{
  "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 LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface.",
    "b": {
      "slug": "longcat-2-5",
      "name": "LongCat-2.5",
      "vendor": "Meituan (LongCat)",
      "vendorUrl": "https://longcat.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.",
      "url": "https://www.anchorterminal.com/tools/longcat-2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json",
      "dns": "longcat-2-5.grade.anchorterminal.com",
      "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "license": "MIT",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5"
        },
        {
          "registry": "huggingface",
          "name": "meituan-longcat/LongCat-2.5-W4A4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/meituan-longcat/LongCat-2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic-coding",
        "mit",
        "safetensors",
        "int4",
        "npu",
        "self-hosted"
      ],
      "lastRelease": "2026-11-10",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 50.5,
        "grade": "D",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 107,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.",
        "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "strengths": [
          "MIT licence, ungated",
          "Tool calling documented with a worked example",
          "Reasoning can be switched per request and comes back as reasoning_content",
          "Meituan publishes its own 4-bit build",
          "Updated on the day we checked"
        ],
        "weaknesses": [
          "3,562 GB in BF16 and 1,336 GB at 4 bits",
          "No released runtime has its architecture",
          "The only documented deployment is on NPU hardware through Meituan's fork",
          "config.json names model code that isn't in the repository",
          "No stated context length, languages, safety results or data sources",
          "No inference provider serves it"
        ],
        "agentNotes": [
          "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet",
          "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format",
          "Pass `chat_template_kwargs` with `enable_thinking` true or false",
          "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends",
          "The card's results use temperature 1.0 and top_p 0.95",
          "Its benchmark table is Meituan's own. The unmarked scores are in-house"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "D",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 50.5
          }
        ],
        "editorialScores": {
          "interface": 75,
          "reach": 35,
          "risk": 60,
          "upkeep": 46
        },
        "provenanceScore": 49,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md",
        "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/longcat-2-5"
      },
      "weights": {
        "trainedBy": "Meituan (LongCat team)",
        "builtOn": "LongCat-2.0 and LongCat-Next, per the card",
        "params": "1.6T",
        "active": "48B",
        "context": 524288,
        "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.",
        "runtimes": [],
        "builds": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 1336,
            "memoryGB": 2048,
            "format": "safetensors",
            "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators"
          },
          {
            "name": "LongCat-2.5 (BF16 safetensors)",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 3562,
            "memoryGB": 8192,
            "format": "safetensors",
            "note": "210 files"
          }
        ],
        "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu",
        "runBuild": "LongCat-2.5-W4A4",
        "checked": "2026-11-10",
        "minMemoryGB": 2048
      },
      "family": {
        "lab": "Meituan (LongCat)",
        "family": "LongCat-2",
        "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat",
        "others": [
          {
            "name": "LongCat-2.5-W4A4",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4"
          },
          {
            "name": "LongCat-2.0",
            "url": "https://huggingface.co/meituan-longcat/LongCat-2.0"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up",
        "domain": "longcat.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-11-10",
        "notes": [
          "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.",
          "The domain's registration date wasn't looked up.",
          "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the commit history, 50 commits or more since 4 November 2026."
        ],
        "score": 49
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json"
    },
    "edges": [
      {
        "by": 50,
        "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 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35."
        },
        "scores": {
          "a": 85,
          "b": 35
        }
      },
      {
        "by": 22,
        "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 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46."
        },
        "scores": {
          "a": 68,
          "b": 46
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "longcat-2-5",
        "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, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75."
        },
        "scores": {
          "a": 65,
          "b": 75
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Tencent (Hy team)",
        "b": "Meituan (LongCat)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-16",
        "b": "2026-11-10",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "not read yet",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      }
    ],
    "faq": [
      {
        "answer": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface.",
        "question": "Which is better for AI agents, Hy-Memory-30B-A3B or LongCat-2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 35",
          "Documentation and upkeep, 68 against 46"
        ],
        "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": [
          "Agent interface, 75 against 65"
        ],
        "also": null,
        "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do",
        "slug": "longcat-2-5",
        "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b.json",
        "title": "LongCat-2.5 vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b.json",
        "title": "LongCat-2.5 vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b.json",
        "title": "LongCat-2.5 vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next.json",
        "title": "LongCat-2.5 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "LongCat-2.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json",
        "title": "LongCat-2.5 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.json",
        "title": "LongCat-2.5 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.json",
        "title": "LongCat-2.5 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.json",
        "title": "LongCat-2.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.json",
        "title": "LongCat-2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json",
        "title": "LongCat-2.5 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json",
        "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json",
        "title": "LongCat-2.5 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.json",
        "title": "LongCat-2.5 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 50,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 85,
        "key": "reach",
        "longcat-2-5": 35,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 0,
        "edge": "",
        "hy-memory-30b-a3b": 60,
        "key": "risk",
        "longcat-2-5": 60,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 10,
        "edge": "longcat-2-5",
        "hy-memory-30b-a3b": 65,
        "key": "interface",
        "longcat-2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 22,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 68,
