{
  "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 and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk and agent interface.",
    "b": {
      "slug": "mimo-v2-6-distill-qwen-9b",
      "name": "MiMo-V2.6 Distill Qwen 9B",
      "vendor": "Xiaomi (MiMo team)",
      "vendorUrl": "https://mimo.xiaomi.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiMo-V2.6 Distill Qwen 9B is Xiaomi's open-weight 9B agentic model, Qwen3.5-9B fine-tuned on data generated by MiMo-V2.6 for coding, agent, visual coding and cybersecurity tasks, released in BF16 under MIT as a starting point for RL research.",
      "url": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b",
      "markdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json",
      "dns": "mimo-v2-6-distill-qwen-9b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "license": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under MIT on the card and Apache 2.0 for the Qwen3.5-9B base. Xiaomi's MiMo API serves the larger MiMo-V2.6 models, not this one. Running it yourself costs the hardware, 18.8 GB for the BF16 files (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-03"
      },
      "docsUrl": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "multimodal",
        "reasoning",
        "tool-calling",
        "derivative",
        "distillation",
        "mit",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-09-21",
      "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": 37,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.",
        "bestFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.",
        "strengths": [
          "Runs on Qwen3.5-9B's architecture, so released vLLM, SGLang, Transformers and llama.cpp load it, and Ollama's library carries it at 4-bit and 8-bit",
          "MIT over an Apache 2.0 base, no gate, no account and no use policy",
          "Safetensors only in Xiaomi's own organisation, with a checksum on every file",
          "The fine-tuning mix is published by domain with token counts, and a technical report covers it",
          "Several tool calls a turn, thinking switched per request and 262,144 tokens of context from the base"
        ],
        "weaknesses": [
          "Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's",
          "No safety results or agent guidance beyond general cautions, with 11.0B tokens of offensive-security tuning",
          "No reply from Xiaomi to any of its 14 discussions, including template and tool-call reports",
          "No LICENSE file and no copy of the base's Apache 2.0 text, and the card has no limitations, context, languages or cut-off",
          "The tool format and parser aren't documented on the card"
        ],
        "agentNotes": [
          "Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input",
          "Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls",
          "SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`",
          "Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read",
          "The generation config samples at temperature 0.6, top_k 20 and top_p 0.95",
          "Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them",
          "The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer",
          "It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens",
          "If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.3,
        "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": 70,
          "reach": 80,
          "risk": 65,
          "upkeep": 40
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B \\\n  --reasoning-parser mimo --tool-call-parser mimo --host 0.0.0.0 --port 30000\n# or, with llama.cpp and llama.cpp's own GGUF:\nllama-server -hf ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF:Q8_0 --jinja",
        "http": "curl http://localhost:30000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/mimo-v2-6-distill-qwen-9b"
      },
      "sameCompany": [
        "mimo-v2-6-flash-rl"
      ],
      "weights": {
        "trainedBy": "Xiaomi (MiMo team)",
        "builtOn": "Qwen/Qwen3.5-9B (supervised fine-tune on data generated by MiMo-V2.6; base Apache 2.0, declared in the card's metadata)",
        "params": "9.41B",
        "context": 262144,
        "goodFor": "A 9B agentic model for coding, terminal and tool tasks, with images in, meant as a starting point for RL fine-tuning.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "ollama mimo-v2.6:9b-q4_K_M",
            "url": "https://ollama.com/library/mimo-v2.6:9b-q4_K_M",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 6.6,
            "memoryGB": 8,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Size as Ollama's tags page shows it (the default 9b tag is listed at 6.6 to 8.7 GB across formats), text and image input, 256K context. Ollama also lists 9b-q8_0 (10 GB), 9b-mxfp8 (11 GB), 9b-nvfp4 (8.7 GB) and BF16 GGUF and MLX builds (19 GB)"
          },
          {
