{
  "data": {
    "a": {
      "slug": "apodex-1-1-mini",
      "name": "Apodex-1.1-mini",
      "vendor": "Apodex AI",
      "vendorUrl": "https://www.apodex.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini",
      "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json",
      "dns": "apodex-1-1-mini.grade.anchorterminal.com",
      "repo": "https://huggingface.co/apodex/Apodex-1.1-mini",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-FP8"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-GPTQ-Int4"
        },
        {
          "registry": "huggingface",
          "name": "apodex/Apodex-1.1-mini-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (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. Apodex's hosted API bills by account (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "gptq",
        "self-hosted"
      ],
      "lastRelease": "2026-08-25",
      "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": 31,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 85,
          "risk": 65,
          "upkeep": 38
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.",
        "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence",
        "strengths": [
          "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls",
          "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE",
          "Apache 2.0 through the chain, with the base's licence file carried and no gate",
          "Apodex's own API serves the same model",
          "Safetensors and GGUF only, with checksums on every weight file"
        ],
        "weaknesses": [
          "No safety results, and agent guidance only from the harness",
          "Apodex answered none of the four discussions",
          "The card states no parameters, architecture, limitations or knowledge cut-off",
          "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default",
          "Structured output isn't documented"
        ],
        "agentNotes": [
          "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed",
          "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file",
          "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks",
          "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768",
          "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block",
          "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "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": 85,
          "reach": 85,
          "risk": 65,
          "upkeep": 38
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n  --tool-call-parser qwen3_coder --reasoning-parser qwen3",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/apodex-1-1-mini"
      },
      "weights": {
        "trainedBy": "Apodex AI",
        "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Apodex-1.1-mini-NVFP4",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 24.3,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang"
          },
          {
            "name": "Apodex-1.1-mini-GPTQ-Int4",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4",
            "who": "publisher",
            "precision": "int4",
            "sizeGB": 24.7,
            "memoryGB": 32,
            "format": "safetensors",
            "runsOn": [
              "SGLang"
            ],
            "note": "The card serves it with SGLang"
          },
          {
            "name": "apodex1.1-35b-Q4_K_M-MTP",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 22.6,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server"
          },
          {
            "name": "Apodex-1.1-mini-FP8",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 37.5,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Apodex-1.1-mini",
            "url": "https://huggingface.co/apodex/Apodex-1.1-mini",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 71.9,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3",
        "runBuild": "Apodex-1.1-mini",
        "checked": "2026-10-21",
        "minMemoryGB": 32
      },
      "family": {
        "lab": "Apodex AI (apodex)",
        "family": "Apodex-1",
        "url": "https://www.anchorterminal.com/open-weights/#apodex",
        "others": [
          {
            "name": "Apodex-1.0-mini",
            "url": "https://huggingface.co/apodex/Apodex-1.0-mini",
            "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-4B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT",
            "params": "4B (4,659,865,088 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-2B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT",
            "params": "2B (2,274,069,824 by the Hub's safetensors count)"
          },
          {
            "name": "Apodex-1.0-0.8B-SFT",
            "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT",
            "params": "0.8B (873,438,784 by the Hub's safetensors count)"
          }
        ],
        "unrestrictedCopies": 2
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)",
        "domain": "apodex.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.",
          "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.",
          "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.",
          "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.",
          "unchecked: the domain's registration date."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json",
      "live": {
        "slug": "apodex-1-1-mini",
        "securityTxt": {
          "url": "https://apodex.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:09:39.653671365Z"
        },
        "domain": {
          "domain": "apodex.com",
          "registered": "2018-10-10",
          "source": "https://rdap.verisign.com/com/v1/domain/apodex.com",
          "checkedAt": "2026-11-08T14:48:24.412328563Z"
        },
        "updatedAt": "2026-11-10T16:09:39.653671365Z"
