{
  "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": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.",
    "b": {
      "slug": "gemma-4-26b-a4b-it",
      "name": "Gemma 4 26B A4B IT",
      "vendor": "Google DeepMind",
      "vendorUrl": "https://gemma.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it",
      "markdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json",
      "dns": "gemma-4-26b-a4b-it.grade.anchorterminal.com",
      "repo": "https://huggingface.co/google/gemma-4-26B-A4B-it",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it"
        },
        {
          "registry": "huggingface",
          "name": "google/gemma-4-26B-A4B-it-qat-q4_0-gguf"
        },
        {
          "registry": "ollama",
          "name": "gemma4:26b"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Google also hosts it as gemma-4-26b-a4b-it on the Gemini API, which needs an API key. Running it yourself costs the hardware, 57.7 GB at BF16 and 14.4 GB at Q4_0 per Google (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://gemma.ai/models/gemma-4/",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "reasoning",
        "tool-calling",
        "apache-2.0",
        "safetensors",
        "gguf",
        "self-hosted"
      ],
      "lastRelease": "2026-07-20",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 72.2,
        "grade": "BB",
        "agentReady": true,
        "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": 3,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-11-01"
        },
        "negative": 0,
        "verdict": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.",
        "bestFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "strengths": [
          "Plain Apache 2.0, no account or gate, safetensors and GGUF only with a checksum on every file",
          "Google's own 4-bit QAT GGUF needs about 17 GB with working space",
          "Released support in vLLM, SGLang, llama.cpp, Ollama, LM Studio and Transformers",
          "Tool declaration and call format documented by Google, with parallel calls and named parsers",
          "262,144 tokens of native context and thinking that turns on or off per request",
          "Also served by the Gemini API and four inference providers"
        ],
        "weaknesses": [
          "No safety evaluation figures published",
          "No documented way to ask for JSON or schema output",
          "Only one quantised precision from Google, no FP8",
          "Files unchanged since 20 July 2026, with chat-template bug reports unanswered",
          "Training data described only by type and cut-off"
        ],
        "agentNotes": [
          "On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images",
          "With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K",
          "In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text",
          "Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns",
          "Validate tool names and arguments before running them, and check any JSON yourself"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 72.2
          }
        ],
        "editorialScores": {
          "capability": 59,
          "interface": 85,
          "reach": 90,
          "risk": 70,
          "upkeep": 65
        },
        "provenanceScore": 72,
        "checked": {
          "categories": {
            "capability": "2026-11-01",
            "interface": "2026-11-01",
            "reach": "2026-11-01",
            "risk": "2026-11-01",
            "upkeep": "2026-11-01"
          },
          "last": "2026-11-01",
          "oldest": "2026-11-01",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "ollama run gemma4:26b\n# or, with vLLM:\nvllm serve google/gemma-4-26B-A4B-it --max-model-len 262144 --reasoning-parser gemma4 \\\n  --enable-auto-tool-choice --tool-call-parser gemma4",
        "http": "curl http://localhost:11434/api/chat \\\n  -d '{\"model\": \"gemma4:26b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"options\": {\"num_ctx\": 65536}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/gemma-4-26b-a4b-it"
      },
      "sameCompany": [
        "embeddinggemma-2",
        "embeddinggemma-300m",
        "shieldgemma",
        "diffusiongemma-26b-a4b-it",
        "gemma-4-12b-it",
        "gemma-4-31b-it",
        "gemma-4-e2b-it",
        "gemma-4-e4b-it"
      ],
      "sameModel": [
        "gemini-api"
      ],
      "weights": {
        "trainedBy": "Google DeepMind",
        "builtOn": "google/gemma-4-26B-A4B",
        "params": "25.2B",
        "active": "3.8B",
        "context": 262144,
        "goodFor": "General assistant and agent model with tool calling and optional thinking, taking text, images and video frames, fast for its size as a mixture of experts.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "gemma-4-26B_q4_0-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-qat-q4_0-gguf/blob/main/gemma-4-26B_q4_0-it.gguf",
            "who": "publisher",
            "precision": "q4_0",
            "sizeGB": 14.4,
            "memoryGB": 24,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)",
              "LM Studio"
            ],
            "note": "Quantisation-aware trained by Google. Add gemma-4-26B-it-mmproj.gguf (1.2 GB) for image input"
