{
  "data": {
    "a": {
      "slug": "decision-3",
      "name": "Decision 3.0",
      "vendor": "vLLM Semantic Router Team",
      "vendorUrl": "https://vllm-sr.ai",
      "kind": "model",
      "category": "decision-models",
      "summary": "Decision 3.0 is a family of open-weight multimodal decision models from the vLLM Semantic Router team, 0.6B to 27B, under Apache-2.0. They answer choice, yes-or-no and score questions about text, images and video with a probability per option.",
      "url": "https://www.anchorterminal.com/tools/decision-3",
      "markdownUrl": "https://www.anchorterminal.com/tools/decision-3.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/decision-3.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/decision-3.json",
      "repo": "https://github.com/vllm-project/semantic-router",
      "license": "Apache-2.0 for the weights and the bundled runtime, built on Apache-2.0 Qwen3.5 and Qwen3.8 bases",
      "transports": [
        "http"
      ],
      "packages": [
        {
          "registry": "pypi",
          "name": "vllm-sr"
        }
      ],
      "auth": "none",
      "authNotes": "No account. The weights are public and ungated on Hugging Face. The bundled `d3_server.py` binds to 127.0.0.1 and is open unless `DECISION_API_KEY` is set, after which it requires `Authorization: Bearer \u003ckey\u003e` and answers 401 otherwise. `GET /health` stays open. The router's model runtime has no authentication.",
      "pricing": "free",
      "pricingNotes": "Free and open source, with nothing to buy and no hosted API from the authors. Hardware is the owner's cost. The cards give speeds on one AMD Instinct MI325X GPU, and a third-party Core ML build runs the three smaller sizes on a Mac (checked 2026-10-12).",
      "priceSummary": "Free · OSS",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402. Decision 3.0 is software the owner runs, and neither the bundled server nor the router's model runtime has a payment route (checked 2026-10-12).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-12"
      },
      "docsUrl": "https://huggingface.co/collections/vllm-sr/decision-30",
      "openapi": "https://raw.githubusercontent.com/vllm-project/semantic-router/main/src/model-runtime/vllm_srun/api/openapi.yaml",
      "capabilities": [
        "inference.decision"
      ],
      "tags": [
        "model",
        "open-source",
        "open-weights",
        "self-hosted",
        "local",
        "free",
        "python",
        "multimodal",
        "openapi"
      ],
      "lastRelease": "2026-10-11",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 63.1,
        "grade": "B",
        "agentReady": false,
        "rank": 435,
        "ranked": true,
        "rankOf": 1084,
        "categoryRank": 8,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 72,
          "maintenance": 82,
          "payments": 60,
          "reliability": 58,
          "schema": 70,
          "security": 56,
          "transparency": 45
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-12"
        },
        "negative": 0,
        "verdict": "Open Apache-2.0 decision models that read images and video as well as text and answer several typed questions in one call, with a probability per option and tagged Hub releases. They are two days old, need a GPU and `trust_remote_code`, drop Vela's set and span types, and publish no calibration figures.",
        "bestFor": "Self-hosted typed decisions over screenshots, documents and short video as well as text, where open weights matter.",
        "strengths": [
          "Text, images and video in one request, with every question seeing all of them",
          "Apache-2.0 weights on Apache-2.0 bases, ungated on Hugging Face, with a SHA-256 manifest the runtime checks at load",
          "Hub releases tagged v3.0.0 to v3.1.0, and the router pins a commit hash for each size",
          "An optional `permutation_average` mode scores choice questions in both option orders and averages them",
          "The bundled server takes only data URLs within stated size caps, so it fetches nothing from the network"
        ],
        "weaknesses": [
          "Released on 10 October 2026, with five tagged releases of each model by 11 October",
          "No calibration figures on the cards, though the category compares models on calibration",
          "Only choice, yes-or-no and score questions. No multi-label set or span answers",
          "Loading with `transformers` needs `trust_remote_code=True`, and the router's Engine mode needs its development channel",
          "No training data, training code or changelog published with the models"
        ],
        "agentNotes": [
          "Pin a tag or commit with `revision=` when loading from the Hub. Each model had five tagged releases on 10 and 11 October",
          "Send images and videos to `d3_server.py` as base64 data URLs only. The server refuses paths and http URLs with 422",
          "Set `DECISION_API_KEY` before binding the bundled server beyond 127.0.0.1. It is open otherwise",