      }
    },
    "answer": "Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep.",
    "b": {
      "slug": "hy-memory-30b-a3b",
      "name": "Hy-Memory-30B-A3B",
      "vendor": "Tencent (Hy team)",
      "vendorUrl": "https://aistudio.tencent.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Hy-Memory-30B-A3B is Tencent Hunyuan's open-weight memory writer, a 30B mixture of experts with about 3B active, trained to decide what an agent's memory framework keeps and updates inside Mem0, MemOS and Hy-Memory, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b",
      "markdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json",
      "dns": "hy-memory-30b-a3b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "tencent/Hy-Memory-30B-A3B-FP8"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main, FP8 and GGUF repositories are not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 60.1 GB for the BF16 files, 30.6 GB in FP8 or 18.2 GB for Tencent's Q4_K_M GGUF (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "agent-memory",
        "specialist",
        "apache-2.0",
        "safetensors",
        "fp8",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-10-16",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 54,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.",
        "bestFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "strengths": [
          "Made for one job, deciding what a memory framework stores and updates, and tested in four frameworks",
          "Fits a 24 GB card from Tencent's own Q4_K_M GGUF",
          "Runs in llama.cpp, vLLM, SGLang and Transformers from their releases",
          "Plain Apache 2.0, ungated, safetensors and GGUF only",
          "Tencent replied to three of the four discussions"
        ],
        "weaknesses": [
          "Not a general assistant or a tool caller",
          "No limitations, safety results or guidance on untrusted text in what it reads",
          "Training data isn't described, and the card gives no context length",
          "The results are on a 20% subsample, run by Tencent",
          "No inference provider, so it has to be run locally"
        ],
        "agentNotes": [
          "Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user",
          "Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does",
          "Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does",
          "Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented",
          "Treat what it stores as unverified. It keeps what a conversation says, true or not"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 65,
          "reach": 85,
          "risk": 60,
          "upkeep": 68
        },
        "provenanceScore": 75,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja\n# or, with vLLM, as the card names it:\nvllm serve tencent/Hy-Memory-30B-A3B --served-model-name hy-memory",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"hy-memory\", \"temperature\": 0, \"messages\": [{\"role\": \"system\", \"content\": \"Extract explicit user facts and preferences. Return only a JSON object with a memories list of strings.\"}, {\"role\": \"user\", \"content\": \"I live in Seattle and I am vegetarian.\"}], \"chat_template_kwargs\": {\"reasoning_effort\": \"no_think\"}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/hy-memory-30b-a3b"
      },
      "sameCompany": [
        "hy-memory",
        "tencentdb-agent-memory",
        "hunyuanocr",
        "hy3",
        "wevisone-vl-2b",
        "wevisone-vl-4b"
      ],
      "weights": {
        "trainedBy": "Tencent (Hunyuan)",
        "builtOn": "Not declared. The architecture is Tencent's hy_v3, as Hy3",
        "params": "30.1B",
        "active": "3B",
        "context": 262144,
        "goodFor": "The write path of an agent's memory framework. It reads a conversation and decides what to store and how to update it, in Mem0, MemOS or Hy-Memory, in place of a general model.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Hy-Memory-30B-A3B-Q4_K_M",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 18.24,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "The smallest file at 4 bits. Q3_K_M (14.45 GB) and the two files with experts at 1 or 2 bits are below the 4-bit line and are not listed"
          },
          {
            "name": "Hy-Memory-30B-A3B-Q6_K",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q6_k",
            "sizeGB": 24.71,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-FP8",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 30.58,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B-Q8_0",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 31.99,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ]
          },
          {
            "name": "Hy-Memory-30B-A3B",
            "url": "https://huggingface.co/tencent/Hy-Memory-30B-A3B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 60.13,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja",
        "runBuild": "Hy-Memory-30B-A3B-Q4_K_M",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Tencent (Hunyuan / Hy)",
        "family": "Hy-Memory",
        "url": "https://www.anchorterminal.com/open-weights/#tencent",
        "olderCount": 22
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Tencent (named in the LICENSE file's copyright line)",
        "domain": "tencent.com",
        "domainRegistered": "1998-09-14",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/tencent/Hy-Memory-30B-A3B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text, as on Hy3.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, five commits on 15 and 16 October 2026, the uploads, the card and the licence.",
          "The Hy3 listing found no security.txt on Tencent's domains and records Tencent's Security Response Center as the reporting route. unchecked on 10 November."
