{
  "data": {
    "a": {
      "slug": "arete-27b",
      "name": "Arete-27B",
      "vendor": "Nous Research",
      "vendorUrl": "https://nousresearch.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/arete-27b",
      "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json",
      "dns": "arete-27b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/NousResearch/Arete-27B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "NousResearch/Arete-27B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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/NousResearch/Arete-27B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "fine-tune",
        "qwen",
        "multimodal",
        "vision",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "self-hosted"
      ],
      "lastRelease": "2026-11-06",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.1,
        "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": 92,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 63
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.",
        "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine",
        "strengths": [
          "The training pairs and the evaluation set are both published",
          "Apache 2.0, ungated, safetensors only, no remote code",
          "The base's architecture, so vLLM, SGLang and Transformers run it from their releases",
          "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF",
          "Nous Research reports no meaningful loss against the base on its agent benchmarks"
        ],
        "weaknesses": [
          "No quantised build from Nous Research, so the GGUF route depends on a third party",
          "The card doesn't document tool calling, structured output or how to switch thinking",
          "No limitations section, safety results or security contact",
          "None of the 13 discussions has a reply from Nous Research",
          "The results are Nous Research's own evaluation and nobody else has rerun it yet"
        ],
        "agentNotes": [
          "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM",
          "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's",
          "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off",
          "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 55.1
          }
        ],
        "editorialScores": {
          "interface": 60,
          "reach": 50,
          "risk": 55,
          "upkeep": 63
        },
        "provenanceScore": 61,
        "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": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/arete-27b"
      },
      "sameCompany": [
        "hermes-agent"
      ],
      "weights": {
        "trainedBy": "Nous Research",
        "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher",
        "params": "27.4B",
        "context": 262144,
        "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 18.13,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 29.6,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it"
          },
          {
            "name": "Arete-27B",
            "url": "https://huggingface.co/NousResearch/Arete-27B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 54.7,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ],
            "note": "The only build Nous Research publishes"
          }
        ],
        "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja",
        "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)",
        "checked": "2026-11-10",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Nous Research (NousResearch)",
        "family": "Arete",
        "url": "https://www.anchorterminal.com/open-weights/#nous-research"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)",
        "domain": "nousresearch.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.",
          "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, eight commits on 6 November 2026, the upload and updates to the card.",
          "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.",
          "unchecked: the domain's registration date."
        ],
        "score": 61
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json"
    },
    "answer": "occamy-1.0 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories.",
    "b": {
      "slug": "occamy-1-0",
      "name": "occamy-1.0",
      "vendor": "Accio (Accio-Lab)",
      "vendorUrl": "https://accio-lab.github.io/occamy/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Occamy-1.0 is Accio's open-weight agent model for long multi-step work with tools, files and code, a fine-tune of Qwen3.6-35B-A3B (35B mixture of experts, 3B active) published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/occamy-1-0",
      "markdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/occamy-1-0.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json",
      "dns": "occamy-1-0.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-FP8"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-NVFP4"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-APEX-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-4bit"
        },
        {
          "registry": "huggingface",
          "name": "Accio-Lab/occamy-1.0-MLX-mxfp4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all of Accio's build repositories are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, the same licence as the Qwen3.6-35B-A3B base. Running it yourself costs the hardware, 70.2 GB for BF16 and 18.4 GB for the smallest 4-bit build (checked 2026-10-21).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-21"
      },
      "docsUrl": "https://huggingface.co/Accio-Lab/occamy-1.0",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agent",
        "coding",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "mlx",
        "fp8",
        "nvfp4",
        "self-hosted"
      ],
      "lastRelease": "2026-10-02",
      "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": 49,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 74
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.",
        "bestFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "strengths": [
          "Apache 2.0 like its base, with nothing gated",
          "Accio publishes FP8, NVFP4, GGUF and MLX builds, the 4-bit ones within 24 GB",
          "Same architecture and template as Qwen3.6-35B-A3B, so released llama.cpp, Ollama, vLLM, SGLang and Transformers run it",
          "A technical report, the supervised data mix with token counts and a published data subset",
          "MD5 and SHA-256 checksums for every file"
        ],
        "weaknesses": [
          "No safety results, prompt-injection guidance or security contact",
          "No hosted API or inference provider",
          "Tool-calling format and structured output aren't documented beyond the template and parser name",