          "Turn on `permutation_average=True` when routing must not depend on option order. It is off by default",
          "Expect one request at a time from the bundled server. Use the router's model runtime for queued serving"
        ],
        "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": 63.1
          }
        ],
        "editorialScores": {
          "ergonomics": 72,
          "maintenance": 82,
          "payments": 60,
          "reliability": 58,
          "schema": 70,
          "security": 56,
          "transparency": 62
        },
        "provenanceScore": 27
      },
      "connect": {
        "install": "pip install \"transformers==5.17.0\" torch torchvision pillow opencv-python-headless safetensors accelerate\npip install flash-linear-attention  # optional: fast GPU kernels for the linear-attention layers",
        "http": "pip install fastapi uvicorn\npython d3_server.py --model vllm-sr/d3-mini --device cuda:0 --port 8000"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/decision-3"
      },
      "sameCompany": [
        "vela"
      ],
      "area": "models",
      "provenance": {
        "legalEntity": "",
        "domain": "vllm-sr.ai",
        "domainRegistered": "2026-07-13",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://github.com/vllm-project/semantic-router/blob/main/src/model-runtime/docs/records/decision3-parity.md",
        "securityTxt": "none",
        "checked": "2026-10-12",
        "notes": [
          "An open-source project with no company named as publisher. The NOTICE files read Copyright 2026 vLLM Semantic Router Team.",
          "Domain registration date carried from the Vela 2.0 listing's RDAP check of 8 October 2026.",
          "Software the owner runs, so there's no hosted endpoint, terms or privacy policy. The Apache-2.0 licence stands in for terms.",
          "vllm-sr.ai/.well-known/security.txt returned 404 on 12 October 2026. SECURITY.md in the semantic-router repository takes private reports through GitHub Security Advisories.",
          "The Hub repositories have no changelog. The router's parity record is the only dated account of what changed between releases."
        ],
        "score": 27
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/decision-3.json",
      "live": {
        "slug": "decision-3",
        "versions": [
          {
            "registry": "github",
            "name": "vllm-project/semantic-router",
            "version": "v0.4.0",
            "released": "2026-09-27",
            "seenAt": "2026-10-13T22:52:20.855963989Z"
          },
          {
            "registry": "pypi",
            "name": "vllm-sr",
            "version": "0.4.0",
            "released": "2026-09-27",
            "seenAt": "2026-10-13T22:52:20.651941467Z"
          }
        ],
        "githubStars": 6140,
        "pypiWeekly": 11333,
        "securityTxt": {
          "url": "https://vllm-sr.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-14T15:46:34.901898231Z"
        },
        "pages": [
          {
            "url": "https://raw.githubusercontent.com/vllm-project/semantic-router/main/src/model-runtime/docs/records/decision3-parity.md",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-14T21:08:19.510931465Z",
            "changedAt": "2026-10-14T21:08:19.510931465Z",
            "fingerprint": "3fe50aaef2ee"
          }
        ],
        "updatedAt": "2026-10-14T21:08:19.510931465Z"
      }
    },
    "answer": "Decision 3.0 scores 63.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on transparency \u0026 trust.",
    "b": {
      "slug": "scaledown",
      "name": "ScaleDown",
      "vendor": "Carbon ScaleDown, Inc.",
      "vendorUrl": "https://scaledown.ai",
      "kind": "model",
      "category": "decision-models",
      "summary": "ScaleDown's API serves three single-task Constrained Decoding Models (Classify, Extract and Summarize) and a Compress model, called with an API key at api.scaledown.xyz. Classify answers Jev-shaped decision requests with probabilities.",
      "url": "https://www.anchorterminal.com/tools/scaledown",
      "markdownUrl": "https://www.anchorterminal.com/tools/scaledown.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/scaledown.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/scaledown.json",
      "repo": "https://github.com/scaledown-team/langchain-scaledown",
      "license": "Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.scaledown.xyz",
      "packages": [
        {
          "registry": "pypi",
          "name": "langchain-scaledown"
        }
      ],
      "auth": "api-key",
      "authNotes": "An API key in the `x-api-key` header. Extract and Summarize on `/v1/chat/completions` also accept `Authorization: Bearer`, so an unmodified OpenAI SDK works by changing the base URL. Keys are made in the dashboard after sign-up at scaledown.ai/auth. We found no documentation of key scopes, expiry or rotation. An unauthenticated request returns 401 with `{\"detail\":\"x-api-key header is required\"}`.",
      "pricing": "usage",