        ],
        "score": 75
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/hy-memory-30b-a3b.json"
    },
    "edges": [
      {
        "by": 30,
        "category": "upkeep",
        "leads": "hy-memory-30b-a3b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.",
          "b": "Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68."
        },
        "scores": {
          "a": 38,
          "b": 68
        }
      },
      {
        "by": 20,
        "category": "interface",
        "leads": "apodex-1-1-mini",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.",
          "b": "Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65."
        },
        "scores": {
          "a": 85,
          "b": 65
        }
      },
      {
        "by": 5,
        "category": "risk",
        "leads": "apodex-1-1-mini",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.",
          "b": "File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60."
        },
        "scores": {
          "a": 65,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Apodex AI",
        "b": "Tencent (Hy team)",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "",
        "b": "",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-08-25",
        "b": "2026-10-16",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Apodex-1.1-mini or Hy-Memory-30B-A3B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 60",
          "Agent interface, 85 against 65"
        ],
        "also": null,
        "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence",
        "slug": "apodex-1-1-mini",
        "watchFor": "No safety results, and agent guidance only from the harness"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 68 against 38"
        ],
        "also": null,
        "goodFor": "Agents with an external memory framework that want a local model for the write path on a 24 GB card",
        "slug": "hy-memory-30b-a3b",
        "watchFor": "Not a general assistant or a tool caller"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.json",
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        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini"
      },
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b.json",
        "title": "Agents-A1-4B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json",
        "title": "Apertus v1.5 8B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini"
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-flash-0731"
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.json",
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-diffusiongemma-26b-a4b-it"
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        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.json",
        "title": "Hy-Memory-30B-A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think.json",
        "title": "Hy-Memory-30B-A3B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-122B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-122b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it.json",
        "title": "Hy-Memory-30B-A3B vs OTel-2.0-LLM-31B-IT",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-otel-2-0-llm-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b.json",
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        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen-agentworld-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-30b-a3b-instruct-2507"
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      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct.json",
        "title": "Hy-Memory-30B-A3B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Hy-Memory-30B-A3B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint.json",
        "title": "Hy-Memory-30B-A3B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview.json",
        "title": "Hy-Memory-30B-A3B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview.json",
        "title": "Hy-Memory-30B-A3B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b.json",
        "title": "Hy-Memory-30B-A3B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b.json",
        "title": "Hy-Memory-30B-A3B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.json",
        "title": "Hy-Memory-30B-A3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Hy-Memory-30B-A3B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Hy-Memory-30B-A3B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2.json",
        "title": "Hy-Memory-30B-A3B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b.json",
        "title": "Hy-Memory-30B-A3B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b.json",
        "title": "Hy-Memory-30B-A3B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "apodex-1-1-mini": 85,
        "by": 0,
        "edge": "",
        "hy-memory-30b-a3b": 85,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "apodex-1-1-mini": 65,
        "by": 5,
        "edge": "apodex-1-1-mini",
        "hy-memory-30b-a3b": 60,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "apodex-1-1-mini": 85,
        "by": 20,
        "edge": "apodex-1-1-mini",
        "hy-memory-30b-a3b": 65,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "apodex-1-1-mini": 38,
        "by": 30,
        "edge": "hy-memory-30b-a3b",
        "hy-memory-30b-a3b": 68,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.",
      "hy-memory-30b-a3b": "A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b",
