{
  "data": {
    "a": {
      "slug": "agents-a1-4b",
      "name": "Agents-A1-4B",
      "vendor": "InternScience (Shanghai AI Laboratory)",
      "vendorUrl": "https://internscience.github.io/Agents-A1/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/agents-a1-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json",
      "dns": "agents-a1-4b.grade.anchorterminal.com",
      "repo": "https://huggingface.co/InternScience/Agents-A1-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q8_0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-F16-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (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/InternScience/Agents-A1-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "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": 29,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
        "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "strengths": [
          "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model",
          "FP8 and three GGUF precisions from the publisher",
          "Apache 2.0 through the chain, with no gate",
          "Trained for tool calling, several calls a turn, and 262,144 tokens of context",
          "An evaluation framework for its agent benchmarks is published on GitHub"
        ],
        "weaknesses": [
          "The base isn't declared, and the technical report doesn't cover the 4B",
          "No safety results, agent guidance or security contact",
          "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14",
          "Tool calling and structured output aren't documented beyond a parser name",
          "InternScience answered one of three discussions, and the history was squashed"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector",
          "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work",
          "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14",
          "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 43
        },
        "provenanceScore": 47,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/agents-a1-4b"
      },
      "weights": {
        "trainedBy": "InternScience (Shanghai AI Laboratory)",
        "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B",
        "params": "4.5B",
        "context": 262144,
        "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Agents-A1-4B-Q8_0",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 5.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B-Q4_K_M",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.4,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.7 GB of weights and the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Agents-A1-4B-FP8",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 7.8,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Agents-A1-4B-F16",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          }
        ],
        "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Agents-A1-4B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "InternScience (Shanghai AI Laboratory)",
        "family": "Agents-A1",
        "url": "https://www.anchorterminal.com/open-weights/#internscience",
        "others": [
          {
            "name": "Agents-A1",
            "url": "https://huggingface.co/InternScience/Agents-A1",
            "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template",
        "domain": "internscience.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.",
          "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.",
          "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)."
        ],
        "score": 47
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json",
      "live": {
        "slug": "agents-a1-4b",
        "securityTxt": {
          "url": "https://internscience.github.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-10T16:07:46.042106985Z"
        },
        "domain": {
          "domain": "github.io",
          "checkedAt": "2026-11-08T15:08:12.013832199Z"
        },
        "updatedAt": "2026-11-10T16:07:46.042106985Z"
      }
    },
    "answer": "Agents-A1-4B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep.",
    "b": {
      "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"
    },
    "edges": [
      {
        "by": 40,
        "category": "reach",
        "leads": "agents-a1-4b",
        "name": "Reach",
        "reasons": {
          "a": "Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.",
          "b": "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."
        },
        "scores": {
          "a": 90,
          "b": 50
        }
      },
      {
        "by": 20,
        "category": "upkeep",
        "leads": "arete-27b",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.",
          "b": "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."
        },
        "scores": {
          "a": 43,
          "b": 63
        }
      },
      {
        "by": 10,
        "category": "interface",
        "leads": "agents-a1-4b",
        "name": "Agent interface",
        "reasons": {
          "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. Context 10, 262,144 tokens.",
          "b": "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."
        },
        "scores": {
          "a": 70,
          "b": 60
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "InternScience (Shanghai AI Laboratory)",
        "b": "Nous Research",
        "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": "2026-07-14",
        "b": "2026-11-06",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Agents-A1-4B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep.",
        "question": "Which is better for AI agents, Agents-A1-4B or Arete-27B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 90 against 50",
          "Agent interface, 70 against 60"
        ],
        "also": null,
        "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "slug": "agents-a1-4b",
        "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B"
      },
      {
        "aheadOn": [
          "Documentation and upkeep, 63 against 43"
        ],
        "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"
      }
    ],
    "history": null,
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Arete-27B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct.json",
        "title": "Arete-27B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct.json",
        "title": "Arete-27B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct.json",
        "title": "Arete-27B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Arete-27B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint.json",
        "title": "Arete-27B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview.json",
        "title": "Arete-27B vs Ripple-65B Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ripple-65b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview.json",
        "title": "Arete-27B vs SenseNova-Loop-9B-Preview",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-sensenova-loop-9b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b.json",
        "title": "Arete-27B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b.json",
        "title": "Arete-27B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.json",
        "title": "Arete-27B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Arete-27B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Arete-27B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Arete-27B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.json",
        "title": "Arete-27B vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b.json",
        "title": "Arete-27B vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.json",
        "title": "Arete-27B vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b.json",
        "title": "Arete-27B vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-zaya1-8b"
      }
    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "agents-a1-4b": 90,
        "arete-27b": 50,
        "by": 40,
        "edge": "agents-a1-4b",
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "agents-a1-4b": 55,
        "arete-27b": 55,
        "by": 0,
        "edge": "",
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "agents-a1-4b": 70,
        "arete-27b": 60,
        "by": 10,
        "edge": "agents-a1-4b",
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "agents-a1-4b": 43,
        "arete-27b": 63,
        "by": 20,
        "edge": "arete-27b",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Agents-A1-4B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
      "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."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b",
    "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.min.md"
  },
  "markdown": "Agents-A1-4B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #39 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json\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- 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### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Reach, 90 against 50\n- Agent interface, 70 against 60\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\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\nAhead on:\n- Documentation and upkeep, 63 against 43\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\n\n\n## Why the edge\n\n### Reach: Agents-A1-4B +40\n\n- Agents-A1-4B (90): Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.\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\n### Documentation and upkeep: Arete-27B +20\n\n- Agents-A1-4B (43): Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.\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\n### Agent interface: Agents-A1-4B +10\n\n- Agents-A1-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. Context 10, 262,144 tokens.\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\n## Score by category\n\n| Category | Weight | Agents-A1-4B | Arete-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 50 | Agents-A1-4B +40 |\n| Risk | 20% (20 this run) | 55 | 55 | even |\n| Agent interface | 10% (10 this run) | 70 | 60 | Agents-A1-4B +10 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 63 | Arete-27B +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **55.1 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Arete-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | Nous Research |\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 | 2026-07-14 | 2026-11-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\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## Before you call either\n\n### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Agents-A1-4B or Arete-27B?\n\nAgents-A1-4B scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"arete-27b\"}`. From a terminal: `anchor compare agents-a1-4b arete-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/arete-27b.json\n\n## Other comparisons with Agents-A1-4B or Arete-27B\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md)\n- [Agents-A1-4B vs Cola2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs 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