{
  "data": {
    "a": {
      "slug": "decider",
      "name": "Decider",
      "vendor": "Mark Marosi (Mapika)",
      "vendorUrl": "https://github.com/Mapika",
      "kind": "model",
      "category": "decision-models",
      "summary": "Decider is a family of open-weight decision models by Mark Marosi (Mapika), from 0.8B to 35B parameters under Apache-2.0. It answers typed yes or no, choice and score questions with probabilities, and runs locally from the `decider-ai` Python package.",
      "url": "https://www.anchorterminal.com/tools/decider",
      "markdownUrl": "https://www.anchorterminal.com/tools/decider.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/decider.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/decider.json",
      "repo": "https://github.com/Mapika/decider",
      "license": "Apache-2.0 (code and weights)",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "pypi",
          "name": "decider-ai"
        }
      ],
      "auth": "none",
      "authNotes": "No account. `decider.serve` has no authentication option. `scripts/serve.sh` binds to 127.0.0.1 since 1.7.1, and `DECIDER_HOST=0.0.0.0` opens it to the network. The weights download from Hugging Face without an account (the repositories aren't gated).",
      "pricing": "free",
      "pricingNotes": "Free and open source, with nothing to buy. You pay for your own hardware. The README puts the 2B at about 4 GB of GPU memory, the 4B at 8.4 GB and the 35B at 65 GB in bf16, with GGUF files of 1.3 GB and 2.7 GB for CPU (https://github.com/Mapika/decider). No hosted API was found.",
      "priceSummary": "Free · OSS",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402. Decider is software you run, and its server has no payment route (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 1100,
        "npmWeekly": null,
        "pypiWeekly": 2680,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://github.com/Mapika/decider#readme",
      "capabilities": [
        "inference.decision"
      ],
      "tags": [
        "model",
        "open-source",
        "open-weights",
        "self-hosted",
        "local",
        "free",
        "python"
      ],
      "lastRelease": "2026-10-07",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 69.5,
        "grade": "B",
        "agentReady": false,
        "rank": 172,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 2,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 80,
          "maintenance": 87,
          "payments": 60,
          "reliability": 90,
          "schema": 78,
          "security": 38,
          "transparency": 46
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "An Apache-2.0 decision model family with a dated changelog, passing CI, 21 package releases since 22 September 2026 and model cards that list measured regressions. One person maintains it, the local server has no authentication option, states over 32,768 tokens are cut without an error, and no security policy is published.",
        "bestFor": "Local classification, routing, triage and checks where a team wants open weights in several sizes and a Jev-shaped route.",
        "strengths": [
          "Apache-2.0 code and weights, with the training code, data builders and per-version measurements in the repository",
          "Sizes from 0.8B to 35B parameters, with GGUF files for CPU and builds for CUDA, Apple silicon and vLLM",
          "`POST /v1/systemone` follows TypeSafe's wire format, and the README says TypeSafe's SDKs work with `TYPESAFE_BASE_URL` set to the local server",
          "Dated changelog entries for all 21 `decider-ai` releases from 1.0.0 (22 September 2026) to 1.9.0 (7 October 2026)",
          "Size limits return 413, overload 503 and invalid questions 422, each with a message documented in `docs/SERVING.md`"
        ],
        "weaknesses": [
          "The local server has no authentication option. It binds to 127.0.0.1 since 1.7.1",
          "States over 32,768 tokens are truncated (`DECIDER_MAX_STATE_TOKENS`) without an error",
          "No SECURITY.md, disclosure policy or security contact was found in the repository",
          "One maintainer wrote 95 of 98 commits, and the project is three weeks old",
          "The README says calibration on hard items is weak, the models are English only, and one pass can't do multi-step arithmetic",
          "No hosted API, and the HTTP server doesn't serve the GGUF files"
        ],
        "agentNotes": [
          "Pin weights by Hub tag (`v10`, `v2`) when results must repeat. The `main` branch of each model repository changes with new versions",
          "Read `x_p_max` for the top probability. Since 1.3.0 `confidence` on choice and score answers follows TypeSafe's rescaled definition, not the top probability",
          "Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option",
          "Count state tokens before sending. Over 32,768 the state is cut silently, and `/decide` caps context at 1,536 tokens",
          "Split multi-step arithmetic or multi-hop judgements into several questions, and don't write long rules into a question. The README says both fail"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "B",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 69.5
          }
        ],
        "editorialScores": {
          "ergonomics": 80,
          "maintenance": 87,
          "payments": 60,
          "reliability": 90,
          "schema": 78,
          "security": 38,
          "transparency": 64
        },
        "provenanceScore": 27
      },
      "connect": {
        "install": "pip install decider-ai"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/decider"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "github.com/Mapika",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/Mapika/decider/blob/main/docs/CHANGELOG.md",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "An individual's open-source project under Apache-2.0. The pyproject and the README citation name Mark Marosi as author, and no company is named.",
          "No vendor domain. The code is at github.com/Mapika/decider and the weights at huggingface.co/Mapika, so the domain line names the GitHub account and scores no domain age.",
          "Software you run, so there's no hosted endpoint, service terms or privacy policy. The author publishes none, and the Apache-2.0 licence stands in for terms.",
          "The changelog is `docs/CHANGELOG.md`, with a dated entry for each package release and model update. Release tags v1.0.2 to v1.9.0 match the PyPI versions."
