistant or a tool caller\n\n\n## Why the edge\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +30\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- Hy-Memory-30B-A3B (68): Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.\n\n### Agent interface: GLM-4.5-Air +20\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- Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.\n\n### Reach: Hy-Memory-30B-A3B +15\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- Hy-Memory-30B-A3B (85): Memory 25, Tencent's own Q4_K_M GGUF is 18.24 GB, and 18.24 GB × 1.2 = 21.9 GB, which fits 24 GB but not 16 GB. Its Q3_K_M file and the two files with experts at 1 or 2 bits are below 4-bit precision and don't count. Runtimes 20, hy_v3 model code in released vLLM (HYV3ForCausalLM in the v0.33.0 registry), SGLang v0.5.23 (hunyuan_v3.py), Transformers v5.22.2 and llama.cpp b12242, 5 each. Builds 15, Tencent publishes an FP8 repository and a GGUF repository with Q4_K_M, Q6_K and Q8_0 files in its own organisation, so quantised builds at two precisions or more. Terms 15, Apache 2.0, with only a preamble naming Tencent as copyright holder above the licence text. Download 10, gated is false for the main, FP8 and GGUF repositories on 10 November 2026, so the files download with no account or terms to accept. Hosted 0, Hugging Face lists no inference provider for the model (an empty provider mapping on 10 November 2026), and the card names no API. 25 + 20 + 15 + 15 + 10 + 0 = 85.\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- Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | GLM-4.5-Air | Hy-Memory-30B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 70 | 85 | Hy-Memory-30B-A3B +15 |\n| Risk | 20% (20 this run) | 65 | 60 | GLM-4.5-Air +5 |\n| Agent interface | 10% (10 this run) | 85 | 65 | GLM-4.5-Air +20 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 68 | Hy-Memory-30B-A3B +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **60.3 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | GLM-4.5-Air | Hy-Memory-30B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Z.ai (Zhipu AI) | Tencent (Hy team) |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2025-08-11 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### GLM-4.5-Air\n\n- +1.2 on 4 November 2026: correction\n\n### Hy-Memory-30B-A3B\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**Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.\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### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n## Questions\n\n### Which is better for AI agents, GLM-4.5-Air or Hy-Memory-30B-A3B?\n\nHy-Memory-30B-A3B and GLM-4.5-Air score within a point of each other on agent readiness, 61 (C) and 60.3 (C). GLM-4.5-Air leads on 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-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"glm-4-5-air\", \"b\": \"hy-memory-30b-a3b\"}`. From a terminal: `anchor compare glm-4-5-air hy-memory-30b-a3b`\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/hy-memory-30b-a3b.json\n\n## Other comparisons with GLM-4.5-Air or Hy-Memory-30B-A3B\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 Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.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 Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.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 Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs 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    "title": "GLM-4.5-Air vs Hy-Memory-30B-A3B for AI agents (2026)",
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