        "key": "upkeep",
        "longcat-2-5": 46,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface. 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.",
      "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5",
    "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.min.md"
  },
  "markdown": "Hy-Memory-30B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface. 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- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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 35\n- Documentation and upkeep, 68 against 46\n\nWatch for: Not a general assistant or a tool caller\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Agent interface, 75 against 65\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Reach: Hy-Memory-30B-A3B +50\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- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +22\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- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Agent interface: LongCat-2.5 +10\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- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n\n## Score by category\n\n| Category | Weight | Hy-Memory-30B-A3B | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 35 | Hy-Memory-30B-A3B +50 |\n| Risk | 20% (20 this run) | 60 | 60 | even |\n| Agent interface | 10% (10 this run) | 65 | 75 | LongCat-2.5 +10 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 46 | Hy-Memory-30B-A3B +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | Hy-Memory-30B-A3B | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | Meituan (LongCat) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-11-10 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\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**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n## Before you call either\n\n### 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### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, Hy-Memory-30B-A3B or LongCat-2.5?\n\nHy-Memory-30B-A3B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory-30b-a3b\", \"b\": \"longcat-2-5\"}`. From a terminal: `anchor compare hy-memory-30b-a3b longcat-2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json and https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n\n## Other comparisons with Hy-Memory-30B-A3B or LongCat-2.5\n\n- [Agents-A1-4B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.md)\n- [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md)\n- [Apertus v1.5 8B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy-memory-30b-a3b.md)\n- [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md)\n- [Apodex-1.1-mini vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md)\n- [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Arete-27B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [BigBang-v1 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy-memory-30b-a3b.md)\n- [BigBang-v1 vs LongCat-2.5](https://www.anchorterminal.com/compare/bigbang-v1-vs-longcat-2-5.md)\n- [Carbon-X vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/carbon-x-vs-hy-memory-30b-a3b.md)\n- [Carbon-X vs LongCat-2.5](https://www.anchorterminal.com/compare/carbon-x-vs-longcat-2-5.md)\n- [Cola2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cola2-vs-hy-memory-30b-a3b.md)\n- [Cola2 vs LongCat-2.5](https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.md)\n- [Cosmos-Reason2-2B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy-memory-30b-a3b.md)\n- [Cosmos-Reason2-2B vs LongCat-2.5](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-longcat-2-5.md)\n- [Darwin-36B-Opus vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy-memory-30b-a3b.md)\n- [Darwin-36B-Opus vs LongCat-2.5](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-longcat-2-5.md)\n- [DeepSeek-V3.2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V3.2 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-longcat-2-5.md)\n- [DeepSeek V4.1 Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy-memory-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-longcat-2-5.md)\n- [DeepSeek-V4-Flash-0731 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy-memory-30b-a3b.md)\n- [DeepSeek-V4-Flash-0731 vs LongCat-2.5](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-longcat-2-5.md)\n- [Devstral Small 2 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/devstral-small-2-vs-hy-memory-30b-a3b.md)\n- [Devstral Small 2 vs LongCat-2.5](https://www.anchorterminal.com/compare/devstral-small-2-vs-longcat-2-5.md)\n- [DiffusionGemma 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [DiffusionGemma 26B A4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-longcat-2-5.md)\n- [EXAONE 4.5 33B vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-hy-memory-30b-a3b.md)\n- [EXAONE 4.5 33B vs LongCat-2.5](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-longcat-2-5.md)\n- [Gemma 4 12B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 12B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-longcat-2-5.md)\n- [Gemma 4 26B A4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 26B A4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-longcat-2-5.md)\n- [Gemma 4 31B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 31B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-longcat-2-5.md)\n- [Gemma 4 E2B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E2B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-longcat-2-5.md)\n- [Gemma 4 E4B IT vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy-memory-30b-a3b.md)\n- [Gemma 4 E4B IT vs LongCat-2.5](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-longcat-2-5.md)\n- [GLM-4.5-Air vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy-memory-30b-a3b.md)\n- [GLM-4.5-Air vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-4-5-air-vs-longcat-2-5.md)\n- [GLM-4.7-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-4.7-Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-longcat-2-5.md)\n- [GLM-5.3-Flash vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3-Flash vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-longcat-2-5.md)\n- [GLM-5.3 vs Hy-Memory-30B-A3B](https://www.anchorterminal.com/compare/glm-5-3-vs-hy-memory-30b-a3b.md)\n- [GLM-5.3 vs LongCat-2.5](https://www.anchorterminal.com/compare/glm-5-3-vs-longcat-2-5.md)\n- [gpt-oss-120b vs 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35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minicpm5-2b.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-5.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m2-7.md)\n- [Hy-Memory-30B-A3B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-minimax-m3-1-flash.md)\n- [Hy-Memory-30B-A3B vs Mistral Large 4](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mistral-large-4.md)\n- [Hy-Memory-30B-A3B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-muse-glimmer-30b.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-2-3b.md)\n- [Hy-Memory-30B-A3B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nanbeige-4-3-33b.md)\n- [Hy-Memory-30B-A3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-30b-a3b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-4b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-super-120b-a12b.md)\n- [Hy-Memory-30B-A3B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Hy-Memory-30B-A3B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-north-mini-code-1-0.md)\n- [Hy-Memory-30B-A3B vs occamy-1.0](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.md)\n- [Hy-Memory-30B-A3B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b.md)\n- [Hy-Memory-30B-A3B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b.md)\n- [Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.md)\n- [Hy-Memory-30B-A3B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Hy-Memory-30B-A3B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b.md)\n- [Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.md)\n- [Hy-Memory-30B-A3B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Hy-Memory-30B-A3B vs 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