            "name": "ggml-org MiMo-V2.6-Distill-Qwen-9B-GGUF (Q8_0)",
            "url": "https://huggingface.co/ggml-org/MiMo-V2.6-Distill-Qwen-9B-GGUF",
            "who": "llama.cpp",
            "precision": "q8_0",
            "sizeGB": 10.15,
            "memoryGB": 16,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From ggml-org, the llama.cpp project's organisation. 9.53 GB of Q8_0 weights plus the 0.62 GB Q8_0 vision projector, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it yet"
          },
          {
            "name": "MiMo-V2.6-Distill-Qwen-9B",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 18.85,
            "memoryGB": 24,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "Xiaomi's only build"
          }
        ],
        "run": "sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo --tool-call-parser mimo --context-length 131072 --port 30000",
        "runBuild": "MiMo-V2.6-Distill-Qwen-9B",
        "checked": "2026-11-03",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "Xiaomi (MiMo)",
        "family": "MiMo-V2 (V2, V2.5, V2.6)",
        "url": "https://www.anchorterminal.com/open-weights/#xiaomi",
        "others": [
          {
            "name": "MiMo-V2.6-Pro-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-MOPD",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-MOPD",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Pro-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL",
            "params": "1024.22B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.6-Flash-RL",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL",
            "params": "310.76B (Hub safetensors count; the name states none)",
            "listing": "https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl"
          },
          {
            "name": "MiMo-V2.5-Pro-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Pro",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro",
            "params": "1023.18B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5",
            "params": "310.76B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2.5-ASR",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2.5-ASR",
            "params": "7.62B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash-Base",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base",
            "params": "309.77B (Hub safetensors count; the name states none)"
          },
          {
            "name": "MiMo-V2-Flash",
            "url": "https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash",
            "params": "309.77B (Hub safetensors count; the name states none)"
          }
        ],
        "olderCount": 12,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Xiaomi (the card and report name Xiaomi MiMo; no LICENSE file names a copyright holder)",
        "domain": "mimo.xiaomi.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-11-03",
        "notes": [
          "The Hugging Face organisation XiaomiMiMo isn't verified. mimo.xiaomi.com links to it, and the MiMo-V2.6 technical report links this repository.",
          "Nothing is hosted for this model, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog. The six commits are the upload on 21 September 2026, a README update on 22 September and two README updates on 28 October.",
          "mimo.xiaomi.com has no security.txt (404) and www.mi.com's returns 403. Xiaomi runs a company security centre at sec.xiaomi.com, which no MiMo page links."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.json",
      "live": {
        "slug": "mimo-v2-6-distill-qwen-9b",
        "securityTxt": {
          "url": "https://mimo.xiaomi.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:46.192470354Z"
        },
        "domain": {
          "domain": "xiaomi.com",
          "registered": "2003-07-22",
          "source": "https://rdap.verisign.com/com/v1/domain/xiaomi.com",
          "checkedAt": "2026-11-08T15:08:19.964915329Z"
        },
        "updatedAt": "2026-11-10T16:07:46.192470354Z"
      }
    },
    "edges": [
      {
        "by": 28,
        "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 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply."
        },
        "scores": {
          "a": 68,
          "b": 40
        }
      },
      {
        "by": 5,
        "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 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill."
        },
        "scores": {
          "a": 85,
          "b": 80
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "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 repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found."
        },
        "scores": {
          "a": 60,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "mimo-v2-6-distill-qwen-9b",
        "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. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say."
