{
  "data": {
    "a": {
      "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": 884,
        "ranked": true,
        "rankOf": 1133,
        "categoryRank": 16,
        "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",
      "live": {
        "slug": "gliclass",
        "versions": [
          {
            "registry": "github",
            "name": "Knowledgator/GLiClass",
            "version": "v0.1.20",
            "released": "2026-07-21",
            "seenAt": "2026-10-17T01:50:23.535545243Z"
          },
          {
            "registry": "pypi",
            "name": "gliclass",
            "version": "0.1.21",
            "released": "2026-10-16",
            "seenAt": "2026-10-17T01:50:23.412045Z"
          }
        ],
        "githubStars": 561,
        "pypiWeekly": 11944,
        "securityTxt": {
          "url": "https://knowledgator.com/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-17T15:51:57.107713982Z"
        },
        "domain": {
          "domain": "knowledgator.com",
          "registered": "2021-06-25",
          "source": "https://rdap.verisign.com/com/v1/domain/knowledgator.com",
          "checkedAt": "2026-10-11T13:24:38.717810411Z"
        },
        "updatedAt": "2026-10-17T15:51:57.107713982Z"
      }
    },
    "answer": "kettei scores 58.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on reliability, payments \u0026 pricing and transparency \u0026 trust.",
    "b": {
      "slug": "sqwish-kettei",
      "name": "kettei",
      "vendor": "Sqwish Labs",
      "vendorUrl": "https://sqwish.ai",
      "kind": "model",
      "category": "decision-models",
      "summary": "kettei is Sqwish Labs' family of decision models, released on 14 October 2026. Its OneHot API returns a probability for each allowed answer to yes or no, choice and rating questions about text or images, and fine-tunes the smaller sizes.",
      "url": "https://www.anchorterminal.com/tools/sqwish-kettei",
      "markdownUrl": "https://www.anchorterminal.com/tools/sqwish-kettei.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sqwish-kettei.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sqwish-kettei.json",
      "repo": "https://github.com/sqwishai/onehot-agent",
      "license": "Proprietary hosted models and API. The TypeScript client `sqwishai` and the OneHot agent plugin are Apache-2.0. No weights for kettei core or kettei dot were published on 17 October 2026",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.sqwish.ai/v1/decide",
      "packages": [
        {
          "registry": "pypi",
          "name": "sqwishai"
        },
        {
          "registry": "npm",
          "name": "sqwishai"
        }
      ],
      "auth": "api-key",
      "authNotes": "A bearer key (`onehot_sk_...`) created in the console at console.sqwish.ai and shown once. Each key has full, inference or read-only scope, and an inference key can carry a monthly spend cap. No key can create keys, buy credit or change the account. `/v1/lint`, `/v1/recipes`, `/v1/systemone/translate` and `/healthz` need no key.",
      "pricing": "usage",
      "pricingNotes": "Prepaid credit bought in the console, with no contract. Per million input tokens, kettei fat costs $0.06, chub $0.04 and core $0.02, and fine-tuned chub and core cost twice their base rate. Output isn't billed. Training costs $0.80 (chub) or $0.40 (core) per million training tokens, at least $2.50 a job, charged only if it succeeds. Keep warm is $0.10 an hour. Admission gives $5.00 of credit for 30 days, and new accounts may start on a waitlist (https://docs.sqwish.ai/guides/pricing).",
      "priceSummary": "$0.50 / tx",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the docs, llms.txt, the OpenAPI document or the pricing page (checked 2026-10-17). Credit is bought through Stripe in the console.",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": 1224,
        "pypiWeekly": null,
        "asOf": "2026-10-17"
      },
      "docsUrl": "https://docs.sqwish.ai/introduction",
      "llmsTxt": "https://docs.sqwish.ai/llms.txt",
      "openapi": "https://api.sqwish.ai/openapi.json",
      "capabilities": [
        "inference.decision",
        "finetune.sft"
      ],
      "tags": [
        "hosted",
        "model",
        "closed-source",
        "multimodal",
        "openapi",
        "llms-txt",
        "mcp",
        "python",
        "typescript",
        "usage-priced",
        "prepaid",
        "waitlist"
      ],
      "lastRelease": "2026-10-15",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.3,
        "grade": "C",
        "agentReady": false,
        "rank": 640,
        "ranked": true,
        "rankOf": 1133,
        "categoryRank": 14,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 89,
          "maintenance": 57,
          "payments": 35,
          "reliability": 30,
          "schema": 87,
          "security": 68,
          "transparency": 51
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-17"
        },
        "negative": -2,
        "negativeNotes": [
          "2026-10-14. The launch post says 'We're open-sourcing kettei core (3.7B) and kettei dot (900M) ... so you can run them yourself', and the OneHot page says 'core and dot are open weights you can run yourself' (https://sqwish.ai/blog/your-ai-agent-is-writing-essays-to-tick-boxes, https://sqwish.ai/onehot). On 17 October 2026 the verified Hugging Face organisation Sqwish (Sqwish Labs) listed 0 public models, no model named kettei was found on Hugging Face, and the docs name no download and no kettei-dot model (https://huggingface.co/api/organizations/sqwish/overview, checked 2026-10-17). A claim of available open weights not borne out three days after launch, -2."