        ],
        "score": 27
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/decider.json"
    },
    "answer": "Decider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust.",
    "b": {
      "slug": "gliclass",
      "name": "GLiClass",
      "vendor": "Knowledgator",
      "vendorUrl": "https://www.knowledgator.com",
      "kind": "model",
      "category": "decision-models",
      "summary": "GLiClass is an open-source Python library and family of open-weight zero-shot text classifiers from Knowledgator. It scores every candidate label in one forward pass and runs locally through a pipeline or a Ray Serve endpoint.",
      "url": "https://www.anchorterminal.com/tools/gliclass",
      "markdownUrl": "https://www.anchorterminal.com/tools/gliclass.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gliclass.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gliclass.json",
      "repo": "https://github.com/Knowledgator/GLiClass",
      "license": "Apache-2.0 (library and the model weights we checked)",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "pypi",
          "name": "gliclass"
        }
      ],
      "auth": "none",
      "authNotes": "No account or key. The weights are public and ungated on Hugging Face. The bundled Ray Serve deployment has no authentication of any kind, and `python -m gliclass.serve` binds to 0.0.0.0 unless `--host` is passed, so the owner has to restrict the port.",
      "pricing": "free",
      "pricingNotes": "Free and open source, with nothing to buy for the library or the weights. Hardware is the owner's cost. Knowledgator's site links a hosted platform with plans at platform.knowledgator.com, which we couldn't reach on 8 October 2026, so whether it serves GLiClass and at what price is unchecked.",
      "priceSummary": "Free · OSS",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402. GLiClass is software the owner runs, and its server has no payment route (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 555,
        "npmWeekly": null,
        "pypiWeekly": 13204,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.knowledgator.com/docs/frameworks/gliclass/",
      "capabilities": [
        "inference.decision"
      ],
      "tags": [
        "model",
        "open-source",
        "open-weights",
        "self-hosted",
        "local",
        "free",
        "python",
        "no-auth"
      ],
      "lastRelease": "2026-07-21",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 49.9,
        "grade": "D",
        "agentReady": false,
        "rank": 697,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 9,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 60,
          "maintenance": 44,
          "payments": 60,
          "reliability": 43,
          "schema": 49,
          "security": 38,
          "transparency": 64
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.",
        "bestFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.",
        "strengths": [
          "Apache-2.0 code and weights, with `train.py`, the training datasets named on the model cards and an arXiv paper (2508.07662)",
          "One forward pass scores every label. The base v3.0 card reports 51.6 examples a second averaged over 1 to 128 labels on an A6000 (vendor figures)",
          "Single-label (softmax) and multi-label (sigmoid) modes, hierarchical label sets, task prompts and few-shot examples in one pipeline call",
          "A Ray Serve deployment with dynamic batching ships in the `gliclass[serve]` extra, with a documented request table for `POST /gliclass`",
          "31 public, ungated GLiClass checkpoints on Hugging Face, from 32.7M to 439M parameters, as safetensors"
        ],
        "weaknesses": [
          "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic",
          "The bundled server has no authentication, and `python -m gliclass.serve` binds to 0.0.0.0 by default",
          "The last three runs of the Tests workflow on main, all on 24 September 2026, failed",
          "Still 0.1.x with no changelog file. 0.1.18 raised the `transformers` floor to 5.0, and issue #44 on v5 loading has been open since 6 July 2026",
          "No OpenAPI file, no llms.txt, no SECURITY.md and no documented error responses"
        ],
        "agentNotes": [
          "Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential",
          "On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA",
          "Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error",
          "Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1",
          "Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "D",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 49.9
          }
        ],
        "editorialScores": {
          "ergonomics": 60,
          "maintenance": 44,
          "payments": 60,
          "reliability": 43,
          "schema": 49,
          "security": 38,
          "transparency": 60
        },
        "provenanceScore": 68
      },
      "connect": {
        "install": "pip install gliclass",