          },
          {
            "name": "ollama gemma4:26b",
            "url": "https://ollama.com/library/gemma4:26b",
            "who": "ollama",
            "precision": "q4_k_m",
            "sizeGB": 19,
            "memoryGB": 24,
            "format": "ollama",
            "runsOn": [
              "Ollama"
            ],
            "note": "Q4_K_M weights with a BF16 vision projector and a Q8_0 draft model (digest 001e5dafc3c7). Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant"
          },
          {
            "name": "gemma-4-26B-A4B-it",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 51.6,
            "memoryGB": 64,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)"
            ]
          }
        ],
        "run": "llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536",
        "runBuild": "gemma-4-26B_q4_0-it",
        "checked": "2026-10-21",
        "minMemoryGB": 24
      },
      "family": {
        "lab": "Google (Gemma)",
        "family": "Gemma 4",
        "url": "https://www.anchorterminal.com/open-weights/#google",
        "others": [
          {
            "name": "gemma-4-26B-A4B",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B",
            "params": "25.2B total, 3.8B active"
          },
          {
            "name": "gemma-4-31B",
            "url": "https://huggingface.co/google/gemma-4-31B",
            "params": "30.7B"
          },
          {
            "name": "gemma-4-31B-it",
            "url": "https://huggingface.co/google/gemma-4-31B-it",
            "params": "30.7B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-31b-it"
          },
          {
            "name": "gemma-4-E2B",
            "url": "https://huggingface.co/google/gemma-4-E2B",
            "params": "2.3B effective (5.1B with embeddings)"
          },
          {
            "name": "gemma-4-E2B-it",
            "url": "https://huggingface.co/google/gemma-4-E2B-it",
            "params": "2.3B effective (5.1B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e2b-it"
          },
          {
            "name": "gemma-4-E4B",
            "url": "https://huggingface.co/google/gemma-4-E4B",
            "params": "4.5B effective (8B with embeddings)"
          },
          {
            "name": "gemma-4-E4B-it",
            "url": "https://huggingface.co/google/gemma-4-E4B-it",
            "params": "4.5B effective (8B with embeddings)",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-e4b-it"
          },
          {
            "name": "gemma-4-26B-A4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-26B-A4B-it-assistant",
            "params": "420M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-31B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-31B-it-assistant",
            "params": "470M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E2B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E2B-it-assistant",
            "params": "78M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-E4B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-E4B-it-assistant",
            "params": "79M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "gemma-4-12B",
            "url": "https://huggingface.co/google/gemma-4-12B",
            "params": "11.95B"
          },
          {
            "name": "gemma-4-12B-it",
            "url": "https://huggingface.co/google/gemma-4-12B-it",
            "params": "11.95B",
            "listing": "https://www.anchorterminal.com/tools/gemma-4-12b-it"
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          {
            "name": "gemma-4-12B-it-assistant",
            "url": "https://huggingface.co/google/gemma-4-12B-it-assistant",
            "params": "423M (file metadata on Hugging Face; drafter)"
          },
          {
            "name": "diffusiongemma-26B-A4B-it",
            "url": "https://huggingface.co/google/diffusiongemma-26B-A4B-it",
            "params": "25.2B total, 3.8B active",
            "listing": "https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it"
          },
          {
            "name": "embeddinggemma-2",
            "url": "https://huggingface.co/google/embeddinggemma-2",
            "params": "740M",
            "listing": "https://www.anchorterminal.com/tools/embeddinggemma-2"
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        ],
        "olderCount": 171,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Google LLC (RDAP registrant of gemma.ai; the card names Google DeepMind as author)",
        "domain": "gemma.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://gemma.ai/models/releases/",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "RDAP names Google LLC as the registrant of gemma.ai, registered 16 December 2017, with MarkMonitor as registrar and Google's name servers. ai.google.dev's Gemma pages say the documentation moved to gemma.ai.",
          "Nothing is hosted under this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "gemma.ai keeps a dated releases page for the Gemma family (Gemma 4 on 31 March 2026). The Hugging Face commit history says what each change fixed.",
          "gemma.ai/.well-known/security.txt answers 404. Google's company-wide security.txt at google.com points to its vulnerability reward programme."