      "pricingNotes": "$0.05 per million input tokens for every model, with output tokens free (https://scaledown.ai, FAQ). Classify's optional one-sentence reasoning bills its extra input tokens at 2x. New accounts get 50 million free tokens with no card, per the homepage. The docs introduction instead says to contact sales for a key. OpenRouter lists Extract and Summarize at $0.04 per million input tokens. Enterprise and self-hosted plans are priced by sales.",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the docs, the OpenAPI document, the site or the LiteLLM and OpenRouter entries (checked 2026-10-14). Running out of credit returns 402 Payment Required as an ordinary error.",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 0,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-14"
      },
      "docsUrl": "https://docs.scaledown.ai/introduction",
      "llmsTxt": "https://docs.scaledown.ai/llms.txt",
      "openapi": "https://docs.scaledown.ai/api-reference/openapi.json",
      "capabilities": [
        "inference.decision",
        "docs.extract"
      ],
      "tags": [
        "hosted",
        "model",
        "closed-source",
        "openapi",
        "llms-txt",
        "openai-compatible",
        "usage-priced",
        "free-tier",
        "no-card",
        "python"
      ],
      "lastRelease": "2026-10-14",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 48.6,
        "grade": "D",
        "agentReady": false,
        "rank": 873,
        "ranked": true,
        "rankOf": 1084,
        "categoryRank": 14,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 76,
          "maintenance": 56,
          "payments": 40,
          "reliability": 20,
          "schema": 69,
          "security": 48,
          "transparency": 54
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-14"
        },
        "negative": -2,
        "negativeNotes": [
          "Conflicting compliance claims on ScaleDown's own pages, read 14 October 2026. The DPA (20 July 2026) says ScaleDown maintains a SOC 2 Type II attestation and ISO 27001 certification, the homepage shows SOC 2 Type 1, HIPAA and ISO 27001 badges, and llms.txt says GDPR, HIPAA and SOC 2 compliance is in progress. At least one is wrong. No report or trust centre found to settle it (-2) (https://scaledown.ai/dpa, https://scaledown.ai/llms.txt)"
        ],
        "verdict": "Classify answers Jev-shaped `choice`, `noul` and `score` questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.",
        "bestFor": "Cheap, high-volume labelling, routing and yes or no checks where a probability is more useful than prose, and for pulling described fields with offsets from text, images or PDFs.",
        "strengths": [
          "`POST /v1/scaledown` takes Jev's request shape, so a Jev integration can move by changing the base URL, per the docs",
          "$0.05 per million input tokens for every model, output free, and 50 million free tokens with no card, per the homepage",
          "Extract returns each value with a confidence, character offsets and surrounding context, and routes `enum` fields to the Classify model",
          "llms.txt with Markdown twins, an OpenAPI 3.1 document and OpenAI-compatible chat completions for Extract and Summarize",
          "The terms, DPA and FAQ agree that customer data isn't used for training, and the DPA lists four US subprocessors"
        ],
        "weaknesses": [
          "No status page, no published rate limits and no SLA outside enterprise contracts",
          "No public changelog. The docs dropped the native /extract and /summarization endpoints from the reference on 13 October 2026",
          "Compliance claims disagree between the DPA (SOC 2 Type II, ISO 27001), the homepage badges (SOC 2 Type 1) and llms.txt (in progress)",
          "No security.txt, disclosure policy or bug bounty, and no guidance on hostile text in classified input",
          "The OpenAPI document covers /classify, /v1/scaledown and Compress only, without the documented limits"
        ],
        "agentNotes": [
          "Send several questions about one item in one `/v1/scaledown` call. Each becomes its own Classify call and the usage is summed",
          "Pin `classify-1`, `extract-1` or `summarize-1` once you've tuned thresholds on your own labelled cases",
          "Set `context_chars` to 0 and use `top_n` on Extract unless you need the 500 characters of context each side",
          "Set `reasoning: false` on /classify when you only need the label. The reasoning call bills its input at 2x",
          "Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects"
        ],
        "metrics": {
          "kind": "remote",
          "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": 48.6
          }
        ],
        "editorialScores": {
          "ergonomics": 76,
          "maintenance": 56,
          "payments": 40,
          "reliability": 20,
          "schema": 69,
          "security": 48,
          "transparency": 56
        },
        "provenanceScore": 52
      },
      "connect": {
        "install": "pip install langchain-scaledown   # LangChain middleware; any OpenAI SDK also works against /v1/chat/completions",