          "Languages and a knowledge cut-off aren't stated",
          "The MLX builds await Apple Metal acceptance, per Accio's own cards"
        ],
        "agentNotes": [
          "Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later",
          "BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism",
          "Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says",
          "Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5",
          "With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata",
          "The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head",
          "Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 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": 90,
          "risk": 55,
          "upkeep": 74
        },
        "provenanceScore": 24,
        "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": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or a 4-bit GGUF with llama.cpp:\nllama-server -hf Accio-Lab/occamy-1.0-GGUF:Q4_K_M --jinja -c 131072",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"Accio-Lab/occamy-1.0\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/occamy-1-0"
      },
      "weights": {
        "trainedBy": "Accio (Accio-Lab)",
        "builtOn": "Qwen/Qwen3.6-35B-A3B (Apache 2.0), declared in the card's metadata (base_model) and text as the post-trained starting checkpoint. Same qwen3_5_moe architecture and config apart from the removed MTP layer, and the same chat template. Occamy is also Apache 2.0",
        "params": "35B (3B active)",
        "active": "3B",
        "context": 262144,
        "goodFor": "Mixture-of-experts agent model for long multi-step work with tools, files, code and office tasks, text and images in, thinking on by default.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "occamy-1.0-MLX-mxfp4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-mxfp4",
            "who": "publisher",
            "precision": "mxfp4",
            "sizeGB": 18.4,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-MLX-4bit",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MLX-4bit",
            "who": "publisher",
            "precision": "mlx-4bit",
            "sizeGB": 19.5,
            "memoryGB": 24,
            "format": "mlx",
            "runsOn": [
              "MLX"
            ],
            "note": "Text only. Apple Metal acceptance pending, per the card"
          },
          {
            "name": "occamy-1.0-IQ4_XS",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-IQ4_XS.gguf",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 18.7,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Image input needs the 0.90 GB mmproj file. Input should be NFC-normalised, per the card"
          },
          {
            "name": "occamy-1.0-APEX-Compact",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-APEX-GGUF/blob/main/occamy-1.0-APEX-Compact.gguf",
            "who": "publisher",
            "precision": "mixed-q3-q4",
            "sizeGB": 16.5,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Most routed experts at Q3_K, so it doesn't count as a 4-bit build for the memory line"
          },
          {
            "name": "occamy-1.0-NVFP4",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 23.9,
            "memoryGB": 32,
            "format": "safetensors",
            "note": "Expert-only mixed precision, checked on H200 with Marlin W4A16, per the card"
          },
          {
            "name": "occamy-1.0-Q4_K_M",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q4_K_M.gguf",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 21.2,
            "memoryGB": 32,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0-FP8",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 36.6,
            "memoryGB": 48,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "occamy-1.0-Q8_0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-GGUF/blob/main/occamy-1.0-Q8_0.gguf",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 36.9,
            "memoryGB": 48,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ]
          },
          {
            "name": "occamy-1.0",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 70.2,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          }
        ],
        "run": "vllm serve Accio-Lab/occamy-1.0 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "occamy-1.0",
        "checked": "2026-11-03",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Accio (Accio-Lab)",
        "family": "occamy",
        "url": "https://www.anchorterminal.com/open-weights/#accio-lab",
        "others": [
          {
            "name": "occamy-1.0-MTP",
            "url": "https://huggingface.co/Accio-Lab/occamy-1.0-MTP",
            "params": "not stated (draft head only)"
          }
        ],
        "olderCount": 4,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "accio-lab.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "No legal entity is named on the card, the report or the repository. The report's byline is Accio Team, and the Hugging Face organisation Accio-Lab (fullname Accio, 15 members) isn't verified. The GitHub README and the project website link the organisation.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The project website is a GitHub Pages site drawn by script, so its text couldn't be read beyond the title.",
          "No SECURITY.md in Accio-Lab/occamy. The README sends bug reports and feature requests to GitHub issues."
        ],
        "score": 24
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/occamy-1-0.json",
      "live": {
        "slug": "occamy-1-0",
        "securityTxt": {
          "url": "https://accio-lab.github.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:29.220890734Z"
        },
        "domain": {
          "domain": "github.io",
          "checkedAt": "2026-11-08T15:08:12.013832199Z"
        },
        "updatedAt": "2026-11-10T16:08:29.220890734Z"
      }
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "occamy-1-0",
        "name": "Reach",
        "reasons": {
          "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.",
          "b": "Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90."
        },
        "scores": {
          "a": 50,
          "b": 90
        }
      },
      {
        "by": 11,
        "category": "upkeep",
        "leads": "occamy-1-0",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.",
          "b": "Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74."
        },
        "scores": {
          "a": 63,
          "b": 74
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "occamy-1-0",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.",
          "b": "Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens."
