{ "data": { "a": { "slug": "liquid-d1", "name": "Liquid d1", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "model", "category": "decision-models", "summary": "d1 is Liquid AI's decision model family. It answers typed yes or no, choice and score questions about text and images with probabilities, through a hosted API and the open-weight d1-3B and d1-omni-600M models.", "url": "https://www.anchorterminal.com/tools/liquid-d1", "markdownUrl": "https://www.anchorterminal.com/tools/liquid-d1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/liquid-d1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/liquid-d1.json", "repo": "https://huggingface.co/LiquidAI/d1-3B", "license": "The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published", "transports": [ "http" ], "remoteUrl": "https://api.liquid.ai/decisions/v1/systemone", "packages": [], "auth": "api-key", "authNotes": "A key created at console.liquid.ai under Dashboard, API Keys, after registering and joining an organisation, sent as a Bearer header. Keys start with `liquid_`. No scopes, expiry or rotation were found in the reviewed documentation. The weights download from Hugging Face without an account.", "pricing": "freemium", "pricingNotes": "The hosted `d1` model costs $0.04 per million input tokens and bills no output tokens, per the launch post of 5 October 2026 (https://www.liquid.ai/blog/d1-decision-model). Each question is billed as its own prompt, and an image counts 1.5 tokens per 32 by 32 pixel patch. A text-only `d1:free` model exists, with no published limits. Liquid's pricing page covers model licensing only. The open weights are free to run, and commercial use is free below $10 million in annual revenue.", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the d1 docs, the launch posts or the terms of service (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.liquid.ai/lfm/models/decision-models", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "inference.decision" ], "tags": [ "hosted", "model", "open-weights", "source-available", "self-hosted", "llms-txt", "free-tier", "usage-priced" ], "lastRelease": "2026-10-07", "graded": true, "anchor": { "graded": true, "score": 43.5, "grade": "E", "agentReady": false, "rank": 960, "ranked": true, "rankOf": 1084, "categoryRank": 15, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 63, "maintenance": 60, "payments": 27, "reliability": 21, "schema": 59, "security": 35, "transparency": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.", "bestFor": "Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.", "strengths": [ "$0.04 per million input tokens with no output tokens billed, per the launch post, and a text-only `d1:free` model", "d1-3B (3.12B parameters, 32,768-token context) and d1-omni-600M are ungated on Hugging Face, with GGUF builds", "llama.cpp's server README documents `/v1/systemone` for d1, so the same request runs locally", "The hosted `d1` model accepts up to 8 images a request as Base64 data", "Docs are served as Markdown with an llms.txt index, and say when to use a language model instead" ], "weaknesses": [ "The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found", "No status page, rate limits, SLA or error reference found for the hosted API", "The LFM Open Licence v1.0 ends free commercial use at $10 million in annual revenue, so the weights aren't open source", "No OpenAPI file, API changelog or versioned model IDs. The hosted models are `d1` and `d1:free`", "No SDK of its own. TypeSafe's SDKs are the documented clients and don't send images" ], "agentNotes": [ "POST to https://api.liquid.ai/decisions/v1/systemone with a `liquid_` key as a Bearer header. This is not a chat-completions endpoint", "Use `d1` for images. `d1:free` is text-only and answers that it does not accept images", "Send images as Base64 data URLs in `images`, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused", "Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again", "Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 43.5 } ], "editorialScores": { "ergonomics": 63, "maintenance": 60, "payments": 27, "reliability": 21, "schema": 59, "security": 35, "transparency": 46 }, "provenanceScore": 62 }, "connect": { "install": "pip install typesafe-sdk # or: npm install @typesafe-ai/sdk", "http": "curl -s https://api.liquid.ai/decisions/v1/systemone \\\n -H \"Authorization: Bearer $LIQUID_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"d1\",\"state\":\"I have been waiting over three weeks for my order and nobody has responded to my emails.\",\"questions\":{\"is_complaint\":{\"type\":\"noul\",\"instructions\":\"Is this message a complaint from the customer?