        ],
        "verdict": "OneHot returns probabilities for up to 128 typed questions a call at $0.02 to $0.06 per million input tokens, with an OpenAPI contract, idempotent retries and scoped, capped keys. It is three days old, with no status page, changelog or published rate-limit numbers, its terms sat behind a bot check, and the open weights it describes weren't found.",
        "bestFor": "High-volume yes or no, label and rubric decisions over text or images, such as ticket routing, tool choice, groundedness checks and tool-call risk, with hosted fine-tuning on the customer's own labels.",
        "strengths": [
          "Binary, single and ordinal decisions, up to 128 a request, each with a probability for every allowed answer and an optional abstain or cost policy",
          "Per-token prices published without a login, from $0.02 per million input tokens for kettei core, with output unbilled",
          "OpenAPI 3.1 contract of 76 operations, llms.txt on two hosts, Markdown twins and error tables that say what is retried and what is billed",
          "Idempotency keys on every decision, with a successful answer replayed free for 24 hours",
          "Keys scoped to full, inference or read access, with optional monthly spend caps, and no key can change the account"
        ],
        "weaknesses": [
          "Launched on 14 October 2026, with no status page, no changelog and no SLA",
          "Rate limits depend on a tier read from the account, and the docs publish no numbers",
          "Terms, privacy policy and subprocessor list sit behind a Cloudflare bot check and weren't read",
          "The launch post and OneHot page describe kettei core and dot as open weights, and Sqwish's Hugging Face organisation listed no public models on 17 October",
          "No security.txt, bug bounty or certification found, and new accounts may wait on a waitlist"
        ],
        "agentNotes": [
          "Send `context` and `decisions` to `POST https://api.sqwish.ai/v1/decide` with a bearer key. Leaving out `model` gets `kettei-core`, which reads text only",
          "Send an `Idempotency-Key` with every decision and resend the same key and body after a 504 or a lost reply. Replays aren't charged",
          "Branch on `error.code` and `error.retryable`, not the status. Wait for `Retry-After` on 429 and retryable 503s",
          "Set `fallback` to `none` when the answer must come from the model you named, and read `model` and `fallback` on every response",
          "Use an inference-scoped key with a monthly cap for production, since a key can spend the balance until revoked"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 58.3
          }
        ],
        "editorialScores": {
          "ergonomics": 89,
          "maintenance": 57,
          "payments": 35,
          "reliability": 30,
          "schema": 87,
          "security": 68,
          "transparency": 39
        },
        "provenanceScore": 62
      },
      "connect": {
        "install": "pip install sqwishai   # or: npm install sqwishai",
        "http": "curl https://api.sqwish.ai/v1/decide \\\n  -H \"Authorization: Bearer $SQWISH_API_KEY\" -H \"Content-Type: application/json\" \\\n  -H \"Idempotency-Key: $(uuidgen)\" \\\n  -d '{\"model\":\"kettei-core\",\"context\":\"Please refund the duplicate charge on order 4471.\",\"decisions\":[{\"id\":\"refund\",\"kind\":\"binary\",\"question\":\"Is a refund requested?\"}]}'",
        "claudeCode": "claude mcp add --transport http onehot https://api.sqwish.ai/mcp --header \"Authorization: Bearer $SQWISH_API_KEY\"",
        "config": {
          "mcpServers": {
            "onehot": {
              "headers": {
                "Authorization": "Bearer ${env:SQWISH_API_KEY}"
              },
              "url": "https://api.sqwish.ai/mcp"
            }
          }
        }
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/sqwish-kettei"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "kettei fat input",
          "unit": "1m-tokens",
          "usd": 0.06,
          "note": "Output not billed"
        },
        {
          "item": "kettei chub input",
          "unit": "1m-tokens",
          "usd": 0.04,
          "note": "Output not billed. Fine-tuned chub $0.08"
        },
        {
          "item": "kettei core input",
          "unit": "1m-tokens",
          "usd": 0.02,
          "note": "Output not billed. Fine-tuned core $0.04"
        },
        {
          "item": "Fine-tuning kettei chub",
          "unit": "1m-tokens",
          "usd": 0.8,
          "note": "Per million training tokens, at least $2.50 a job, charged only on success"
        },
        {
          "item": "Fine-tuning kettei core",