        },
        "scores": {
          "a": 60,
          "b": 70
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nous Research",
        "b": "Accio (Accio-Lab)",
        "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": "2026-11-06",
        "b": "2026-10-02",
        "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": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "occamy-1.0 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories.",
        "question": "Which is better for AI agents, Arete-27B or occamy-1.0?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": null,
        "also": null,
        "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine",
        "slug": "arete-27b",
        "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party"
      },
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Agent interface, 70 against 60",
          "Documentation and upkeep, 74 against 63"
        ],
        "also": null,
        "goodFor": "Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.",
        "slug": "occamy-1-0",
        "watchFor": "No safety results, prompt-injection guidance or security contact"
      }
    ],
    "history": {
      "arete-27b": null,
      "occamy-1-0": [
        {
          "causes": [
            "correction"
          ],
          "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": [
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json",
        "title": "Agents-A1-4B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.json",
        "title": "Agents-A1-4B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json",
        "title": "Apertus v1.5 8B vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json",
        "title": "Apertus v1.5 8B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.json",
        "title": "Apodex-1.1-mini vs Arete-27B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.json",
        "title": "Apodex-1.1-mini vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.json",
        "title": "Arete-27B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.json",
        "title": "Arete-27B vs Carbon-X",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json",
        "title": "Arete-27B vs Cola2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.json",
        "title": "Arete-27B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.json",
        "title": "Arete-27B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.json",
        "title": "Arete-27B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.json",
        "title": "Arete-27B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.json",
        "title": "Arete-27B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json",
        "title": "Arete-27B vs Devstral Small 2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Arete-27B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json",
        "title": "Arete-27B vs EXAONE 4.5 33B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.json",
        "title": "Arete-27B vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.json",
        "title": "Arete-27B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.json",
        "title": "Arete-27B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json",
        "title": "Arete-27B vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.json",
        "title": "Arete-27B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.json",
        "title": "Arete-27B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.json",
        "title": "Arete-27B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.json",
        "title": "Arete-27B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.json",
        "title": "Arete-27B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.json",
        "title": "Arete-27B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.json",
        "title": "Arete-27B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json",
        "title": "Arete-27B vs Granite 4.2 30B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json",
        "title": "Arete-27B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json",
        "title": "Arete-27B vs Granite 4.2 8B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json",
        "title": "Arete-27B vs Hy-Memory-30B-A3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3.json",
        "title": "Arete-27B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling.json",
        "title": "Arete-27B vs Inkling",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small.json",
        "title": "Arete-27B vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.json",
        "title": "Arete-27B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.json",
        "title": "Arete-27B vs Kimi K3.1 Pro",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.json",
        "title": "Arete-27B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.json",
        "title": "Arete-27B vs Kronos",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.json",
        "title": "Arete-27B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json",
        "title": "Arete-27B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.json",
        "title": "Arete-27B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json",
        "title": "Arete-27B vs LBS-27B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.json",
        "title": "Arete-27B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.json",
        "title": "Arete-27B vs LFM3-1.7B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.json",
        "title": "Arete-27B vs LFM3-Audio-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.json",
        "title": "Arete-27B vs LFM3-Omni-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.json",
        "title": "Arete-27B vs LFM3-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.json",
        "title": "Arete-27B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.json",
        "title": "Arete-27B vs Ling-3.1-flash-1019",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json",
        "title": "Arete-27B vs LongCat-2.5",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Arete-27B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b"
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        "title": "Gemma 4 31B IT vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0.json",
        "title": "Gemma 4 E2B IT vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0.json",
        "title": "Gemma 4 E4B IT vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0.json",
        "title": "GLM-4.5-Air vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0.json",
        "title": "GLM-4.7-Flash vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0.json",
        "title": "GLM-5.3-Flash vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0.json",
        "title": "GLM-5.3 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0.json",
        "title": "gpt-oss-120b vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0.json",
        "title": "Granite 4.2 30B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-30b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0.json",
