{
  "data": {
    "a": {
      "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"
    },
    "answer": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.",
    "b": {
      "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
      "name": "InternVL3.5-GPT-OSS-20B-A4B-Preview",
      "vendor": "OpenGVLab (Shanghai AI Laboratory)",
      "vendorUrl": "https://github.com/OpenGVLab",
      "kind": "weights",
      "category": "open-weights",
      "summary": "InternVL3.5-GPT-OSS-20B-A4B-Preview is OpenGVLab's open-weight vision-language preview that joins OpenAI's gpt-oss-20b mixture-of-experts model with the InternViT-300M vision encoder, 21.2B parameters with about 4B active, taking images and video and answering in text, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview",
      "markdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json",
      "dns": "internvl3-5-gpt-oss-20b-a4b-preview.grade.anchorterminal.com",
      "repo": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
      "license": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF"
        },
        {
          "registry": "huggingface",
          "name": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither the Transformers-format repository nor the GitHub-format one is gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, with gpt-oss-20b's Apache 2.0 and usage policy and InternViT's MIT licence carried with it. Running it costs the hardware, 42.5 GB of BF16 files in either format, since the gpt-oss experts were expanded from MXFP4 to 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/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "vision",
        "video",
        "reasoning",
        "merge",
        "preview",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2025-09-08",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 53.2,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 101,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 45,
          "reach": 65,
          "risk": 55,
          "upkeep": 28
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.",
        "bestFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.",
        "strengths": [
          "Plain Apache 2.0 over Apache and MIT bases, with no gate",
          "Safetensors only and no repository code in the Transformers-format files",
          "Loads in released Transformers and vLLM, and the GitHub-format twin in SGLang, llama.cpp and Ollama through its llama.cpp pin",
          "Thinking turned on per request with a documented system prompt",
          "About 4B active parameters per token on a 21.2B mixture of experts"
        ],
        "weaknesses": [
          "No quantised build from OpenGVLab, so about 51 GB at BF16",
          "Chat template uses markers its tokenizer lacks and differs from the twin's harmony template",
          "No change to the files since September 2025, and it's still a preview",
          "No safety results, agent guidance or security contact",
          "No documented tool calling or structured output, and trained at 32K tokens"
        ],
        "agentNotes": [
          "Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM",
          "Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative",
          "Use Transformers 4.55.0 or later, as the card requires for this size",
          "For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises",
          "Keep inputs within 32K tokens, the length it was trained at, whatever the config says"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 53.2
          }
        ],
        "editorialScores": {
          "interface": 45,
          "reach": 65,
          "risk": 55,
          "upkeep": 28
        },
        "provenanceScore": 61,
        "checked": {
          "categories": {
            "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": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF\", \"messages\": [{\"role\": \"user\", \"content\": [{\"type\": \"image_url\", \"image_url\": {\"url\": \"https://example.com/chart.png\"}}, {\"type\": \"text\", \"text\": \"Describe this chart.\"}]}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/internvl3-5-gpt-oss-20b-a4b-preview"
      },
      "weights": {
        "trainedBy": "OpenGVLab (Shanghai AI Laboratory)",
        "builtOn": "openai/gpt-oss-20b and OpenGVLab/InternViT-300M-448px-V2_5 (declared as a merge)",
        "params": "21.2B",
        "active": "4B",
        "context": 32768,
        "goodFor": "Vision-language preview for images, documents, charts and video with text out and an optional thinking mode, on the gpt-oss-20b mixture-of-experts backbone.",
        "runtimes": [
          "Transformers",
          "vLLM",
          "SGLang",
          "llama.cpp",
          "Ollama"
        ],
        "builds": [
          {
            "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 42.5,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "Transformers",
              "vLLM"
            ],
            "note": "Transformers' native InternVL format, no repository code. The shipped chat template uses markers that aren't in the tokenizer"
          },
          {
            "name": "InternVL3_5-GPT-OSS-20B-A4B-Preview",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 42.5,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "SGLang",
              "llama.cpp (after conversion)",
              "Ollama (through its llama.cpp pin, after conversion)",
              "Transformers (remote code)"
            ],
            "note": "The same weights in OpenGVLab's GitHub format (InternVLChatModel) with modeling code for trust_remote_code and gpt-oss's harmony chat template"
          }
        ],
        "run": "vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768",
        "runBuild": "InternVL3_5-GPT-OSS-20B-A4B-Preview-HF",
