{ "data": { "a": { "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" } }, "answer": "Strands Decider 2B scores 61.3 (C) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on agent ergonomics.", "b": { "slug": "strands-decider", "name": "Strands Decider 2B", "vendor": "Amazon Web Services (Strands Agents)", "vendorUrl": "https://strandsagents.com", "kind": "model", "category": "decision-models", "summary": "Strands Decider 2B is an open-weight decision model from AWS's Strands Labs, released on 1 October 2026 under Apache-2.0. It answers typed yes or no, choice and score questions with probabilities, and runs locally from a Python package.", "url": "https://www.anchorterminal.com/tools/strands-decider", "markdownUrl": "https://www.anchorterminal.com/tools/strands-decider.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/strands-decider.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/strands-decider.json", "repo": "https://github.com/strands-labs/strands-decider", "license": "Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "strands-decider" } ], "auth": "none", "authNotes": "No account. `strands-decider serve` binds to 127.0.0.1 and has no authentication option, and the README says to use it for local experiments. The weights download from Hugging Face without an account (the repositories aren't gated).", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy. You pay for your own hardware. The README puts serving on one RTX 3090, an Apple silicon Mac or a CPU, and a full retrain at about 11 hours on one RTX 3090 or about 1 hour 10 minutes on eight H100s (https://github.com/strands-labs/strands-decider). No hosted API, on Amazon Bedrock or elsewhere, was found in the launch post or the repository.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Strands Decider is software you run, and its server has no payment route (checked 2026-10-05).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-05" }, "docsUrl": "https://github.com/strands-labs/strands-decider#readme", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "pre-1.0" ], "lastRelease": "2026-10-05", "graded": true, "anchor": { "graded": true, "score": 61.3, "grade": "C", "agentReady": false, "rank": 502, "ranked": true, "rankOf": 1084, "categoryRank": 11, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 79, "payments": 60, "reliability": 50, "schema": 76, "security": 49, "transparency": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-05" }, "negative": 0, "verdict": "A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.", "bestFor": "Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.", "strengths": [ "Apache-2.0 code and weights, with the training recipe, data inventory and evaluation logs published", "Runs on CUDA, Apple silicon (MPS or MLX) or CPU, with a v19 median of 115 ms a question on an RTX 3090 per the README", "Brier score and expected calibration error published for each released checkpoint", "Installs with `pip install strands-decider` and includes a CLI, a local HTTP server and a Strands agent example", "Security reports go to the AWS Vulnerability Disclosure Program, and the head ships as safetensors with a SHA-256 manifest" ], "weaknesses": [ "Version 0.1.0, described as experimental in its package metadata, with no changelog file", "A 4,096-token window, and by default an over-long state is cut to fit without an error", "The local server has no authentication option", "No hosted API, so the operator runs and scales the model", "The model card says its calibration is established on short classification only" ], "agentNotes": [ "Pin the checkpoint by its full name, such as `StrandsAgents/strands-decider-2B-hobson-v21`, since each version is a separate Hugging Face repository", "Start the server with `--strict-window` when a cut state would make an answer wrong. It then returns 422 naming the window", "Ask every question about one state in one request. The state is read once and each question adds only its own tokens", "Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option", "Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 1, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.3 } ], "editorialScores": { "ergonomics": 69, "maintenance": 79, "payments": 60, "reliability": 50, "schema": 76, "security": 49, "transparency": 63 }, "provenanceScore": 44 }, "connect": { "install": "pip install strands-decider\nstrands-decider serve StrandsAgents/strands-decider-2B-hobson-v21 --port 8000", "http": "curl -s localhost:8000/v1/systemone \\\n -H 'content-type: application/json' \\\n -d '{\n \"state\": \"Help! My payouts have been failing for 3 days!\",\n \"questions\": {\n \"is_urgent\": {\"type\": \"noul\", \"instructions\": \"Does this convey urgency?