        "title": "Granite 4.2 3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-occamy-1-0.json",
        "title": "Granite 4.2 8B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/granite-4-2-8b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0.json",
        "title": "Hy-Memory-30B-A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json",
        "title": "Hy3 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0.json",
        "title": "Inkling-Small vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0.json",
        "title": "Inkling vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/inkling-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0.json",
        "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-occamy-1-0"
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        "title": "Kimi K3.1 Pro vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0.json",
        "title": "Kimi K3 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kimi-k3-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.json",
        "title": "Kolibri-1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0.json",
        "title": "Kronos vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/kronos-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json",
        "title": "Laguna M.1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0.json",
        "title": "Laguna S 2.1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/laguna-s-2-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0.json",
        "title": "Laguna XS 2.1 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0.json",
        "title": "LBS-27B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lbs-27b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json",
        "title": "LFM2.5-2.6B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0"
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      {
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        "title": "LFM3-1.7B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-1-7b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0.json",
        "title": "LFM3-Audio-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0.json",
        "title": "LFM3-Omni-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0.json",
        "title": "LFM3-VL-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0.json",
        "title": "Ling-3.0-tiny vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-occamy-1-0.json",
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        "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0.json",
        "title": "LongCat-2.5 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-occamy-1-0.json",
        "title": "MiMo-V2.6 Distill Qwen 9B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-occamy-1-0"
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        "title": "MiMo-V2.6-Flash-RL vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-occamy-1-0"
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        "title": "MiniCPM-o 4.5 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-occamy-1-0"
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        "title": "MiniCPM-V 4.7 1B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-1b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0.json",
        "title": "MiniCPM-V 4.7 35B-A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0.json",
        "title": "MiniCPM5-2B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0.json",
        "title": "MiniMax-M2.5 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0.json",
        "title": "MiniMax-M2.7 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-occamy-1-0"
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        "title": "Mistral Large 4 vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-occamy-1-0"
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        "title": "Muse Glimmer 30B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-occamy-1-0"
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        "title": "Nanbeige4.2-3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-occamy-1-0"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0.json",
        "title": "Nanbeige4.3-33B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-occamy-1-0.json",
        "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0.json",
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-occamy-1-0"
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        "title": "Nemotron 3 Nano 30B A3B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-occamy-1-0"
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        "title": "Nemotron 3 Super 120B A12B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-occamy-1-0"
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        "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-occamy-1-0"
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        "title": "occamy-1.0 vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-35b-a3b"
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      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next.json",
        "title": "occamy-1.0 vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct.json",
        "title": "occamy-1.0 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2.json",
        "title": "occamy-1.0 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint.json",
        "title": "occamy-1.0 vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview.json",
        "title": "occamy-1.0 vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview.json",
        "title": "occamy-1.0 vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b.json",
        "title": "occamy-1.0 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.json",
        "title": "occamy-1.0 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash.json",
        "title": "occamy-1.0 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b.json",
        "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "occamy-1.0 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b.json",
        "title": "occamy-1.0 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json",
        "title": "occamy-1.0 vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b.json",
        "title": "occamy-1.0 vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b.json",
        "title": "occamy-1.0 vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b.json",
        "title": "occamy-1.0 vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "arete-27b": 50,
        "by": 40,
        "edge": "occamy-1-0",
        "key": "reach",
        "name": "Reach",
        "occamy-1-0": 90,
        "weight": 25
      },
      {
        "arete-27b": 55,
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "occamy-1-0": 55,
        "weight": 20
      },
      {
        "arete-27b": 60,
        "by": 10,
        "edge": "occamy-1-0",
        "key": "interface",
        "name": "Agent interface",
        "occamy-1-0": 70,
        "weight": 10
      },
      {
        "arete-27b": 63,
        "by": 11,