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        "score": 72
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      "live": {
        "slug": "gemma-4-26b-a4b-it",
        "securityTxt": {
          "url": "https://gemma.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:08:36.94110613Z"
        },
        "domain": {
          "domain": "gemma.ai",
          "registered": "2017-12-16",
          "source": "https://rdap.identitydigital.services/rdap/domain/gemma.ai",
          "checkedAt": "2026-11-08T15:00:32.790630815Z"
        },
        "updatedAt": "2026-11-10T16:08:36.94110613Z"
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    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "gemma-4-26b-a4b-it",
        "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, Google's own 4-bit QAT GGUF is 14.4 GB, which with a fifth on top is 17.3 GB and lands in the 24 GB band, with the 1.2 GB vision projector extra for images. Runtimes 25, released support in vLLM, Transformers, llama.cpp, Ollama, SGLang and LM Studio, six of six. Builds 10, Google publishes one quantised precision (Q4_0 GGUF), since its unquantised QAT repository is BF16 and there's no FP8 build. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 5, the Gemini API serves it and four inference providers are live on Hugging Face."
        },
        "scores": {
          "a": 50,
          "b": 90
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      },
      {
        "by": 25,
        "category": "interface",
        "leads": "gemma-4-26b-a4b-it",
        "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, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native."
        },
        "scores": {
          "a": 60,
          "b": 85
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        "by": 15,
        "category": "risk",
        "leads": "gemma-4-26b-a4b-it",
        "name": "Risk",
        "reasons": {
          "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.",
          "b": "File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked."
        },
        "scores": {
          "a": 55,
          "b": 70
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    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Nous Research",
        "b": "Google DeepMind",
        "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": "2026-07-20",
        "name": "Last release"
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      {
        "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": "3.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.",
        "question": "Which is better for AI agents, Arete-27B or Gemma 4 26B A4B IT?"
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    "goodFor": [
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        "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",
          "Risk, 70 against 55",
          "Agent interface, 85 against 60"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context",
        "slug": "gemma-4-26b-a4b-it",
        "watchFor": "No safety evaluation figures published"
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    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-darwin-36b-opus"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v3-2"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-deepseek-v4-flash-0731.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-devstral-small-2"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-diffusiongemma-26b-a4b-it.json",
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-exaone-4-5-33b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-12b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.json",
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        "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"
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        "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"
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b"
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        "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"
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      {
        "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"
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b"
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny"
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        "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"
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    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "arete-27b": 50,
        "by": 40,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 90,
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "arete-27b": 55,
        "by": 15,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 70,
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "arete-27b": 60,
        "by": 25,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 85,
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "arete-27b": 63,
        "by": 2,
        "edge": "gemma-4-26b-a4b-it",
        "gemma-4-26b-a4b-it": 65,
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. 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.",
      "gemma-4-26b-a4b-it": "A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports."
    }
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  "markdown": "Gemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. 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- Gemma 4 26B A4B IT: grade BB, 72.2/100, rank #5 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.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### Gemma 4 26B A4B IT (BB)\n\nGood for: Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context\n\nAhead on:\n- Reach, 90 against 50\n- Risk, 70 against 55\n- Agent interface, 85 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: No safety evaluation figures published\n\n\n## Why the edge\n\n### Reach: Gemma 4 26B A4B IT +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- Gemma 4 26B A4B IT (90): Memory 25, Google's own 4-bit QAT GGUF is 14.4 GB, which with a fifth on top is 17.3 GB and lands in the 24 GB band, with the 1.2 GB vision projector extra for images. Runtimes 25, released support in vLLM, Transformers, llama.cpp, Ollama, SGLang and LM Studio, six of six. Builds 10, Google publishes one quantised precision (Q4_0 GGUF), since its unquantised QAT repository is BF16 and there's no FP8 build. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 5, the Gemini API serves it and four inference providers are live on Hugging Face.\n\n### Agent interface: Gemma 4 26B A4B IT +25\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- Gemma 4 26B A4B IT (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents how tools are declared and how calls come back, ships a response schema for Transformers' parser, and names the gemma4 parser in vLLM and SGLang. Trained tool calling 20, several calls in one turn. Structured output 0, nothing documents how to ask the weights for JSON or a schema. Reasoning control 10, thinking is off by default, turned on per request, and returned in a thought channel. Context 10, 262,144 tokens native.