        "http": "curl https://api.scaledown.xyz/v1/scaledown \\\n  -H \"x-api-key: $SCALEDOWN_API_KEY\" -H \"Content-Type: application/json\" \\\n  -d '{\"model\":\"classify-1\",\"state\":\"I was charged twice for my subscription this month.\",\"questions\":{\"category\":{\"type\":\"choice\",\"criteria\":{\"billing\":\"About a charge, invoice, refund, or payment problem.\",\"technical\":\"About a bug or something not working.\"}}}}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/scaledown"
      },
      "alsoIn": [
        "document-extraction"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Classify, Extract, Summarize and Compress input",
          "unit": "1m-tokens",
          "usd": 0.05,
          "note": "Output tokens free. Classify reasoning input billed at 2x. 50M free tokens for new accounts"
        }
      ],
      "provenance": {
        "legalEntity": "Carbon ScaleDown, Inc.",
        "domain": "scaledown.ai",
        "domainRegistered": "2025-03-28",
        "endpointOnVendorDomain": true,
        "terms": "https://scaledown.ai/terms",
        "privacy": "https://scaledown.ai/privacy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-14",
        "notes": [
          "The terms (last updated 20 July 2026), privacy notice (3 June 2026) and DPA (20 July 2026) name Carbon ScaleDown, Inc., 22952 Stokes Rd, Ramona, CA 92065, and Delaware law. One sentence of the terms names ScaleDown, Inc.",
          "scaledown.ai is a single-page app. /terms, /privacy and /dpa answer HTTP 404 with the app shell, and their text is in the JavaScript bundle (https://scaledown.ai/assets/index-CIrIjse9.js), where we read it.",
          "The API host is api.scaledown.xyz. scaledown.xyz redirects to scaledown.ai, so we treat the endpoint as on the vendor's domain.",
          "scaledown.ai/.well-known/security.txt and docs.scaledown.ai/.well-known/security.txt return 404. status.scaledown.ai and status.scaledown.xyz don't resolve.",
          "No public changelog. The docs source at github.com/scaledown-team/docs has dated commits that record API changes.",
          "RDAP gives scaledown.ai a registration date of 28 March 2025."
        ],
        "score": 52
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/scaledown.json",
      "live": {
        "slug": "scaledown",
        "probe": {
          "target": "https://api.scaledown.xyz",
          "method": "get",
          "lastAt": "2026-10-14T22:57:53.555186334Z",
          "lastOk": true,
          "lastStatus": 403,
          "lastMs": 282,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 269,
          "p95ms24h": 325,
          "samples24h": 37,
          "samples30d": 37,
          "days": [
            {
              "date": "2026-10-14",
              "probes": 37,
              "ok": 37
            }
          ]
        },
        "pages": [
          {
            "url": "https://scaledown.ai/privacy",
            "kind": "privacy",
            "status": 404,
            "checkedAt": "2026-10-14T21:06:40.247126332Z",
            "changedAt": "0001-01-01T00:00:00Z"
          },
          {
            "url": "https://scaledown.ai/terms",
            "kind": "terms",
            "status": 404,
            "checkedAt": "2026-10-14T21:06:42.626164273Z",
            "changedAt": "0001-01-01T00:00:00Z"
          }
        ],
        "updatedAt": "2026-10-14T22:57:53.555186334Z"
      }
    },
    "facts": [
      {
        "a": "Model API",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "vLLM Semantic Router Team",
        "b": "Carbon ScaleDown, Inc.",
        "name": "Vendor"
      },
      {
        "a": "no (local only)",
        "b": "https://api.scaledown.xyz",
        "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 for the weights and the bundled runtime, built on Apache-2.0 Qwen3.5 and Qwen3.8 bases",
        "b": "Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-11",
        "b": "2026-10-14",
        "name": "Last release"
      },
      {
        "a": "no document linked",
        "b": "couldn't be read",
        "name": "Terms last updated"
      },
      {
        "a": "no document linked",
        "b": "couldn't be read",
        "name": "Privacy policy last updated"
      },
      {
        "a": "",
        "b": "couldn't be read",
        "name": "Customer content may train models"
      },
      {
        "a": "",
        "b": "couldn't be read",
        "name": "Terms restrict automated access"
      },
      {
        "a": "",
        "b": "couldn't be read",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "",
        "b": "couldn't be read",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "",
        "b": "couldn't be read",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "none",
        "b": "0 stars",
        "name": "Popularity"
      }
    ],
    "faq": [
      {
        "answer": "Decision 3.0 scores 63.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on transparency \u0026 trust.",
        "question": "Which is better for AI agents, Decision 3.0 or ScaleDown?"