        "http": "pip install \"gliclass[serve]\"\npython -m gliclass.serve --model knowledgator/gliclass-edge-v3.0 --port 8000\ncurl -X POST http://localhost:8000/gliclass \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"text\": \"This is a great product.\", \"labels\": [\"positive\", \"negative\", \"neutral\"], \"threshold\": 0.3, \"multi_label\": true}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/gliclass"
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Knowledgator Engineering Ltd.",
        "domain": "knowledgator.com",
        "domainRegistered": "2021-06-25",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/Knowledgator/GLiClass/releases",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "The terms of use and privacy policy linked from knowledgator.com name Knowledgator Engineering Ltd., London, United Kingdom. Both are dated 21 September 2023.",
          "Those two documents govern the website and Knowledgator's hosted services, not the library, so `terms` and `privacy` are left out. The Apache-2.0 licence governs what an agent would run.",
          "Software the owner runs, so there is no hosted endpoint and no status page for it.",
          "www.knowledgator.com/.well-known/security.txt returns 404, and the repository has no SECURITY.md.",
          "RDAP for knowledgator.com gives a registration date of 2021-06-25.",
          "The code is on github.com under the Knowledgator organisation and the weights on huggingface.co under knowledgator."
        ],
        "score": 68
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/gliclass.json"
    },
    "facts": [
      {
        "a": "Model API",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "Mark Marosi (Mapika)",
        "b": "Knowledgator",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "None",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Free",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0 (code and weights)",
        "b": "Apache-2.0 (library and the model weights we checked)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-07",
        "b": "2026-07-21",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "no document linked",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "1.1k stars, 2.7k PyPI/wk",
        "b": "555 stars, 13k PyPI/wk",
        "name": "Popularity"
      }
    ],
    "faq": [
      {
        "answer": "Decider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust.",
        "question": "Which is better for AI agents, Decider or GLiClass?"
      },
      {
        "answer": "Neither needs a key.",
        "question": "Do Decider and GLiClass need an API key?"
      },
      {
        "answer": "No hosted endpoint is listed for Decider. No hosted endpoint is listed for GLiClass.",
        "question": "Can an agent call Decider and GLiClass without installing anything?"
      },
      {
        "answer": "Yes. Decider is open source (Apache-2.0 (code and weights)). GLiClass is open source (Apache-2.0 (library and the model weights we checked)).",
        "question": "Are Decider and GLiClass open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 90 against 43",
          "Schema \u0026 documentation, 78 against 49",
          "Agent ergonomics, 80 against 60",
          "Maintenance \u0026 community, 87 against 44"
        ],
        "also": null,
        "goodFor": "Local classification, routing, triage and checks where a team wants open weights in several sizes and a Jev-shaped route.",
        "slug": "decider",
        "watchFor": "The local server has no authentication option. It binds to 127.0.0.1 since 1.7.1"
      },
      {
        "aheadOn": [
          "Transparency \u0026 trust, 64 against 46"
        ],
        "also": null,
        "goodFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.",
        "slug": "gliclass",
        "watchFor": "No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic"
      }
    ],
    "job": {
      "capability": "inference.decision",
      "name": "Inference decision"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider.json",
        "title": "Celeris-1 Decision vs Decider",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.json",
        "title": "Celeris-1 Decision vs GLiClass",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-decider.json",
        "title": "Clef vs Decider",
        "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.json",
        "title": "Clef vs GLiClass",
        "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass"
      },
      {
        "json": "https://www.anchorterminal.com/compare/convai-laya-vs-decider.json",
        "title": "Laya vs Decider",
        "url": "https://www.anchorterminal.com/compare/convai-laya-vs-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.json",
        "title": "Laya vs GLiClass",
        "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev.json",
        "title": "Decider vs Kev",
        "url": "https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-liquid-d1.json",
        "title": "Decider vs Liquid d1",
        "url": "https://www.anchorterminal.com/compare/decider-vs-liquid-d1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-openai-decisions-api.json",