        "edge": "occamy-1-0",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "occamy-1-0": 74,
        "weight": 10
      }
    ],
    "summary": "occamy-1.0 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.",
      "occamy-1-0": "A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0",
    "json": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.min.md"
  },
  "markdown": "occamy-1.0 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\n- occamy-1.0: grade C, 61/100, rank #59 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/occamy-1-0.md · JSON https://www.anchorterminal.com/api/v1/tools/occamy-1-0.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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\n\n### occamy-1.0 (C)\n\nGood for: Agents that want an Apache 2.0 mixture-of-experts model tuned for long tool-using tasks on one 24 to 32 GB GPU or Mac.\n\nAhead on:\n- Reach, 90 against 50\n- Agent interface, 70 against 60\n- Documentation and upkeep, 74 against 63\n\nWatch for: No safety results, prompt-injection guidance or security contact\n\n\n## Why the edge\n\n### Reach: occamy-1.0 +40\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n- occamy-1.0 (90): Memory 25 of 30, Accio's MLX mxfp4 build is 18.4 GB, 22.1 GB with a fifth added, and its IQ4_XS GGUF needs 22.5 GB, both within 24 GB. Runtimes 25, the most the line gives, it keeps the base's qwen3_5_moe architecture, which llama.cpp, vLLM, SGLang and Transformers run in released versions, and Ollama v0.40.14 builds a llama.cpp (b11963) that has it. Builds 15, FP8, NVFP4, GGUF and MLX from Accio. Terms 15, Apache 2.0 like its base. Download 10, nothing is gated. Hosted 0, no API or inference provider serves it. 25 + 25 + 15 + 15 + 10 + 0 = 90.\n\n### Documentation and upkeep: occamy-1.0 +11\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\n- occamy-1.0 (74): Model card 14 of 20, languages and a knowledge cut-off are missing. Training data 15, the mix by type with token counts, the method and a published subset, without a cut-off. Last change 20, Accio's GGUF build card changed on 19 October 2026 and the main card on 2 October. Responsiveness 10, Accio answered 4 of 9 questions. Version policy 5, a first release with no files replaced. File health 10, the files agree with the card. 14 + 15 + 20 + 10 + 5 + 10 = 74.\n\n### Agent interface: occamy-1.0 +10\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- occamy-1.0 (70): Chat template 20, the base's Jinja template in the repository. Tool-calling format 10 of 25, the card names the parser but the format lives only in the template. Trained tool calling 20, trained on tool use with several calls a turn. Structured output 0, not documented. Reasoning control 10, enable_thinking per request and the card says to keep the returned reasoning content. Context 10, 262,144 tokens.\n\n## Score by category\n\n| Category | Weight | Arete-27B | occamy-1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 90 | occamy-1.0 +40 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 60 | 70 | occamy-1.0 +10 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 74 | occamy-1.0 +11 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | occamy-1.0 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Accio (Accio-Lab) |\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 | 2026-11-06 | 2026-10-02 |\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 | 3/5 (4) |\n\n## How the scores have moved\n\n### Arete-27B\n\nNo score change since it was listed.\n\n### occamy-1.0\n\n- no change on 3 November 2026: correction\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\n\n**occamy-1.0.** A Qwen3.6-35B-A3B fine-tuned for long agent work with tools, files and code, under plain Apache 2.0, and Accio's 4-bit builds run in about 23 GB. The main gaps are no safety results, agent guidance or security contact, and every benchmark figure is Accio's own.\n\n## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\n\n### occamy-1.0\n\n1. Serve with `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder` on vLLM 0.19.0 or SGLang 0.5.10 and later\n2. BF16 is 70.2 GB, so on a 128 GB machine run it without the card's 8-way tensor parallelism\n3. Keep the whole assistant message, reasoning and tool calls included, in later turns, as the card says\n4. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 1.5\n5. With the GGUFs, NFC-normalise input text and read TOKENIZER.md, since the files store qwen2 pre-tokenizer metadata\n6. The base's MTP layer isn't in this checkpoint, Accio-Lab/occamy-1.0-MTP is an experimental separate head\n7. Ollama has no Occamy library entry, so load Accio's GGUF in Ollama 0.40.14 or later, which builds a llama.cpp with the qwen35moe architecture\n\n## Questions\n\n### Which is better for AI agents, Arete-27B or occamy-1.0?\n\noccamy-1.0 scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"occamy-1-0\"}`. From a terminal: `anchor compare arete-27b occamy-1-0`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/occamy-1-0.json\n\n## Other comparisons with Arete-27B or occamy-1.0\n\n- [Agents-A1-4B vs Arete-27B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md)\n- [Agents-A1-4B vs occamy-1.0](https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.md)\n- [Apertus v1.5 8B vs Arete-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.md)\n- [Apertus v1.5 8B vs occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md)\n- [Apodex-1.1-mini vs Arete-27B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.md)\n- [Apodex-1.1-mini vs occamy-1.0](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-occamy-1-0.md)\n- [Arete-27B vs BigBang-v1](https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.md)\n- [Arete-27B vs Carbon-X](https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.md)\n- [Arete-27B vs Cola2](https://www.anchorterminal.com/compare/arete-27b-vs-cola2.md)\n- [Arete-27B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.md)\n- [Arete-27B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.md)\n- [Arete-27B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.md)\n- [Arete-27B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-1-flash.md)\n- [Arete-27B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.md)\n- [Arete-27B vs Devstral Small 2](https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.md)\n- [Arete-27B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.md)\n- [Arete-27B vs EXAONE 4.5 33B](https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.md)\n- [Arete-27B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.md)\n- [Arete-27B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.md)\n- [Arete-27B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.md)\n- [Arete-27B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.md)\n- [Arete-27B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.md)\n- [Arete-27B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.md)\n- [Arete-27B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.md)\n- [Arete-27B vs GLM-5.3](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.md)\n- [Arete-27B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.md)\n- 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