\"}}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/liquid-d1" }, "area": "models", "unitPrices": [ { "item": "d1 input", "unit": "1m-tokens", "usd": 0.04, "note": "No output tokens. Each question is billed as its own prompt, images at 1.5 tokens per 32 by 32 pixel patch" } ], "provenance": { "legalEntity": "Liquid AI, Inc.", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://www.liquid.ai/terms-conditions", "privacy": "https://www.liquid.ai/privacy-policy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The terms of service (updated 30 September 2026) name Liquid AI, Inc., a Delaware corporation, and Massachusetts law. The privacy policy carries the same date.", "RDAP gives liquid.ai a registration date of 16 December 2017 and a transfer on 20 April 2023. The site footer says the company was established in 2023.", "The hosted endpoint is on api.liquid.ai. The weights sit on huggingface.co under the LiquidAI organisation.", "/.well-known/security.txt returned 404 on www.liquid.ai, liquid.ai and api.liquid.ai.", "No status page is linked from the site, the docs or the launch posts, and status.liquid.ai did not answer our reader. No changelog for the API or the docs was found. docs.liquid.ai/changelog returned 404.", "A trust centre at trust.liquid.ai is hosted by Vanta and renders only with JavaScript, so its contents are unread." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/liquid-d1.json", "live": { "slug": "liquid-d1", "probe": { "target": "https://api.liquid.ai/decisions/v1/systemone", "method": "get", "lastAt": "2026-10-14T22:57:44.163978041Z", "lastOk": true, "lastStatus": 404, "lastMs": 159, "authRequired": false, "uptime24h": 100, "uptime30d": 99.94, "p50ms24h": 143, "p95ms24h": 388, "samples24h": 250, "samples30d": 1571, "days": [ { "date": "2026-10-08", "probes": 93, "ok": 93 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 247 }, { "date": "2026-10-14", "probes": 239, "ok": 239 } ] }, "securityTxt": { "url": "https://liquid.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:46:15.256461598Z" }, "llmsTxt": { "url": "https://docs.liquid.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-14T14:14:23.825722532Z" }, "domain": { "domain": "liquid.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/liquid.ai", "checkedAt": "2026-10-11T13:18:35.535693069Z" }, "pages": [ { "url": "https://www.liquid.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-14T21:14:20.37599983Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "42d9e4ba3a39" }, { "url": "https://www.liquid.ai/terms-conditions", "kind": "terms", "status": 200, "checkedAt": "2026-10-14T21:14:22.590776197Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "8fd8f3573ffc" } ], "updatedAt": "2026-10-14T22:57:44.163978041Z" } }, "answer": "ScaleDown scores 48.6 (D) on agent readiness against Liquid d1's 43.5 (E), and leads in 4 of 7 scored categories.", "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": "Liquid AI", "b": "Carbon ScaleDown, Inc.", "name": "Vendor" }, { "a": "https://api.liquid.ai/decisions/v1/systemone", "b": "https://api.scaledown.xyz", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published", "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": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-07", "b": "2026-10-14", "name": "Last release" }, { "a": "2026-09-30", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "2026-09-30", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "0 stars", "name": "Popularity" } ], "faq": [ { "answer": "ScaleDown scores 48.6 (D) on agent readiness against Liquid d1's 43.5 (E), and leads in 4 of 7 scored categories.", "question": "Which is better for AI agents, Liquid d1 or ScaleDown?" }, { "answer": "Both need an API key.", "question": "Do Liquid d1 and ScaleDown need an API key?" }, { "answer": "Yes. Liquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone and ScaleDown at https://api.scaledown.xyz.", "question": "Can an agent call Liquid d1 and ScaleDown without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.", "slug": "liquid-d1", "watchFor": "The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found" }, { "aheadOn": [ "Schema \u0026 documentation, 69 against 59", "Agent ergonomics, 76 against 63", "Security \u0026 auth, 48 against 35", "Payments \u0026 pricing, 40 against 27" ], "also": [ "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-liquid-d1.json", "title": "Celeris-1 Decision vs Liquid d1", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-liquid-d1" }, { "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-liquid-d1.json", "title": "Clef vs Liquid d1", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1" }, { "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-liquid-d1.json", "title": "Laya vs Liquid d1", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1" }, { "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-liquid-d1.json", "title": "Decider