        "checked": "2026-11-04",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "OpenGVLab (Shanghai AI Laboratory)",
        "family": "InternVL3 (3.0, 3.5)",
        "url": "https://www.anchorterminal.com/open-weights/#opengvlab",
        "others": [
          {
            "name": "InternVL3_5-8B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Flash",
            "params": "8B (Hub safetensors count 8.81B)"
          },
          {
            "name": "InternVL3_5-4B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Flash",
            "params": "4B (Hub safetensors count 4.91B)"
          },
          {
            "name": "InternVL3_5-38B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Flash",
            "params": "38B (Hub safetensors count 39.56B)"
          },
          {
            "name": "InternVL3_5-30B-A3B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Flash",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.99B)"
          },
          {
            "name": "InternVL3_5-2B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Flash",
            "params": "2B (Hub safetensors count 2.49B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Flash",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 241.71B)"
          },
          {
            "name": "InternVL3_5-1B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Flash",
            "params": "1B (Hub safetensors count 1.15B)"
          },
          {
            "name": "InternVL3_5-14B-Flash",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Flash",
            "params": "14B (Hub safetensors count 15.50B)"
          },
          {
            "name": "InternVL3_5-8B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained",
            "params": "8B (Hub safetensors count 8.53B)"
          },
          {
            "name": "InternVL3_5-8B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO",
            "params": "8B (Hub safetensors count 8.53B)"
          },
          {
            "name": "InternVL3_5-8B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct",
            "params": "8B (Hub safetensors count 8.53B)"
          },
          {
            "name": "InternVL3_5-8B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-8B",
            "params": "8B (Hub safetensors count 8.53B)"
          },
          {
            "name": "InternVL3_5-4B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained",
            "params": "4B (Hub safetensors count 4.73B)"
          },
          {
            "name": "InternVL3_5-4B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO",
            "params": "4B (Hub safetensors count 4.73B)"
          },
          {
            "name": "InternVL3_5-4B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct",
            "params": "4B (Hub safetensors count 4.73B)"
          },
          {
            "name": "InternVL3_5-4B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-4B",
            "params": "4B (Hub safetensors count 4.73B)"
          },
          {
            "name": "InternVL3_5-38B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3_5-38B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3_5-38B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3_5-38B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-38B",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3_5-30B-A3B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Pretrained",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-30B-A3B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-MPO",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-30B-A3B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Instruct",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-30B-A3B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B",
            "params": "30B total, 3B active (MoE) (Hub safetensors count 30.85B)"
          },
          {
            "name": "InternVL3_5-2B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Pretrained",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-MPO",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B-Instruct",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-2B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-2B",
            "params": "2B (Hub safetensors count 2.35B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Pretrained",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-MPO",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-241B-A28B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Instruct",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-241B-A28B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B",
            "params": "241B total, 28B active (MoE) (Hub safetensors count 240.70B)"
          },
          {
            "name": "InternVL3_5-1B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained",
            "params": "1B (Hub safetensors count 1.06B)"
          },
          {
            "name": "InternVL3_5-1B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO",
            "params": "1B (Hub safetensors count 1.06B)"
          },
          {
            "name": "InternVL3_5-1B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B-Instruct",
            "params": "1B (Hub safetensors count 1.06B)"
          },
          {
            "name": "InternVL3_5-1B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-1B",
            "params": "1B (Hub safetensors count 1.06B)"
          },
          {
            "name": "InternVL3_5-14B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Pretrained",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B-MPO",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-MPO",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B-Instruct",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3_5-14B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-14B",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3-9B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Pretrained",