          "unit": "1m-tokens",
          "usd": 0.4,
          "note": "Per million training tokens, at least $2.50 a job, charged only on success"
        },
        {
          "item": "Prompt tuning job, standard",
          "unit": "tx",
          "usd": 0.5,
          "note": "Thorough effort $3.00 a job"
        }
      ],
      "provenance": {
        "legalEntity": "Sqwish Labs",
        "domain": "sqwish.ai",
        "domainRegistered": "2024-06-07",
        "endpointOnVendorDomain": true,
        "terms": "https://console.sqwish.ai/terms",
        "privacy": "https://console.sqwish.ai/privacy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-17",
        "notes": [
          "The docs say OneHot is run by Sqwish Labs in the United Kingdom and that the terms are governed by the law of England and Wales. The terms, privacy policy and subprocessor list on console.sqwish.ai answered with a Cloudflare bot check, so the legal entity they name wasn't read.",
          "Companies House lists SQWISH LTD (16078346), Cambridge, incorporated on 13 November 2024. Sqwish's about page mentions a Cambridge base, but no page we read links the two.",
          "RDAP for sqwish.ai gives a registration date of 7 June 2024.",
          "sqwish.ai/.well-known/security.txt and docs.sqwish.ai/.well-known/security.txt return 404, and the same path on api.sqwish.ai and console.sqwish.ai returns 401. The docs give security@sqwish.ai for leaked keys.",
          "status.sqwish.ai did not resolve, sqwish.ai/status serves the home page, and docs.sqwish.ai/changelog returns 404.",
          "robots.txt allows our reader on sqwish.ai and docs.sqwish.ai. console.sqwish.ai disallows /v1/ and /auth/. api.sqwish.ai has no robots.txt.",
          "The docs' agent page includes a prompt for an agent to install the plugin. It was read as data and not acted on."
        ],
        "score": 62
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/sqwish-kettei.json",
      "live": {
        "slug": "sqwish-kettei",
        "probe": {
          "target": "https://api.sqwish.ai/v1/decide",
          "method": "get",
          "lastAt": "2026-10-18T00:31:09.09406199Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 216,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 223,
          "p95ms24h": 374,
          "samples24h": 27,
          "samples30d": 27,
          "days": [
            {
              "date": "2026-10-17",
              "probes": 21,
              "ok": 21
            },
            {
              "date": "2026-10-18",
              "probes": 6,
              "ok": 6
            }
          ]
        },
        "pages": [
          {
            "url": "https://docs.sqwish.ai/guides/pricing",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-10-17T23:02:04.301442485Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "ad2ab7ad7b40"
          },
          {
            "url": "https://console.sqwish.ai/privacy",
            "kind": "privacy",
            "status": 403,
            "checkedAt": "2026-10-17T22:58:05.710191107Z",
            "changedAt": "0001-01-01T00:00:00Z"
          },
          {
            "url": "https://console.sqwish.ai/terms",
            "kind": "terms",
            "status": 403,
            "checkedAt": "2026-10-17T22:58:07.731699708Z",
            "changedAt": "0001-01-01T00:00:00Z"
          }
        ],
        "updatedAt": "2026-10-18T00:31:09.09406199Z"
      }
    },
    "facts": [
      {
        "a": "Model API",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "Knowledgator",
        "b": "Sqwish Labs",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "https://api.sqwish.ai/v1/decide",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "None",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Free",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Apache-2.0 (library and the model weights we checked)",
        "b": "Proprietary hosted models and API. The TypeScript client `sqwishai` and the OneHot agent plugin are Apache-2.0. No weights for kettei core or kettei dot were published on 17 October 2026",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-21",
        "b": "2026-10-15",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "",
        "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": "555 stars, 13k PyPI/wk",
        "b": "1.2k npm/wk",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "3.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "kettei scores 58.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on reliability, payments \u0026 pricing and transparency \u0026 trust.",
        "question": "Which is better for AI agents, GLiClass or kettei?"