      },
      {
        "answer": "Decision 3.0 needs no key. ScaleDown needs an API key.",
        "question": "Do Decision 3.0 and ScaleDown need an API key?"
      },
      {
        "answer": "No hosted endpoint is listed for Decision 3.0. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.",
        "question": "Can an agent call Decision 3.0 and ScaleDown without installing anything?"
      },
      {
        "answer": "Decision 3.0 is open source (Apache-2.0 for the weights and the bundled runtime, built on Apache-2.0 Qwen3.5 and Qwen3.8 bases). No open-source release is listed for ScaleDown.",
        "question": "Are Decision 3.0 and ScaleDown open source?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 58 against 20",
          "Security \u0026 auth, 56 against 48",
          "Payments \u0026 pricing, 60 against 40",
          "Maintenance \u0026 community, 82 against 56"
        ],
        "also": [
          "No key needed to call it",
          "Open source"
        ],
        "goodFor": "Self-hosted typed decisions over screenshots, documents and short video as well as text, where open weights matter.",
        "slug": "decision-3",
        "watchFor": "Released on 10 October 2026, with five tagged releases of each model by 11 October"
      },
      {
        "aheadOn": [
          "Transparency \u0026 trust, 54 against 45"
        ],
        "also": [
          "A hosted endpoint, with nothing to install",
          "Free to start without a card"
        ],
        "goodFor": "Cheap, high-volume labelling, routing and yes or no checks where a probability is more useful than prose, and for pulling described fields with offsets from text, images or PDFs.",
        "slug": "scaledown",
        "watchFor": "No status page, no published rate limits and no SLA outside enterprise contracts"
      }
    ],
    "job": {
      "capability": "inference.decision",
      "name": "Decision models"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decision-3.json",
        "title": "Celeris-1 Decision vs Decision 3.0",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decision-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.json",
        "title": "Celeris-1 Decision vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-decision-3.json",
        "title": "Clef vs Decision 3.0",
        "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-decision-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown.json",
        "title": "Clef vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/convai-laya-vs-decision-3.json",
        "title": "Laya vs Decision 3.0",
        "url": "https://www.anchorterminal.com/compare/convai-laya-vs-decision-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.json",
        "title": "Laya vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-decision-3.json",
        "title": "Decider vs Decision 3.0",
        "url": "https://www.anchorterminal.com/compare/decider-vs-decision-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-scaledown.json",
        "title": "Decider vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/decider-vs-scaledown"
      },
      {
        "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-jaredpalmer-kev.json",
        "title": "Decision 3.0 vs Kev",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-jaredpalmer-kev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-liquid-d1.json",
        "title": "Decision 3.0 vs Liquid d1",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-liquid-d1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-microsoft-decision-1.json",
        "title": "Decision 3.0 vs Microsoft-Decision-1",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-microsoft-decision-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-nace-drex.json",
        "title": "Decision 3.0 vs Drex 1.5",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-nace-drex"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-openai-decisions-api.json",
        "title": "Decision 3.0 vs OpenAI Decisions API",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-openai-decisions-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-pplx-decider.json",
        "title": "Decision 3.0 vs pplx-decider",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-pplx-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-strands-decider.json",
        "title": "Decision 3.0 vs Strands Decider 2B",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-strands-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-typesafe-jev.json",