            "params": "9B (Hub safetensors count 9.14B)"
          },
          {
            "name": "InternVL3-8B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Pretrained",
            "params": "8B (Hub safetensors count 7.94B)"
          },
          {
            "name": "InternVL3-78B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Pretrained",
            "params": "78B (Hub safetensors count 78.41B)"
          },
          {
            "name": "InternVL3-38B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Pretrained",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Pretrained",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Pretrained",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B-Pretrained",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Pretrained",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3-9B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B-Instruct",
            "params": "9B (Hub safetensors count 9.14B)"
          },
          {
            "name": "InternVL3-8B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct",
            "params": "8B (Hub safetensors count 7.94B)"
          },
          {
            "name": "InternVL3-78B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B-Instruct",
            "params": "78B (Hub safetensors count 78.41B)"
          },
          {
            "name": "InternVL3-38B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B-Instruct",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B-Instruct",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B-Instruct",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B-Instruct",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B-Instruct",
            "params": "14B (Hub safetensors count 15.12B)"
          },
          {
            "name": "InternVL3-9B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-9B",
            "params": "9B (Hub safetensors count 9.14B)"
          },
          {
            "name": "InternVL3-8B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-8B",
            "params": "8B (Hub safetensors count 7.94B)"
          },
          {
            "name": "InternVL3-78B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-78B",
            "params": "78B (Hub safetensors count 78.41B)"
          },
          {
            "name": "InternVL3-38B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-38B",
            "params": "38B (Hub safetensors count 38.39B)"
          },
          {
            "name": "InternVL3-2B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-2B",
            "params": "2B (Hub safetensors count 2.09B)"
          },
          {
            "name": "InternVL3-1B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-1B",
            "params": "1B (Hub safetensors count 938M)"
          },
          {
            "name": "InternVL3-14B",
            "url": "https://huggingface.co/OpenGVLab/InternVL3-14B",
            "params": "14B (Hub safetensors count 15.12B)"
          }
        ],
        "olderCount": 41
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai AI Laboratory (OpenGVLab), not named in the repository",
        "domain": "github.com/OpenGVLab",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-04",
        "notes": [
          "OpenGVLab's GitHub organisation profile links its Hugging Face organisation. The repositories carry no LICENSE file or copyright line, and shlab.org.cn's registration date wasn't checked.",
          "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 commit history, five commits from 29 August to 8 September 2025, all titled Upload folder using huggingface_hub, which don't say what changed. Unchanged on 4 November 2026.",
          "www.shlab.org.cn and internvl.github.io answered 404 for security.txt on 21 October 2026, and OpenGVLab/InternVL has no security policy."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.json",
      "live": {
        "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
        "domain": {
          "domain": "github.com/opengvlab",
          "checkedAt": "2026-11-08T15:08:02.394346699Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:18.761399377Z",
            "changedAt": "2026-11-10T16:22:18.761399377Z",
            "fingerprint": "e63419d9bf08"
          }
        ],
        "updatedAt": "2026-11-10T16:22:18.761399377Z"
      }
    },
    "edges": [
      {
        "by": 40,
        "category": "upkeep",
        "leads": "hy-memory-30b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "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.",
          "b": "Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line."
        },
        "scores": {
          "a": 68,
          "b": 28
        }
      },
      {
        "by": 20,
        "category": "reach",
        "leads": "hy-memory-30b-a3b",
        "name": "Reach",
        "reasons": {
          "a": "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.",
          "b": "Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "hy-memory-30b-a3b",
        "name": "Agent interface",
        "reasons": {
          "a": "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.",
          "b": "Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens."
        },
        "scores": {
          "a": 65,
          "b": 45
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "hy-memory-30b-a3b",
        "name": "Risk",
        "reasons": {
          "a": "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.",
          "b": "File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories."