vs Liquid d1", "url": "https://www.anchorterminal.com/compare/decider-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/decider-vs-scaledown.json", "title": "Decider vs ScaleDown", "url": "https://www.anchorterminal.com/compare/decider-vs-scaledown" }, { "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-scaledown.json", "title": "Decision 3.0 vs ScaleDown", "url": "https://www.anchorterminal.com/compare/decision-3-vs-scaledown" }, { "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-scaledown.json", "title": "GLiClass vs ScaleDown", "url": "https://www.anchorterminal.com/compare/gliclass-vs-scaledown" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.json", "title": "Kev vs Liquid d1", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1" }, { "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-microsoft-decision-1.json", "title": "Liquid d1 vs Microsoft-Decision-1", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-microsoft-decision-1" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex.json", "title": "Liquid d1 vs Drex 1.5", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.json", "title": "Liquid d1 vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-pplx-decider.json", "title": "Liquid d1 vs pplx-decider", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-pplx-decider" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.json", "title": "Liquid d1 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.json", "title": "Liquid d1 vs Jev", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela.json", "title": "Liquid d1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela" }, { "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": 1, "edge": "liquid-d1", "key": "reliability", "liquid-d1": 21, "name": "Reliability", "scaledown": 20, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 10, "edge": "scaledown", "key": "schema", "liquid-d1": 59, "name": "Schema \u0026 documentation", "scaledown": 69, "weight": 13 }, { "by": 13, "edge": "scaledown", "key": "ergonomics", "liquid-d1": 63, "name": "Agent ergonomics", "scaledown": 76, "weight": 13 }, { "by": 13, "edge": "scaledown", "key": "security", "liquid-d1": 35, "name": "Security \u0026 auth", "scaledown": 48, "weight": 14 }, { "by": 13, "edge": "scaledown", "key": "payments", "liquid-d1": 27, "name": "Payments \u0026 pricing", "scaledown": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 4, "edge": "liquid-d1", "key": "maintenance", "liquid-d1": 60, "name": "Maintenance \u0026 community", "scaledown": 56, "weight": 7 }, { "by": 0, "edge": "", "key": "transparency", "liquid-d1": 54, "name": "Transparency \u0026 trust", "scaledown": 54, "weight": 7 } ], "summary": "ScaleDown scores 48.6 (D) on agent readiness against Liquid d1's 43.5 (E), and leads in 4 of 7 scored categories. Both do decision models.", "verdicts": { "liquid-d1": "The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.", "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/liquid-d1-vs-scaledown", "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.md", "slim": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.min.md" }, "markdown": "ScaleDown scores 48.6 (D) on agent readiness against Liquid d1's 43.5 (E), and leads in 4 of 7 scored categories. Both do decision models.\n\n- Liquid d1: grade E, 43.5/100, rank #960 of 1084. Markdown https://www.anchorterminal.com/tools/liquid-d1.md · JSON https://www.anchorterminal.com/api/v1/tools/liquid-d1.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### Liquid d1 (E)\n\nGood for: Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.\n\nWatch for: The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found\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- Schema \u0026 documentation, 69 against 59\n- Agent ergonomics, 76 against 63\n- Security \u0026 auth, 48 against 35\n- Payments \u0026 pricing, 40 against 27\n\nAlso in its favour:\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 | Liquid d1 | ScaleDown | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 21 | 20 | Liquid d1 +1 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 59 | 69 | ScaleDown +10 |\n| Agent ergonomics | 13% (16.2 this run) | 63 | 76 | ScaleDown +13 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 48 | ScaleDown +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 27 | 40 | ScaleDown +13 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 60 | 56 | Liquid d1 +4 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 54 | 54 | even |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **43.5 · E** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Liquid d1 | ScaleDown |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Liquid AI | Carbon