        "title": "Decision 3.0 vs Jev",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-typesafe-jev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/decision-3-vs-vela.json",
        "title": "Decision 3.0 vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/decision-3-vs-vela"
      },
      {
        "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/jaredpalmer-kev-vs-scaledown.json",
        "title": "Kev vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.json",
        "title": "Liquid d1 vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown.json",
        "title": "Microsoft-Decision-1 vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nace-drex-vs-scaledown.json",
        "title": "Drex 1.5 vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/nace-drex-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-scaledown.json",
        "title": "OpenAI Decisions API vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/pplx-decider-vs-scaledown.json",
        "title": "pplx-decider vs ScaleDown",
        "url": "https://www.anchorterminal.com/compare/pplx-decider-vs-scaledown"
      },
      {
        "json": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.json",
        "title": "ScaleDown vs Strands Decider 2B",
        "url": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider"
      },
      {
        "json": "https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.json",
        "title": "ScaleDown vs Jev",
        "url": "https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev"
      },
      {
        "json": "https://www.anchorterminal.com/compare/scaledown-vs-vela.json",
        "title": "ScaleDown vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/scaledown-vs-vela"
      }
    ],
    "scores": [
      {
        "by": 38,
        "decision-3": 58,
        "edge": "decision-3",
        "key": "reliability",
        "name": "Reliability",
        "scaledown": 20,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 1,
        "decision-3": 70,
        "edge": "decision-3",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "scaledown": 69,
        "weight": 13
      },
      {
        "by": 4,
        "decision-3": 72,
        "edge": "scaledown",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "scaledown": 76,
        "weight": 13
      },
      {
        "by": 8,
        "decision-3": 56,
        "edge": "decision-3",
        "key": "security",
        "name": "Security \u0026 auth",
        "scaledown": 48,
        "weight": 14
      },
      {
        "by": 20,
        "decision-3": 60,
        "edge": "decision-3",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "scaledown": 40,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 26,
        "decision-3": 82,
        "edge": "decision-3",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "scaledown": 56,
        "weight": 7
      },
      {
        "by": 9,
        "decision-3": 45,
        "edge": "scaledown",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "scaledown": 54,
        "weight": 7
      }
    ],
    "summary": "Decision 3.0 scores 63.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on transparency \u0026 trust. Both do decision models.",
    "verdicts": {
      "decision-3": "Open Apache-2.0 decision models that read images and video as well as text and answer several typed questions in one call, with a probability per option and tagged Hub releases. They are two days old, need a GPU and `trust_remote_code`, drop Vela's set and span types, and publish no calibration figures.",
      "scaledown": "Classify answers Jev-shaped `choice`, `noul` and `score` questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown",
    "json": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown.md",
    "slim": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown.min.md"
  },
  "markdown": "Decision 3.0 scores 63.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on transparency \u0026 trust. Both do decision models.\n\n- Decision 3.0: grade B, 63.1/100, rank #435 of 1084. Markdown https://www.anchorterminal.com/tools/decision-3.md · JSON https://www.anchorterminal.com/api/v1/tools/decision-3.json\n- ScaleDown: grade D, 48.6/100, rank #873 of 1084. Markdown https://www.anchorterminal.com/tools/scaledown.md · JSON https://www.anchorterminal.com/api/v1/tools/scaledown.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 120 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md\n- All 124 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md\n\n## Which one, for what\n\n### Decision 3.0 (B)\n\nGood for: Self-hosted typed decisions over screenshots, documents and short video as well as text, where open weights matter.