      },
      {
        "answer": "GLiClass needs no key. kettei needs an API key.",
        "question": "Do GLiClass and kettei need an API key?"
      },
      {
        "answer": "No hosted endpoint is listed for GLiClass. kettei has a hosted endpoint at https://api.sqwish.ai/v1/decide.",
        "question": "Can an agent call GLiClass and kettei without installing anything?"
      },
      {
        "answer": "GLiClass is open source (Apache-2.0 (library and the model weights we checked)). No open-source release is listed for kettei.",
        "question": "Are GLiClass and kettei open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 43 against 30",
          "Payments \u0026 pricing, 60 against 35",
          "Transparency \u0026 trust, 64 against 51"
        ],
        "also": [
          "No key needed to call it",
          "Open source"
        ],
        "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"
      },
      {
        "aheadOn": [
          "Schema \u0026 documentation, 87 against 49",
          "Agent ergonomics, 89 against 60",
          "Security \u0026 auth, 68 against 38",
          "Maintenance \u0026 community, 57 against 44"
        ],
        "also": [
          "A hosted endpoint, with nothing to install"
        ],
        "goodFor": "High-volume yes or no, label and rubric decisions over text or images, such as ticket routing, tool choice, groundedness checks and tool-call risk, with hosted fine-tuning on the customer's own labels.",
        "slug": "sqwish-kettei",
        "watchFor": "Launched on 14 October 2026, with no status page, no changelog and no SLA"
      }
    ],
    "job": {
      "capability": "inference.decision",
      "name": "Decision models"
    },
    "others": [
      {
        "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/celeris-1-decision-vs-sqwish-kettei.json",
        "title": "Celeris-1 Decision vs kettei",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-sqwish-kettei"
      },
      {
        "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/cloudflare-clef-vs-sqwish-kettei.json",
        "title": "Clef vs kettei",
        "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-sqwish-kettei"
      },
      {
        "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/convai-laya-vs-sqwish-kettei.json",
        "title": "Laya vs kettei",
        "url": "https://www.anchorterminal.com/compare/convai-laya-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-gliclass.json",
        "title": "Decider vs GLiClass",
        "url": "https://www.anchorterminal.com/compare/decider-vs-gliclass"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-sqwish-kettei.json",
        "title": "Decider vs kettei",
        "url": "https://www.anchorterminal.com/compare/decider-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-gliclass.json",
        "title": "Decision 3.0 vs GLiClass",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-gliclass"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-sqwish-kettei.json",
        "title": "Decision 3.0 vs kettei",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-granite-switch.json",
        "title": "GLiClass vs Granite Switch 4.1",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-granite-switch"
      },
      {
        "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-microsoft-decision-1.json",
        "title": "GLiClass vs Microsoft-Decision-1",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-nace-drex.json",
        "title": "GLiClass vs Drex 1.5",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-nace-drex"
      },
      {
        "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-pplx-decider.json",
        "title": "GLiClass vs pplx-decider",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash.json",
        "title": "GLiClass vs pplx-decider-flash",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-scaledown.json",
        "title": "GLiClass vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev.json",
        "title": "GLiClass vs SPEV",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev"
      },
      {
        "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"
      },
      {
        "json": "https://www.anchorterminal.com/compare/granite-switch-vs-sqwish-kettei.json",
        "title": "Granite Switch 4.1 vs kettei",
        "url": "https://www.anchorterminal.com/compare/granite-switch-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei.json",
        "title": "Kev vs kettei",