        },
        "scores": {
          "a": 60,
          "b": 55
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Tencent (Hy team)",
        "b": "OpenGVLab (Shanghai AI Laboratory)",
        "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": "Apache-2.0",
        "b": "Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-16",
        "b": "2025-09-08",
        "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": "none",
        "b": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.",
        "question": "Which is better for AI agents, Hy-Memory-30B-A3B or InternVL3.5-GPT-OSS-20B-A4B-Preview?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 65",
          "Risk, 60 against 55",
          "Agent interface, 65 against 45",
          "Documentation and upkeep, 68 against 28"
        ],
        "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"
      },
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.",
        "slug": "internvl3-5-gpt-oss-20b-a4b-preview",
        "watchFor": "No quantised build from OpenGVLab, so about 51 GB at BF16"
      }
    ],
    "history": {
      "hy-memory-30b-a3b": null,
      "internvl3-5-gpt-oss-20b-a4b-preview": [
        {
          "causes": [
            "correction"
          ],
          "date": "2026-11-04",
          "delta": 2,
          "from": 51.2,
          "fromGrade": "D",
          "to": 53.2,
          "toGrade": "D"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "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/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "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/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 20,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 85,
        "internvl3-5-gpt-oss-20b-a4b-preview": 65,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 60,
        "internvl3-5-gpt-oss-20b-a4b-preview": 55,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 20,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 65,
        "internvl3-5-gpt-oss-20b-a4b-preview": 45,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 40,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 68,
        "internvl3-5-gpt-oss-20b-a4b-preview": 28,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "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.",
      "internvl3-5-gpt-oss-20b-a4b-preview": "An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results."
    }
  },
  "kind": "anchor.page",
  "links": {
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    "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md",
    "mcp": "https://www.anchorterminal.com/mcp",
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  "markdown": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\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- InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 53.2/100, rank #136 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.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### 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 65\n- Risk, 60 against 55\n- Agent interface, 65 against 45\n- Documentation and upkeep, 68 against 28\n\nWatch for: Not a general assistant or a tool caller\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview (D)\n\nGood for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.\n\nWatch for: No quantised build from OpenGVLab, so about 51 GB at BF16\n\n\n## Why the edge\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +40\n\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (28): Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.\n\n### Reach: Hy-Memory-30B-A3B +20\n\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (65): Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.\n\n### Agent interface: Hy-Memory-30B-A3B +20\n\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (45): Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.\n\n### Risk: Hy-Memory-30B-A3B +5\n\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- InternVL3.5-GPT-OSS-20B-A4B-Preview (55): File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.\n\n## Score by category\n\n| Category | Weight | Hy-Memory-30B-A3B | InternVL3.5-GPT-OSS-20B-A4B-Preview | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 65 | Hy-Memory-30B-A3B +20 |\n| Risk | 20% (20 this run) | 60 | 55 | Hy-Memory-30B-A3B +5 |\n| Agent interface | 10% (10 this run) | 65 | 45 | Hy-Memory-30B-A3B +20 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 28 | Hy-Memory-30B-A3B +40 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **53.2 · D** | |\n\n## Facts side by side\n\n| Fact | Hy-Memory-30B-A3B | InternVL3.5-GPT-OSS-20B-A4B-Preview |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | OpenGVLab (Shanghai AI Laboratory) |\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 | Apache-2.0 | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2025-09-08 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 1.8/5 (4) |\n\n## How the scores have moved\n\n### Hy-Memory-30B-A3B\n\nNo score change since it was listed.\n\n### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n- +2.0 on 4 November 2026: correction\n\n## Verdicts\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**InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.\n\n## Before you call either\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### InternVL3.5-GPT-OSS-20B-A4B-Preview\n\n1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM\n2. Check the rendered prompt before relying on it, since the shipped template's \u003c|im_start|\u003e markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative\n3. Use Transformers 4.55.0 or later, as the card requires for this size\n4. For thinking, send the card's thinking system prompt and parse the reasoning out of the \u003cthink\u003e tags, with temperature 0.6 and sampling on as the card advises\n5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says\n\n## Questions\n\n### Which is better for AI agents, Hy-Memory-30B-A3B or InternVL3.5-GPT-OSS-20B-A4B-Preview?\n\nHy-Memory-30B-A3B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-internvl3-5-gpt-oss-20b-a4b-preview.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory-30b-a3b\", \"b\": \"internvl3-5-gpt-oss-20b-a4b-preview\"}`. 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