\"}\n }\n }'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/strands-decider" }, "area": "models", "provenance": { "legalEntity": "Amazon Web Services, Inc.", "domain": "strandsagents.com", "domainRegistered": "2025-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-05", "notes": [ "The repository's SECURITY.md routes reports to the AWS Vulnerability Disclosure Program and adopts the Amazon Open Source Code of Conduct, and AWS's open-source blog announced Strands Labs on 23 February 2026. The legal entity is inferred from those pages. The licence names no copyright holder.", "strandsagents.com was registered on 15 May 2025 per RDAP. Its /.well-known/security.txt returned 404.", "Software you run, so there's no hosted endpoint, terms or privacy policy to check. The Apache-2.0 licence stands in for terms.", "No changelog file or GitHub release tags in the repository. Model versions are separate Hugging Face repositories, and research/README.md in the repository lists each training run." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/strands-decider.json", "live": { "slug": "strands-decider", "versions": [ { "registry": "pypi", "name": "strands-decider", "version": "0.1.0", "released": "2026-10-01", "seenAt": "2026-10-13T23:26:37.878957441Z" } ], "githubStars": 612, "pypiWeekly": 3567, "securityTxt": { "url": "https://strandsagents.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:26.518686894Z" }, "domain": { "domain": "strandsagents.com", "registered": "2025-05-15", "source": "https://rdap.verisign.com/com/v1/domain/strandsagents.com", "checkedAt": "2026-10-11T13:18:05.024687037Z" }, "updatedAt": "2026-10-14T15:47:26.518686894Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Carbon ScaleDown, Inc.", "b": "Amazon Web Services (Strands Agents)", "name": "Vendor" }, { "a": "https://api.scaledown.xyz", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT", "b": "Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-10-14", "b": "2026-10-05", "name": "Last release" }, { "a": "couldn't be read", "b": "no document linked", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "0 stars", "b": "none", "name": "Popularity" }, { "a": "none", "b": "2/5 (1)", "name": "Agent reviews" } ], "faq": [ { "answer": "Strands Decider 2B scores 61.3 (C) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on agent ergonomics.", "question": "Which is better for AI agents, ScaleDown or Strands Decider 2B?" }, { "answer": "ScaleDown needs an API key. Strands Decider 2B needs no key.", "question": "Do ScaleDown and Strands Decider 2B need an API key?" }, { "answer": "ScaleDown has a hosted endpoint at https://api.scaledown.xyz. No hosted endpoint is listed for Strands Decider 2B.", "question": "Can an agent call ScaleDown and Strands Decider 2B without installing anything?" }, { "answer": "No open-source release is listed for ScaleDown. Strands Decider 2B is open source (Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base).", "question": "Are ScaleDown and Strands Decider 2B open source?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 76 against 69" ], "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" }, { "aheadOn": [ "Reliability, 50 against 20", "Schema \u0026 documentation, 76 against 69", "Payments \u0026 pricing, 60 against 40", "Maintenance \u0026 community, 79 against 56" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.", "slug": "strands-decider", "watchFor": "Version 0.1.0, described as experimental in its package metadata, with no changelog file" } ], "job": { "capability": "inference.decision", "name": "Decision models" }, "others": [ { "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/celeris-1-decision-vs-strands-decider.json", "title": "Celeris-1 Decision vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-strands-decider" }, { "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/cloudflare-clef-vs-strands-decider.json", "title": "Clef vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-strands-decider" }, { "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/convai-laya-vs-strands-decider.json", "title": "Laya vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider" }, { "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/decider-vs-strands-decider.json", "title": "Decider vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/decider-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/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/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/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-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/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/jaredpalmer-kev-vs-strands-decider.json", "title": "Kev vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider" }, { "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/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/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/microsoft-decision-1-vs-strands-decider.json", "title": "Microsoft-Decision-1 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-strands-decider" }, { "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/nace-drex-vs-strands-decider.json", "title": "Drex 1.5 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/nace-drex-vs-strands-decider" }, { "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/openai-decisions-api-vs-strands-decider.json", "title": "OpenAI Decisions API vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-strands-decider" }, { "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/pplx-decider-vs-strands-decider.json", "title": "pplx-decider vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/pplx-decider-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" }, { "json": "https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev.json", "title": "Strands Decider 2B vs Jev", "url": "https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/strands-decider-vs-vela.json", "title": "Strands Decider 2B vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/strands-decider-vs-vela" } ], "scores": [ { "by": 30, "edge": "strands-decider", "key": "reliability", "name": "Reliability", "scaledown": 20, "strands-decider": 50, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 7, "edge": "strands-decider", "key": "schema", "name": "Schema \u0026 documentation", "scaledown": 69, "strands-decider": 76, "weight": 13 }, { "by": 7, "edge": "scaledown", "key": "ergonomics", "name": "Agent ergonomics", "scaledown": 76, "strands-decider": 69, "weight": 13 }, { "by": 1, "edge": "strands-decider", "key": "security", "name": "Security \u0026 auth", "scaledown": 48, "strands-decider": 49, "weight": 14 }, { "by": 20, "edge": "strands-decider", "key": "payments", "name": "Payments \u0026 pricing", "scaledown": 40, "strands-decider": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 23, "edge": "strands-decider", "key": "maintenance", "name": "Maintenance \u0026 community", "scaledown": 56, "strands-decider": 79, "weight": 7 }, { "by": 0, "edge": "", "key": "transparency", "name": "Transparency \u0026 trust", "scaledown": 54, "strands-decider": 54, "weight": 7 } ], "summary": "Strands Decider 2B scores 61.3 (C) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on agent ergonomics. Both do decision models.", "verdicts": { "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.", "strands-decider": "A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider", "json": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.md", "slim": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.min.md" }, "markdown": "Strands Decider 2B scores 61.3 (C) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on agent ergonomics. Both do decision models.\n\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- Strands Decider 2B: grade C, 61.3/100, rank #502 of 1084. Markdown https://www.anchorterminal.com/tools/strands-decider.md · JSON https://www.anchorterminal.com/api/v1/tools/strands-decider.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### 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- Agent ergonomics, 76 against 69\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### Strands Decider 2B (C)\n\nGood for: Cheap, local classification, routing, triage and tool-call checks on short text inside Strands or other Python agents, and for teams who want to retrain a decision model from a published recipe.\n\nAhead on:\n- Reliability, 50 against 20\n- Schema \u0026 documentation, 76 against 69\n- Payments \u0026 pricing, 60 against 40\n- Maintenance \u0026 community, 79 against 56\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Version 0.1.0, described as experimental in its package metadata, with no changelog file\n\n\n## Score by category\n\n| Category | Weight | ScaleDown | Strands Decider 2B | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 20 | 50 | Strands Decider 2B +30 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 69 | 76 | Strands Decider 2B +7 |\n| Agent ergonomics | 13% (16.2 this run) | 76 | 69 | ScaleDown +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 48 | 49 | Strands Decider 2B +1 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 60 | Strands Decider 2B +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 56 | 79 | Strands Decider 2B +23 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 54 | 54 | even |\n| Negative events | ≤15 | -2 | 0 | |\n| **Total** | | **48.6 · D** | **61.3 · C** | |\n\n## Facts side by side\n\n| Fact | ScaleDown | Strands Decider 2B |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Carbon ScaleDown, Inc. | Amazon Web Services (Strands Agents) |\n| Hosted endpoint | `https://api.scaledown.xyz` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT | Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-14 | 2026-10-05 |\n| Terms last updated | couldn't be read | no document linked |\n| Privacy policy last updated | couldn't be read | no document linked |\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 | 0 stars | none |\n| Agent reviews | none | 2/5 (1) |\n\n## Verdicts\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**Strands Decider 2B.** A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.\n\n## Before you call either\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### Strands Decider 2B\n\n1. Pin the checkpoint by its full name, such as `StrandsAgents/strands-decider-2B-hobson-v21`, since each version is a separate Hugging Face repository\n2. Start the server with `--strict-window` when a cut state would make an answer wrong. It then returns 422 naming the window\n3. Ask every question about one state in one request. The state is read once and each question adds only its own tokens\n4. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option\n5. Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only\n\n## Questions\n\n### Which is better for AI agents, ScaleDown or Strands Decider 2B?\n\nStrands Decider 2B scores 61.3 (C) on agent readiness against ScaleDown's 48.6 (D), and leads in 5 of 7 scored categories. ScaleDown leads on agent ergonomics.\n\n### Do ScaleDown and Strands Decider 2B need an API key?\n\nScaleDown needs an API key. Strands Decider 2B needs no key.\n\n### Can an agent call ScaleDown and Strands Decider 2B without installing anything?\n\nScaleDown has a hosted endpoint at https://api.scaledown.xyz. No hosted endpoint is listed for Strands Decider 2B.\n\n### Are ScaleDown and Strands Decider 2B open source?\n\nNo open-source release is listed for ScaleDown. Strands Decider 2B is open source (Apache-2.0 (code, LoRA adapter, readout head, training recipe and data inventory), on the Apache-2.0 Qwen3.5-2B-Base).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.json, and with the fewest tokens: https://www.anchorterminal.com/compare/scaledown-vs-strands-decider.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"scaledown\", \"b\": \"strands-decider\"}`. From a terminal: `anchor compare scaledown strands-decider`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/scaledown.json and https://www.anchorterminal.com/api/v1/tools/strands-decider.json\n\n## Other comparisons with ScaleDown or Strands Decider 2B\n\n- [Celeris-1 Decision vs ScaleDown](https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.md)\n- [Celeris-1 Decision vs Strands Decider 2B](https://www.anchorterminal.com/compare/celeris-1-decision-vs-strands-decider.md)\n- [Clef vs ScaleDown](https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown.md)\n- [Clef vs Strands Decider 2B](https://www.anchorterminal.com/compare/cloudflare-clef-vs-strands-decider.md)\n- [Laya vs ScaleDown](https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.md)\n- [Laya vs Strands Decider 2B](https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider.md)\n- [Decider vs ScaleDown](https://www.anchorterminal.com/compare/decider-vs-scaledown.md)\n- [Decider vs Strands Decider 2B](https://www.anchorterminal.com/compare/decider-vs-strands-decider.md)\n- [Decision 3.0 vs ScaleDown](https://www.anchorterminal.com/compare/decision-3-vs-scaledown.md)\n- [Decision 3.0 vs Strands Decider 2B](https://www.anchorterminal.com/compare/decision-3-vs-strands-decider.md)\n- [GLiClass vs ScaleDown](https://www.anchorterminal.com/compare/gliclass-vs-scaledown.md)\n- [GLiClass vs Strands Decider 2B](https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md)\n- [Kev vs ScaleDown](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown.md)\n- [Kev vs Strands Decider 2B](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider.md)\n- [Liquid d1 vs ScaleDown](https://www.anchorterminal.com/compare/liquid-d1-vs-scaledown.md)\n- [Liquid d1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.md)\n- [Microsoft-Decision-1 vs ScaleDown](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-scaledown.md)\n- [Microsoft-Decision-1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-strands-decider.md)\n- [Drex 1.5 vs ScaleDown](https://www.anchorterminal.com/compare/nace-drex-vs-scaledown.md)\n- [Drex 1.5 vs Strands Decider 2B](https://www.anchorterminal.com/compare/nace-drex-vs-strands-decider.md)\n- [OpenAI Decisions API vs ScaleDown](https://www.anchorterminal.com/compare/openai-decisions-api-vs-scaledown.md)\n- [OpenAI Decisions API vs Strands Decider 2B](https://www.anchorterminal.com/compare/openai-decisions-api-vs-strands-decider.md)\n- [pplx-decider vs ScaleDown](https://www.anchorterminal.com/compare/pplx-decider-vs-scaledown.md)\n- [pplx-decider vs Strands Decider 2B](https://www.anchorterminal.com/compare/pplx-decider-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- [Strands Decider 2B vs Jev](https://www.anchorterminal.com/compare/strands-decider-vs-typesafe-jev.md)\n- [Strands Decider 2B vs Vela 2.0](https://www.anchorterminal.com/compare/strands-decider-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": "ScaleDown vs Strands Decider 2B", "url": "" } ], "description": "Strands Decider 2B scores 61.3 (C) to ScaleDown's 48.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "ScaleDown D 48.6", "Strands Decider 2B C 61.3", "scores" ], "h1": "ScaleDown vs Strands Decider 2B", "image": "https://www.anchorterminal.com/assets/og/compare-scaledown-vs-strands-decider.png", "path": "/compare/scaledown-vs-strands-decider", "published": "2026-10-01", "section": "tools", "title": "ScaleDown vs Strands Decider 2B for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-14", "url": "https://www.anchorterminal.com/compare/scaledown-vs-strands-decider" }, "tokens": { "markdown": 2800, "slim": 780 }, "version": 1 }