        },
        "scores": {
          "a": 65,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Tencent (Hy team)",
        "b": "Xiaomi (MiMo team)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-16",
        "b": "2026-09-21",
        "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": "2.3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Hy-Memory-30B-A3B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk and agent interface.",
        "question": "Which is better for AI agents, Hy-Memory-30B-A3B or MiMo-V2.6 Distill Qwen 9B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 85 against 80",
          "Documentation and upkeep, 68 against 40"
        ],
        "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": [
          "Risk, 65 against 60",
          "Agent interface, 70 against 65"
        ],
        "also": null,
        "goodFor": "Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.",
        "slug": "mimo-v2-6-distill-qwen-9b",
        "watchFor": "Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's"
      }
    ],
    "history": {
      "hy-memory-30b-a3b": null,
      "mimo-v2-6-distill-qwen-9b": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        }
      ]
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-zaya1-8b.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 85,
        "key": "reach",
        "mimo-v2-6-distill-qwen-9b": 80,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 5,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "hy-memory-30b-a3b": 60,
        "key": "risk",
        "mimo-v2-6-distill-qwen-9b": 65,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 5,
        "edge": "mimo-v2-6-distill-qwen-9b",
        "hy-memory-30b-a3b": 65,
        "key": "interface",
        "mimo-v2-6-distill-qwen-9b": 70,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 28,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 68,
        "key": "upkeep",
        "mimo-v2-6-distill-qwen-9b": 40,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Hy-Memory-30B-A3B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk and 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.",
      "mimo-v2-6-distill-qwen-9b": "A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b",
    "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.min.md"
  },
  "markdown": "Hy-Memory-30B-A3B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk and 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- MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #47 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.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 80\n- Documentation and upkeep, 68 against 40\n\nWatch for: Not a general assistant or a tool caller\n\n### MiMo-V2.6 Distill Qwen 9B (C)\n\nGood for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.\n\nAhead on:\n- Risk, 65 against 60\n- Agent interface, 70 against 65\n\nWatch for: Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's\n\n\n## Why the edge\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +28\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- MiMo-V2.6 Distill Qwen 9B (40): Model card 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply.\n\n### Reach: Hy-Memory-30B-A3B +5\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- MiMo-V2.6 Distill Qwen 9B (80): Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.\n\n### Risk: MiMo-V2.6 Distill Qwen 9B +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- MiMo-V2.6 Distill Qwen 9B (65): File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.\n\n### Agent interface: MiMo-V2.6 Distill Qwen 9B +5\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- MiMo-V2.6 Distill Qwen 9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.\n\n## Score by category\n\n| Category | Weight | Hy-Memory-30B-A3B | MiMo-V2.6 Distill Qwen 9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 80 | Hy-Memory-30B-A3B +5 |\n| Risk | 20% (20 this run) | 60 | 65 | MiMo-V2.6 Distill Qwen 9B +5 |\n| Agent interface | 10% (10 this run) | 65 | 70 | MiMo-V2.6 Distill Qwen 9B +5 |\n| Documentation and upkeep | 10% (10 this run) | 68 | 40 | Hy-Memory-30B-A3B +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Hy-Memory-30B-A3B | MiMo-V2.6 Distill Qwen 9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | Xiaomi (MiMo team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-16 | 2026-09-21 |\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 | 2.3/5 (4) |\n\n## How the scores have moved\n\n### Hy-Memory-30B-A3B\n\nNo score change since it was listed.\n\n### MiMo-V2.6 Distill Qwen 9B\n\n- no change on 3 November 2026: product\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**MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.\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### MiMo-V2.6 Distill Qwen 9B\n\n1. Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input\n2. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls\n3. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`\n4. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read\n5. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95\n6. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them\n7. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer\n8. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens\n9. If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks\n\n## Questions\n\n### Which is better for AI agents, Hy-Memory-30B-A3B or MiMo-V2.6 Distill Qwen 9B?\n\nHy-Memory-30B-A3B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-mimo-v2-6-distill-qwen-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy-memory-30b-a3b\", \"b\": \"mimo-v2-6-distill-qwen-9b\"}`. From a terminal: `anchor compare hy-memory-30b-a3b mimo-v2-6-distill-qwen-9b`\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/mimo-v2-6-distill-qwen-9b.json\n\n## Other comparisons with Hy-Memory-30B-A3B or MiMo-V2.6 Distill Qwen 9B\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 MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b.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 MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b.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 MiMo-V2.6 Distill 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