ScaleDown, Inc. |\n| Hosted endpoint | `https://api.liquid.ai/decisions/v1/systemone` | `https://api.scaledown.xyz` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published | 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 | yes | yes |\n| Last release | 2026-10-07 | 2026-10-14 |\n| Terms last updated | 2026-09-30 | couldn't be read |\n| Privacy policy last updated | 2026-09-30 | couldn't be read |\n| Customer content may train models | yes | couldn't be read |\n| Terms restrict automated access | not found in the text | couldn't be read |\n| Terms restrict benchmarking | not found in the text | couldn't be read |\n| Terms or service can change without notice | not found in the text | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | none | 0 stars |\n\n## Verdicts\n\n**Liquid d1.** The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.\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### Liquid d1\n\n1. POST to https://api.liquid.ai/decisions/v1/systemone with a `liquid_` key as a Bearer header. This is not a chat-completions endpoint\n2. Use `d1` for images. `d1:free` is text-only and answers that it does not accept images\n3. Send images as Base64 data URLs in `images`, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused\n4. Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again\n5. Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data\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, Liquid d1 or ScaleDown?\n\nScaleDown scores 48.6 (D) on agent readiness against Liquid d1's 43.5 (E), and leads in 4 of 7 scored categories.\n\n### Do Liquid d1 and ScaleDown need an API key?\n\nBoth need an API key.\n\n### Can an agent call Liquid d1 and ScaleDown without installing anything?\n\nYes. Liquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone and ScaleDown at https://api.scaledown.xyz.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.json, and with the fewest tokens: https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"liquid-d1\", \"b\": \"scaledown\"}`. From a terminal: `anchor compare liquid-d1 scaledown`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/liquid-d1.json and https://www.anchorterminal.com/api/v1/tools/scaledown.json\n\n## Other comparisons with Liquid d1 or ScaleDown\n\n- [Celeris-1 Decision vs Liquid d1](https://www.anchorterminal.com/compare/celeris-1-decision-vs-liquid-d1.md)\n- [Celeris-1 Decision vs ScaleDown](https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.md)\n- [Clef vs Liquid d1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1.md)\n- [Clef vs ScaleDown](https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown.md)\n- [Laya vs Liquid d1](https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1.md)\n- [Laya vs ScaleDown](https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.md)\n- [Decider vs Liquid d1](https://www.anchorterminal.com/compare/decider-vs-liquid-d1.md)\n- [Decider vs ScaleDown](https://www.anchorterminal.com/compare/decider-vs-scaledown.md)\n- [Decision 3.0 vs Liquid d1](https://www.anchorterminal.com/compare/decision-3-vs-liquid-d1.md)\n- [Decision 3.0 vs ScaleDown](https://www.anchorterminal.com/compare/decision-3-vs-scaledown.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs ScaleDown](https://www.anchorterminal.com/compare/gliclass-vs-scaledown.md)\n- [Kev vs Liquid d1](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.md)\n- [Kev vs ScaleDown](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown.md)\n- [Liquid d1 vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/liquid-d1-vs-microsoft-decision-1.md)\n- [Liquid d1 vs Drex 1.5](https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex.md)\n- [Liquid d1 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.md)\n- [Liquid d1 vs pplx-decider](https://www.anchorterminal.com/compare/liquid-d1-vs-pplx-decider.md)\n- [Liquid d1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.md)\n- [Liquid d1 vs Jev](https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.md)\n- [Liquid d1 vs Vela 2.0](https://www.anchorterminal.com/compare/liquid-d1-vs-vela.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": "Liquid d1 vs ScaleDown", "url": "" } ], "description": "ScaleDown scores 48.6 (D) to Liquid d1's 43.5 (E) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Liquid d1 E 43.5", "ScaleDown D 48.6", "scores" ], "h1": "Liquid d1 vs ScaleDown", "image": "https://www.anchorterminal.com/assets/og/compare-liquid-d1-vs-scaledown.png", "path": "/compare/liquid-d1-vs-scaledown", "published": "2026-10-01", "section": "tools", "title": "Liquid d1 vs ScaleDown for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-14", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown" }, "tokens": { "markdown": 2600, "slim": 730 }, "version": 1 }