        "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-sqwish-kettei.json",
        "title": "Liquid d1 vs kettei",
        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-sqwish-kettei.json",
        "title": "Microsoft-Decision-1 vs kettei",
        "url": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nace-drex-vs-sqwish-kettei.json",
        "title": "Drex 1.5 vs kettei",
        "url": "https://www.anchorterminal.com/compare/nace-drex-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-sqwish-kettei.json",
        "title": "OpenAI Decisions API vs kettei",
        "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/pplx-decider-flash-vs-sqwish-kettei.json",
        "title": "pplx-decider-flash vs kettei",
        "url": "https://www.anchorterminal.com/compare/pplx-decider-flash-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/pplx-decider-vs-sqwish-kettei.json",
        "title": "pplx-decider vs kettei",
        "url": "https://www.anchorterminal.com/compare/pplx-decider-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/scaledown-vs-sqwish-kettei.json",
        "title": "ScaleDown vs kettei",
        "url": "https://www.anchorterminal.com/compare/scaledown-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sprag-spev-vs-sqwish-kettei.json",
        "title": "SPEV vs kettei",
        "url": "https://www.anchorterminal.com/compare/sprag-spev-vs-sqwish-kettei"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-strands-decider.json",
        "title": "kettei vs Strands Decider 2B",
        "url": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-strands-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-typesafe-jev.json",
        "title": "kettei vs Jev",
        "url": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-typesafe-jev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela.json",
        "title": "kettei vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela"
      }
    ],
    "scores": [
      {
        "by": 13,
        "edge": "gliclass",
        "gliclass": 43,
        "key": "reliability",
        "name": "Reliability",
        "sqwish-kettei": 30,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 38,
        "edge": "sqwish-kettei",
        "gliclass": 49,
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "sqwish-kettei": 87,
        "weight": 13
      },
      {
        "by": 29,
        "edge": "sqwish-kettei",
        "gliclass": 60,
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "sqwish-kettei": 89,
        "weight": 13
      },
      {
        "by": 30,
        "edge": "sqwish-kettei",
        "gliclass": 38,
        "key": "security",
        "name": "Security \u0026 auth",
        "sqwish-kettei": 68,
        "weight": 14
      },
      {
        "by": 25,
        "edge": "gliclass",
        "gliclass": 60,
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "sqwish-kettei": 35,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 13,
        "edge": "sqwish-kettei",
        "gliclass": 44,
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "sqwish-kettei": 57,
        "weight": 7
      },
      {
        "by": 13,
        "edge": "gliclass",
        "gliclass": 64,
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "sqwish-kettei": 51,
        "weight": 7
      }
    ],
    "summary": "kettei scores 58.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on reliability, payments \u0026 pricing and transparency \u0026 trust. Both do decision models.",
    "tutorials": [],
    "verdicts": {
      "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.",
      "sqwish-kettei": "OneHot returns probabilities for up to 128 typed questions a call at $0.02 to $0.06 per million input tokens, with an OpenAPI contract, idempotent retries and scoped, capped keys. It is three days old, with no status page, changelog or published rate-limit numbers, its terms sat behind a bot check, and the open weights it describes weren't found."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei",
    "json": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.min.md"
  },