    "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.min.md"
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  "markdown": "Apodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Apodex-1.1-mini: grade C, 61/100, rank #41 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\n- Hy-Memory-30B-A3B: grade C, 61/100, rank #68 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/hy-memory-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/hy-memory-30b-a3b.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Risk, 65 against 60\n- Agent interface, 85 against 65\n\nWatch for: No safety results, and agent guidance only from the harness\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- Documentation and upkeep, 68 against 38\n\nWatch for: Not a general assistant or a tool caller\n\n\n## Why the edge\n\n### Documentation and upkeep: Hy-Memory-30B-A3B +30\n\n- Apodex-1.1-mini (38): Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.\n- Hy-Memory-30B-A3B (68): Model card 8, Intended use (the write path of Mem0, MemOS and Hy-Memory), parameters (30B, about 3B active) and languages (English and Chinese, in the metadata) are there. Training data 5, A general description of the two training stages inside the harnesses, with no datasets, sizes or cut-off. Last change 20, the last commit, on 16 October 2026, added the licence and the card, 25 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (16 to 30 October 2026), 3 have a reply from a tencent organisation member. Version policy 5, A first release, with nothing replaced. File health 10, Config, tokenizer and template agree on the architecture, the context and the thinking levels. 8 + 5 + 20 + 20 + 5 + 10 = 68.\n\n### Agent interface: Apodex-1.1-mini +20\n\n- Apodex-1.1-mini (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.\n- Hy-Memory-30B-A3B (65): Chat template 20, chat_template.jinja in the repository. Tool-calling format 10, the card doesn't mention tools. Trained tool calling 10, the template can render tool calls, but the card presents the model as a memory writer that returns memories as text or JSON and says nothing of calling tools. Structured output 5, the card's quickstart asks for a JSON object with a memories list in the system prompt and shows nothing else, no response_format and no schema, so JSON output is shown by example without a documented way to constrain it. Reasoning control 10, the card passes reasoning_effort as no_think per request through chat_template_kwargs, and the template takes high, low or no_think. Context 10, 262,144 tokens native (max_position_embeddings in config.json). 20 + 10 + 10 + 5 + 10 + 10 = 65.\n\n### Risk: Apodex-1.1-mini +5\n\n- Apodex-1.1-mini (65): File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.\n- Hy-Memory-30B-A3B (60): File safety 25, 51 safetensors shards and no pickle or Python files. Provenance 15, files in the tencent organisation that the card's own resource list links to (10), with a SHA-256 listed for all 53 large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, no prompt-injection guidance, instruction hierarchy, account of trained refusals or general cautions on the card. Licence risk 15, Apache 2.0 with no use policy, no field-of-use limit, no threshold and no clause letting Tencent change the terms. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide with published reward rules, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Hy-Memory-30B-A3B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 85 | even |\n| Risk | 20% (20 this run) | 65 | 60 | Apodex-1.1-mini +5 |\n| Agent interface | 10% (10 this run) | 85 | 65 | Apodex-1.1-mini +20 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 68 | Hy-Memory-30B-A3B +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Hy-Memory-30B-A3B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | Tencent (Hy team) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports |  |  |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 2026-10-16 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.3/5 (4) | none |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\n\n**Hy-Memory-30B-A3B.** A specialist that writes an agent's long-term memory, built to slot into Mem0, MemOS or Tencent's own framework in place of a general model. It fits a 24 GB card from Tencent's own GGUF, under plain Apache 2.0, and Tencent answers on the repository. Its results are Tencent's own and nobody has rerun them.\n\n## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\n\n### Hy-Memory-30B-A3B\n\n1. Use it as the writer model in Mem0, MemOS or Hy-Memory, not as the model that answers the user\n2. Start it with `llama-server -hf tencent/Hy-Memory-30B-A3B-GGUF:Q4_K_M --jinja`, or with vLLM under the served name hy-memory as the card does\n3. Pass `reasoning_effort` as no_think in `chat_template_kwargs` and temperature 0, as the card's example does\n4. Ask for a JSON object in the system prompt and validate it yourself, since no schema constraint is documented\n5. Treat what it stores as unverified. It keeps what a conversation says, true or not\n\n## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Hy-Memory-30B-A3B?\n\nApodex-1.1-mini and Hy-Memory-30B-A3B score within a point of each other on agent readiness, 61 (C) and 61 (C). Hy-Memory-30B-A3B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy-memory-30b-a3b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"hy-memory-30b-a3b\"}`. 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