\n\nAhead on:\n- Reliability, 58 against 20\n- Security \u0026 auth, 56 against 48\n- Payments \u0026 pricing, 60 against 40\n- Maintenance \u0026 community, 82 against 56\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Released on 10 October 2026, with five tagged releases of each model by 11 October\n\n### ScaleDown (D)\n\nGood for: Cheap, high-volume labelling, routing and yes or no checks where a probability is more useful than prose, and for pulling described fields with offsets from text, images or PDFs.\n\nAhead on:\n- Transparency \u0026 trust, 54 against 45\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n- Free to start without a card\n\nWatch for: No status page, no published rate limits and no SLA outside enterprise contracts\n\n\n## Score by category\n\n| Category | Weight | Decision 3.0 | ScaleDown | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 58 | 20 | Decision 3.0 +38 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 70 | 69 | Decision 3.0 +1 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 76 | ScaleDown +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 56 | 48 | Decision 3.0 +8 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 40 | Decision 3.0 +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 82 | 56 | Decision 3.0 +26 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 45 | 54 | ScaleDown +9 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **63.1 · B** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Decision 3.0 | ScaleDown |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | vLLM Semantic Router Team | Carbon ScaleDown, Inc. |\n| Hosted endpoint | no (local only) | `https://api.scaledown.xyz` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 for the weights and the bundled runtime, built on Apache-2.0 Qwen3.5 and Qwen3.8 bases | Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-11 | 2026-10-14 |\n| Terms last updated | no document linked | couldn't be read |\n| Privacy policy last updated | no document linked | couldn't be read |\n| Customer content may train models |  | couldn't be read |\n| Terms restrict automated access |  | couldn't be read |\n| Terms restrict benchmarking |  | couldn't be read |\n| Terms or service can change without notice |  | couldn't be read |\n| Arbitration or class-action waiver |  | couldn't be read |\n| Popularity | none | 0 stars |\n\n## Verdicts\n\n**Decision 3.0.** Open Apache-2.0 decision models that read images and video as well as text and answer several typed questions in one call, with a probability per option and tagged Hub releases. They are two days old, need a GPU and `trust_remote_code`, drop Vela's set and span types, and publish no calibration figures.\n\n**ScaleDown.** Classify answers Jev-shaped `choice`, `noul` and `score` questions with probabilities, and Extract returns values with confidences and character offsets, at $0.05 per million input tokens with output free. There is no status page, no published rate limit and no public changelog, and the site's compliance claims contradict each other.\n\n## Before you call either\n\n### Decision 3.0\n\n1. Pin a tag or commit with `revision=` when loading from the Hub. Each model had five tagged releases on 10 and 11 October\n2. Send images and videos to `d3_server.py` as base64 data URLs only. The server refuses paths and http URLs with 422\n3. Set `DECISION_API_KEY` before binding the bundled server beyond 127.0.0.1. It is open otherwise\n4. Turn on `permutation_average=True` when routing must not depend on option order. It is off by default\n5. Expect one request at a time from the bundled server. Use the router's model runtime for queued serving\n\n### ScaleDown\n\n1. Send several questions about one item in one `/v1/scaledown` call. Each becomes its own Classify call and the usage is summed\n2. Pin `classify-1`, `extract-1` or `summarize-1` once you've tuned thresholds on your own labelled cases\n3. Set `context_chars` to 0 and use `top_n` on Extract unless you need the 500 characters of context each side\n4. Set `reasoning: false` on /classify when you only need the label. The reasoning call bills its input at 2x\n5. Back off and retry on 429, 502 and 504. No limits are published and calls have no side effects\n\n## Questions\n\n### Which is better for AI agents, Decision 3.0 or ScaleDown?\n\nDecision 3.0 scores 63.1 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on transparency \u0026 trust.\n\n### Do Decision 3.0 and ScaleDown need an API key?