  "markdown": "kettei scores 58.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on reliability, payments \u0026 pricing and transparency \u0026 trust. Both do decision models.\n\n- GLiClass: grade D, 49.9/100, rank #884 of 1133. Markdown https://www.anchorterminal.com/tools/gliclass.md · JSON https://www.anchorterminal.com/api/v1/tools/gliclass.json\n- kettei: grade C, 58.3/100, rank #640 of 1133. Markdown https://www.anchorterminal.com/tools/sqwish-kettei.md · JSON https://www.anchorterminal.com/api/v1/tools/sqwish-kettei.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 190 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n\n## Which one, for what\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- Reliability, 43 against 30\n- Payments \u0026 pricing, 60 against 35\n- Transparency \u0026 trust, 64 against 51\n\nAlso in its favour:\n- No key needed to call it\n- Open source\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### kettei (C)\n\nGood for: High-volume yes or no, label and rubric decisions over text or images, such as ticket routing, tool choice, groundedness checks and tool-call risk, with hosted fine-tuning on the customer's own labels.\n\nAhead on:\n- Schema \u0026 documentation, 87 against 49\n- Agent ergonomics, 89 against 60\n- Security \u0026 auth, 68 against 38\n- Maintenance \u0026 community, 57 against 44\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Launched on 14 October 2026, with no status page, no changelog and no SLA\n\n\n## Score by category\n\n| Category | Weight | GLiClass | kettei | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 43 | 30 | GLiClass +13 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 49 | 87 | kettei +38 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 89 | kettei +29 |\n| Security \u0026 auth | 14% (17.5 this run) | 38 | 68 | kettei +30 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 35 | GLiClass +25 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 44 | 57 | kettei +13 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 64 | 51 | GLiClass +13 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **49.9 · D** | **58.3 · C** | |\n\n## Facts side by side\n\n| Fact | GLiClass | kettei |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Knowledgator | Sqwish Labs |\n| Hosted endpoint | no (local only) | `https://api.sqwish.ai/v1/decide` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (library and the model weights we checked) | Proprietary hosted models and API. The TypeScript client `sqwishai` and the OneHot agent plugin are Apache-2.0. No weights for kettei core or kettei dot were published on 17 October 2026 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-07-21 | 2026-10-15 |\n| Terms last updated | no document linked |  |\n| Privacy policy last updated | 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 | 555 stars, 13k PyPI/wk | 1.2k npm/wk |\n| Agent reviews | none | 3.8/5 (4) |\n\n## Verdicts\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**kettei.** OneHot returns probabilities for up to 128 typed questions a call at $0.02 to $0.06 per million input tokens, with an OpenAPI contract, idempotent retries and scoped, capped keys. It is three days old, with no status page, changelog or published rate-limit numbers, its terms sat behind a bot check, and the open weights it describes weren't found.\n\n## Before you call either\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### kettei\n\n1. Send `context` and `decisions` to `POST https://api.sqwish.ai/v1/decide` with a bearer key. Leaving out `model` gets `kettei-core`, which reads text only\n2. Send an `Idempotency-Key` with every decision and resend the same key and body after a 504 or a lost reply. Replays aren't charged\n3. Branch on `error.code` and `error.retryable`, not the status. Wait for `Retry-After` on 429 and retryable 503s\n4. Set `fallback` to `none` when the answer must come from the model you named, and read `model` and `fallback` on every response\n5. Use an inference-scoped key with a monthly cap for production, since a key can spend the balance until revoked\n\n## Questions\n\n### Which is better for AI agents, GLiClass or kettei?\n\nkettei scores 58.3 (C) on agent readiness against GLiClass's 49.9 (D), and leads in 4 of 7 scored categories. GLiClass leads on reliability, payments \u0026 pricing and transparency \u0026 trust.\n\n### Do GLiClass and kettei need an API key?\n\nGLiClass needs no key. kettei needs an API key.\n\n### Can an agent call GLiClass and kettei without installing anything?\n\nNo hosted endpoint is listed for GLiClass. kettei has a hosted endpoint at https://api.sqwish.ai/v1/decide.\n\n### Are GLiClass and kettei open source?\n\nGLiClass is open source (Apache-2.0 (library and the model weights we checked)). No open-source release is listed for kettei.