        "title": "Decider vs OpenAI Decisions API",
        "url": "https://www.anchorterminal.com/compare/decider-vs-openai-decisions-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-strands-decider.json",
        "title": "Decider vs Strands Decider 2B",
        "url": "https://www.anchorterminal.com/compare/decider-vs-strands-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-typesafe-jev.json",
        "title": "Decider vs Jev",
        "url": "https://www.anchorterminal.com/compare/decider-vs-typesafe-jev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-vela.json",
        "title": "Decider vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/decider-vs-vela"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.json",
        "title": "GLiClass vs Kev",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.json",
        "title": "GLiClass vs Liquid d1",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.json",
        "title": "GLiClass vs OpenAI Decisions API",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json",
        "title": "GLiClass vs Strands Decider 2B",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.json",
        "title": "GLiClass vs Jev",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-vela.json",
        "title": "GLiClass vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-vela"
      }
    ],
    "scores": [
      {
        "by": 47,
        "decider": 90,
        "edge": "decider",
        "gliclass": 43,
        "key": "reliability",
        "name": "Reliability",
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 29,
        "decider": 78,
        "edge": "decider",
        "gliclass": 49,
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "weight": 13
      },
      {
        "by": 20,
        "decider": 80,
        "edge": "decider",
        "gliclass": 60,
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "weight": 13
      },
      {
        "by": 0,
        "decider": 38,
        "edge": "",
        "gliclass": 38,
        "key": "security",
        "name": "Security \u0026 auth",
        "weight": 14
      },
      {
        "by": 0,
        "decider": 60,
        "edge": "",
        "gliclass": 60,
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 43,
        "decider": 87,
        "edge": "decider",
        "gliclass": 44,
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "weight": 7
      },
      {
        "by": 18,
        "decider": 46,
        "edge": "gliclass",
        "gliclass": 64,
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
    ],
    "summary": "Decider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust. Both do inference decision.",
    "verdicts": {
      "decider": "An Apache-2.0 decision model family with a dated changelog, passing CI, 21 package releases since 22 September 2026 and model cards that list measured regressions. One person maintains it, the local server has no authentication option, states over 32,768 tokens are cut without an error, and no security policy is published.",
      "gliclass": "An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed."
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  "markdown": "Decider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust. Both do inference decision.\n\n- Decider: grade B, 69.5/100, rank #172 of 842. Markdown https://www.anchorterminal.com/tools/decider.md · JSON https://www.anchorterminal.com/api/v1/tools/decider.json\n- GLiClass: grade D, 49.9/100, rank #697 of 842. Markdown https://www.anchorterminal.com/tools/gliclass.md · JSON https://www.anchorterminal.com/api/v1/tools/gliclass.json\n\n## Which one, for what\n\n### Decider (B)\n\nGood for: Local classification, routing, triage and checks where a team wants open weights in several sizes and a Jev-shaped route.\n\nAhead on:\n- Reliability, 90 against 43\n- Schema \u0026 documentation, 78 against 49\n- Agent ergonomics, 80 against 60\n- Maintenance \u0026 community, 87 against 44\n\nWatch for: The local server has no authentication option. It binds to 127.0.0.1 since 1.7.1\n\n### GLiClass (D)\n\nGood for: Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.\n\nAhead on:\n- Transparency \u0026 trust, 64 against 46\n\nWatch for: No calibration evidence. The cards report F1 only, and the docs tell users to calibrate thresholds on their own traffic\n\n\n## Score by category\n\n| Category | Weight | Decider | GLiClass | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 90 | 43 | Decider +47 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 49 | Decider +29 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 60 | Decider +20 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 38 | even |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 87 | 44 | Decider +43 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 46 | 64 | GLiClass +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **69.5 · B** | **49.9 · D** | |\n\n## Facts side by side\n\n| Fact | Decider | GLiClass |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Mark Marosi (Mapika) | Knowledgator |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 (code and weights) | Apache-2.0 (library and the model weights we checked) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-07 | 2026-07-21 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models |  |  |\n| Terms restrict automated access |  |  |\n| Terms restrict benchmarking |  |  |\n| Terms or service can change without notice |  |  |\n| Arbitration or class-action waiver |  |  |\n| Popularity | 1.1k stars, 2.7k PyPI/wk | 555 stars, 13k PyPI/wk |\n\n## Verdicts\n\n**Decider.