\n\n### Risk: Gemma 4 26B A4B IT +15\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- Gemma 4 26B A4B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward policy give a route to report a flaw, but no public Gemma advisories were found and GitHub's advisory tab couldn't be checked.\n\n## Score by category\n\n| Category | Weight | Arete-27B | Gemma 4 26B A4B IT | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 90 | Gemma 4 26B A4B IT +40 |\n| Risk | 20% (20 this run) | 55 | 70 | Gemma 4 26B A4B IT +15 |\n| Agent interface | 10% (10 this run) | 60 | 85 | Gemma 4 26B A4B IT +25 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 65 | Gemma 4 26B A4B IT +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **72.2 · BB** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | Gemma 4 26B A4B IT |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Google DeepMind |\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 | 2026-07-20 |\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 | 3.8/5 (4) |\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**Gemma 4 26B A4B IT.** A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.\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### Gemma 4 26B A4B IT\n\n1. On the reference machine run `llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536`, and add the mmproj file for images\n2. With Ollama run `ollama run gemma4:26b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K\n3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text\n4. Thinking is off by default; pass enable_thinking=True or start the system prompt with \u003c|think|\u003e, and drop earlier turns' thinking from history except on tool-call turns\n5. Validate tool names and arguments before running them, and check any JSON yourself\n\n## Questions\n\n### Which is better for AI agents, Arete-27B or Gemma 4 26B A4B IT?\n\nGemma 4 26B A4B IT scores 72.2 (BB) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-26b-a4b-it.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"gemma-4-26b-a4b-it\"}`. From a terminal: `anchor compare arete-27b gemma-4-26b-a4b-it`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/gemma-4-26b-a4b-it.json\n\n## Other comparisons with Arete-27B or Gemma 4 26B A4B IT\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 Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.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 Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.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 Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-gemma-4-26b-a4b-it.md)\n- [Arete-27B vs 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[Arete-27B vs LFM3-1.7B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.md)\n- [Arete-27B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.md)\n- [Arete-27B vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.md)\n- [Arete-27B vs LFM3-VL-2B](https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.md)\n- [Arete-27B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-0-tiny.md)\n- [Arete-27B vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/arete-27b-vs-ling-3-1-flash-1019.md)\n- [Arete-27B vs LongCat-2.5](https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md)\n- [Arete-27B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Arete-27B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md)\n- [Arete-27B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-o-4-5.md)\n- [Arete-27B vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-1b.md)\n- [Arete-27B vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm-v-4-7-35b-a3b.md)\n- [Arete-27B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-minicpm5-2b.md)\n- [Arete-27B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5.md)\n- [Arete-27B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.md)\n- [Arete-27B vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m3-1-flash.md)\n- [Arete-27B vs Mistral Large 4](https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.md)\n- [Arete-27B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.md)\n- [Arete-27B vs 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Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Arete-27B vs North Mini Code 1.0](https://www.anchorterminal.com/compare/arete-27b-vs-north-mini-code-1-0.md)\n- [Arete-27B vs occamy-1.0](https://www.anchorterminal.com/compare/arete-27b-vs-occamy-1-0.md)\n- [Arete-27B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/arete-27b-vs-olmo-3-7b-think.md)\n- [Arete-27B vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-122b.md)\n- [Arete-27B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b.md)\n- [Arete-27B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.md)\n- [Arete-27B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.md)\n- [Arete-27B vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md)\n- [Arete-27B vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.md)\n- [Arete-27B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Arete-27B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-4b-instruct-2507.md)\n- [Arete-27B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-0-8b.md)\n- [Arete-27B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-122b-a10b.md)\n- [Arete-27B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-2b.md)\n- [Arete-27B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-397b-a17b.md)\n- [Arete-27B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-4b.md)\n- [Arete-27B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-5-9b.md)\n- [Arete-27B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-27b.md)\n- [Arete-27B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-6-35b-a3b.md)\n- [Arete-27B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-27b.md)\n- [Arete-27B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-8-flash-next.md)\n- [Arete-27B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-coder-next.md)\n- [Arete-27B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-2b-instruct.md)\n- [Arete-27B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Arete-27B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.md)\n- [Arete-27B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.md)\n- [Arete-27B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.md)\n- [Arete-27B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.md)\n- [Arete-27B vs Recursive Flint](https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.md)\n- [Arete-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.md)\n- [Arete-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.md)\n- [Arete-27B vs 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    "title": "Arete-27B vs Gemma 4 26B A4B IT for AI agents (2026) | Anchor Terminal",
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