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gliclass\", \"b\": \"sqwish-kettei\"}`. From a terminal: `anchor compare gliclass sqwish-kettei`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gliclass.json and https://www.anchorterminal.com/api/v1/tools/sqwish-kettei.json\n\n## Other comparisons with GLiClass or kettei\n\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Celeris-1 Decision vs kettei](https://www.anchorterminal.com/compare/celeris-1-decision-vs-sqwish-kettei.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Clef vs kettei](https://www.anchorterminal.com/compare/cloudflare-clef-vs-sqwish-kettei.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Laya vs kettei](https://www.anchorterminal.com/compare/convai-laya-vs-sqwish-kettei.md)\n- [Decider vs GLiClass](https://www.anchorterminal.com/compare/decider-vs-gliclass.md)\n- [Decider vs kettei](https://www.anchorterminal.com/compare/decider-vs-sqwish-kettei.md)\n- [Decision 3.0 vs GLiClass](https://www.anchorterminal.com/compare/decision-3-vs-gliclass.md)\n- [Decision 3.0 vs kettei](https://www.anchorterminal.com/compare/decision-3-vs-sqwish-kettei.md)\n- [GLiClass vs Granite Switch 4.1](https://www.anchorterminal.com/compare/gliclass-vs-granite-switch.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 Microsoft-Decision-1](https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1.md)\n- [GLiClass vs Drex 1.5](https://www.anchorterminal.com/compare/gliclass-vs-nace-drex.md)\n- [GLiClass vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.md)\n- [GLiClass vs pplx-decider](https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider.md)\n- [GLiClass vs pplx-decider-flash](https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash.md)\n- [GLiClass vs ScaleDown](https://www.anchorterminal.com/compare/gliclass-vs-scaledown.md)\n- [GLiClass vs SPEV](https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev.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- [Granite Switch 4.1 vs kettei](https://www.anchorterminal.com/compare/granite-switch-vs-sqwish-kettei.md)\n- [Kev vs kettei](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei.md)\n- [Liquid d1 vs kettei](https://www.anchorterminal.com/compare/liquid-d1-vs-sqwish-kettei.md)\n- [Microsoft-Decision-1 vs kettei](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-sqwish-kettei.md)\n- [Drex 1.5 vs kettei](https://www.anchorterminal.com/compare/nace-drex-vs-sqwish-kettei.md)\n- [OpenAI Decisions API vs kettei](https://www.anchorterminal.com/compare/openai-decisions-api-vs-sqwish-kettei.md)\n- [pplx-decider-flash vs kettei](https://www.anchorterminal.com/compare/pplx-decider-flash-vs-sqwish-kettei.md)\n- [pplx-decider vs kettei](https://www.anchorterminal.com/compare/pplx-decider-vs-sqwish-kettei.md)\n- [ScaleDown vs kettei](https://www.anchorterminal.com/compare/scaledown-vs-sqwish-kettei.md)\n- [SPEV vs kettei](https://www.anchorterminal.com/compare/sprag-spev-vs-sqwish-kettei.md)\n- [kettei vs Strands Decider 2B](https://www.anchorterminal.com/compare/sqwish-kettei-vs-strands-decider.md)\n- [kettei vs Jev](https://www.anchorterminal.com/compare/sqwish-kettei-vs-typesafe-jev.md)\n- [kettei vs Vela 2.0](https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela.md)\n",
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "attributionRequired": true,
    "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-18",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.4",
    "openapi": "https://www.anchorterminal.com/openapi.json",
    "preview": false,
    "run": "2026-10-01",
    "runLabel": "October 2026 research run",
    "terms": "https://www.anchorterminal.com/terms/#data"
  },
  "page": {
    "breadcrumbs": [
      {
        "name": "Home",
        "url": "https://www.anchorterminal.com/"
      },
      {
        "name": "Compare",
        "url": "https://www.anchorterminal.com/compare/"
      },
      {
        "name": "GLiClass vs kettei",
        "url": ""
      }
    ],
    "description": "kettei scores 58.3 (C) to GLiClass's 49.9 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.",
    "facts": [
      "GLiClass D 49.9",
      "kettei C 58.3",
      "scores"
    ],
    "h1": "GLiClass vs kettei",
    "image": "https://www.anchorterminal.com/assets/og/compare-gliclass-vs-sqwish-kettei.png",
    "path": "/compare/gliclass-vs-sqwish-kettei",
    "published": "2026-10-01",
    "section": "tools",
    "title": "GLiClass vs kettei for AI agents in 2026: scores and prices",
    "toc": null,
    "updated": "2026-10-17",
    "url": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei"
  },
  "tokens": {
    "markdown": 2850,
    "slim": 680
  },
  "version": 1
}