** An Apache-2.0 decision model family with a dated changelog, passing CI, 21 package releases since 22 September 2026 and model cards that list measured regressions. One person maintains it, the local server has no authentication option, states over 32,768 tokens are cut without an error, and no security policy is published.\n\n**GLiClass.** An Apache-2.0 classifier that scores a whole label set in one encoder pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes. It returns label scores with no calibration claim, the bundled server has no authentication, and the last three test runs on the main branch, on 24 September 2026, failed.\n\n## Before you call either\n\n### Decider\n\n1. Pin weights by Hub tag (`v10`, `v2`) when results must repeat. The `main` branch of each model repository changes with new versions\n2. Read `x_p_max` for the top probability. Since 1.3.0 `confidence` on choice and score answers follows TypeSafe's rescaled definition, not the top probability\n3. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option\n4. Count state tokens before sending. Over 32,768 the state is cut silently, and `/decide` caps context at 1,536 tokens\n5. Split multi-step arithmetic or multi-hop judgements into several questions, and don't write long rules into a question. The README says both fail\n\n### GLiClass\n\n1. Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential\n2. On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA\n3. Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error\n4. Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1\n5. Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently\n\n## Questions\n\n### Which is better for AI agents, Decider or GLiClass?\n\nDecider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust.\n\n### Do Decider and GLiClass need an API key?\n\nNeither needs a key.\n\n### Can an agent call Decider and GLiClass without installing anything?\n\nNo hosted endpoint is listed for Decider. No hosted endpoint is listed for GLiClass.\n\n### Are Decider and GLiClass open source?\n\nYes. Decider is open source (Apache-2.0 (code and weights)). GLiClass is open source (Apache-2.0 (library and the model weights we checked)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/decider-vs-gliclass.json, and with the fewest tokens: https://www.anchorterminal.com/compare/decider-vs-gliclass.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"decider\", \"b\": \"gliclass\"}`. From a terminal: `anchor compare decider gliclass`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/decider.json and https://www.anchorterminal.com/api/v1/tools/gliclass.json\n\n## Other comparisons with Decider or GLiClass\n\n- [Celeris-1 Decision vs Decider](https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider.md)\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Clef vs Decider](https://www.anchorterminal.com/compare/cloudflare-clef-vs-decider.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Laya vs Decider](https://www.anchorterminal.com/compare/convai-laya-vs-decider.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Decider vs Kev](https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev.md)\n- [Decider vs Liquid d1](https://www.anchorterminal.com/compare/decider-vs-liquid-d1.md)\n- [Decider vs OpenAI Decisions API](https://www.anchorterminal.com/compare/decider-vs-openai-decisions-api.md)\n- [Decider vs Strands Decider 2B](https://www.anchorterminal.com/compare/decider-vs-strands-decider.md)\n- [Decider vs Jev](https://www.anchorterminal.com/compare/decider-vs-typesafe-jev.md)\n- [Decider vs Vela 2.0](https://www.anchorterminal.com/compare/decider-vs-vela.md)\n- [GLiClass vs Kev](https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.md)\n- [GLiClass vs Strands Decider 2B](https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md)\n- [GLiClass vs Jev](https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.md)\n- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-vs-vela.md)\n",
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    "description": "Decider scores 69.5 (B) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on transparency \u0026 trust. Both do inference decision. Category scores, facts, verdicts and agent notes side by side.",
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    "title": "Decider vs GLiClass for AI agents, B 69.5 vs D 49.9 | Anchor Terminal",
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    "updated": "2026-10-09",
    "url": "https://www.anchorterminal.com/compare/decider-vs-gliclass"
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