\n\nDecision 3.0 needs no key. ScaleDown needs an API key.\n\n### Can an agent call Decision 3.0 and ScaleDown without installing anything?\n\nNo hosted endpoint is listed for Decision 3.0. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.\n\n### Are Decision 3.0 and ScaleDown open source?\n\nDecision 3.0 is open source (Apache-2.0 for the weights and the bundled runtime, built on Apache-2.0 Qwen3.5 and Qwen3.8 bases). No open-source release is listed for ScaleDown.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/decision-3-vs-scaledown.json, and with the fewest tokens: https://www.anchorterminal.com/compare/decision-3-vs-scaledown.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"decision-3\", \"b\": \"scaledown\"}`. From a terminal: `anchor compare decision-3 scaledown`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/decision-3.json and https://www.anchorterminal.com/api/v1/tools/scaledown.json\n\n## Other comparisons with Decision 3.0 or ScaleDown\n\n- [Celeris-1 Decision vs Decision 3.0](https://www.anchorterminal.com/compare/celeris-1-decision-vs-decision-3.md)\n- [Celeris-1 Decision vs ScaleDown](https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.md)\n- [Clef vs Decision 3.0](https://www.anchorterminal.com/compare/cloudflare-clef-vs-decision-3.md)\n- [Clef vs ScaleDown](https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown.md)\n- [Laya vs Decision 3.0](https://www.anchorterminal.com/compare/convai-laya-vs-decision-3.md)\n- [Laya vs ScaleDown](https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.md)\n- [Decider vs Decision 3.0](https://www.anchorterminal.com/compare/decider-vs-decision-3.md)\n- [Decider vs ScaleDown](https://www.anchorterminal.com/compare/decider-vs-scaledown.md)\n- [Decision 3.0 vs GLiClass](https://www.anchorterminal.com/compare/decision-3-vs-gliclass.md)\n- [Decision 3.0 vs Kev](https://www.anchorterminal.com/compare/decision-3-vs-jaredpalmer-kev.md)\n- [Decision 3.0 vs Liquid d1](https://www.anchorterminal.com/compare/decision-3-vs-liquid-d1.md)\n- [Decision 3.0 vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/decision-3-vs-microsoft-decision-1.md)\n- [Decision 3.0 vs Drex 1.5](https://www.anchorterminal.com/compare/decision-3-vs-nace-drex.md)\n- [Decision 3.0 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/decision-3-vs-openai-decisions-api.md)\n- [Decision 3.0 vs pplx-decider](https://www.anchorterminal.com/compare/decision-3-vs-pplx-decider.md)\n- [Decision 3.0 vs Strands Decider 2B](https://www.anchorterminal.com/compare/decision-3-vs-strands-decider.md)\n- [Decision 3.0 vs Jev](https://www.anchorterminal.com/compare/decision-3-vs-typesafe-jev.md)\n- [Decision 3.0 vs Vela 2.0](https://www.anchorterminal.com/compare/decision-3-vs-vela.md)\n- [GLiClass vs ScaleDown](https://www.anchorterminal.com/compare/gliclass-vs-scaledown.md)\n- [Kev vs ScaleDown](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown.md)\n- [Liquid d1 vs ScaleDown](https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.md)\n- [Microsoft-Decision-1 vs ScaleDown](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown.md)\n- [Drex 1.5 vs ScaleDown](https://www.anchorterminal.com/compare/nace-drex-vs-scaledown.md)\n- [OpenAI Decisions API vs ScaleDown](https://www.anchorterminal.com/compare/openai-decisions-api-vs-scaledown.md)\n- [pplx-decider vs ScaleDown](https://www.anchorterminal.com/compare/pplx-decider-vs-scaledown.md)\n- [ScaleDown vs Strands Decider 2B](https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.md)\n- [ScaleDown vs Jev](https://www.anchorterminal.com/compare/scaledown-vs-typesafe-jev.md)\n- [ScaleDown vs Vela 2.0](https://www.anchorterminal.com/compare/scaledown-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": "2026-10-14",
    "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": "Decision 3.0 vs ScaleDown",
        "url": ""
      }
    ],
    "description": "Decision 3.0 scores 63.1 (B) to ScaleDown's 48.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.",
    "facts": [
      "Decision 3.0 B 63.1",
      "ScaleDown D 48.6",
      "scores"
    ],
    "h1": "Decision 3.0 vs ScaleDown",
    "image": "https://www.anchorterminal.com/assets/og/compare-decision-3-vs-scaledown.png",
    "path": "/compare/decision-3-vs-scaledown",
    "published": "2026-10-01",
    "section": "tools",
    "title": "Decision 3.0 vs ScaleDown for AI agents in 2026: scores and prices",
    "toc": null,
    "updated": "2026-10-14",
    "url": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown"
  },
  "tokens": {
    "markdown": 2650,
    "slim": 730
  },
  "version": 1
}