{ "data": { "a": { "slug": "convai-laya", "name": "Laya", "vendor": "Convai Innovations", "vendorUrl": "https://convaiinnovations.com", "kind": "model", "category": "decision-models", "summary": "Open-source decision engine from Convai Innovations, released under Apache-2.0.", "url": "https://www.anchorterminal.com/tools/convai-laya", "markdownUrl": "https://www.anchorterminal.com/tools/convai-laya.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/convai-laya.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/convai-laya.json", "repo": "https://github.com/NandhaKishorM/laya", "license": "Apache-2.0", "transports": [ "http", "stdio" ], "packages": [ { "registry": "pypi", "name": "laya" } ], "auth": "none", "authNotes": "No account. `laya-serve` binds to 0.0.0.0:8000 and is open unless `LAYA_API_KEY` is set, after which it requires `Authorization: Bearer \u003ckey\u003e` on every route but liveness. The MCP server runs over stdio. Weights download from Hugging Face without an account.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy. You pay for the hardware, and the README's speed figures were measured on a Tesla T4. The maintainer takes donations through Buy Me A Coffee (https://github.com/NandhaKishorM/laya).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Laya is software you run, and its server has no payment route (checked 2026-10-02).", "endpoints": [] }, "toolCount": 8, "popularity": { "githubStars": 29200, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-02" }, "docsUrl": "https://nandhakishorm.github.io/laya/", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "mcp", "batch", "pre-1.0" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 69.2, "grade": "B", "agentReady": false, "rank": 202, "ranked": true, "rankOf": 1094, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 83, "payments": 60, "reliability": 65, "schema": 80, "security": 57, "transparency": 62 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.", "bestFor": "Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.", "strengths": [ "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI", "421M and 322M-parameter encoders that the README times at 32.8 to 39.5 ms for one question on a Tesla T4", "A Jev-compatible HTTP server with a batch route for up to 64 states, an 8-tool MCP server, and LangChain, LlamaIndex and CrewAI wrappers", "A multilingual checkpoint for 100+ languages and a router that picks the checkpoint per request", "An honest-limits section in the README, SECURITY.md with private reporting, and CI that runs gitleaks, pip-audit and CodeQL" ], "weaknesses": [ "Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning", "Choice options share a 192 or 256-token budget, and the README reports 0.425 on Banking77's 77 labels", "512 tokens of context on the English checkpoint and 1,024 by default on the others", "`laya-serve` listens on 0.0.0.0 with no key unless `LAYA_API_KEY` is set", "26 releases in 13 days, still 0.x and marked beta, with 61 open issues and 81 open pull requests" ], "agentNotes": [ "Set `LAYA_API_KEY` before starting `laya-serve`. It listens on every interface by default", "Gate on `answer_confidence`, not `confidence`, which measures entropy and doesn't match Jev's field", "Shortlist choice questions with more than about 20 options using `predict_shortlist` or the `laya_shortlist` tool", "Use semantic or opaque labels such as `A` and `B`, not `yes` and `no`, in choice questions. The checkpoints can follow the label text", "Pass `model=\"multilingual\"` and `max_len=8192` for long documents. The English checkpoint stops at 512 tokens" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 69.2 } ], "editorialScores": { "ergonomics": 80, "maintenance": 83, "payments": 60, "reliability": 65, "schema": 80, "security": 57, "transparency": 70 }, "provenanceScore": 53 }, "connect": { "install": "pip install \"laya[serve]\"\nLAYA_API_KEY=change-me LAYA_DEVICE=cuda LAYA_PRELOAD=1 laya-serve # 0.0.0.0:8000", "http": "curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -H \"Authorization: Bearer $LAYA_API_KEY\" \\\n -d '{\"state\":\"Checkout has failed for every customer for an hour.\",\"questions\":{\"urgent\":{\"type\":\"noul\",\"instructions\":\"Is this request urgent?\"},\"team\":{\"type\":\"choice\",\"criteria\":{\"billing\":\"Payments and refunds\",\"technical\":\"Outages and errors\"}}}}'", "claudeCode": "pip install \"laya[mcp]\" \u0026\u0026 claude mcp add laya -- laya-mcp-server", "config": { "mcpServers": { "laya": { "command": "laya-mcp-server", "env": { "LAYA_DEVICE": "cpu" } } } } }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/convai-laya" }, "area": "models", "provenance": { "legalEntity": "Convai Innovations Pvt. Ltd.", "domain": "convaiinnovations.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/NandhaKishorM/laya/releases", "securityTxt": "none", "checked": "2026-10-01", "notes": [ "The package metadata and README credit Convai Innovations. The company's site names Convai Innovations Pvt. Ltd. at the Kerala Startup Mission office in Kasaragod, Kerala, India, but doesn't mention Laya.", "convaiinnovations.com/.well-known/security.txt returns 404. SECURITY.md in the repository takes private reports through GitHub Security Advisories.", "We didn't read the domain's registration date.", "Software you run, so there's no hosted endpoint. The Apache-2.0 licence stands in for terms." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/convai-laya.json", "live": { "slug": "convai-laya", "versions": [ { "registry": "github", "name": "NandhaKishorM/laya", "version": "v0.4.7", "released": "2026-10-14", "seenAt": "2026-10-14T23:46:28.704482505Z" }, { "registry": "pypi", "name": "laya", "version": "0.4.7", "released": "2026-10-14", "seenAt": "2026-10-14T23:46:28.583585119Z" } ], "githubStars": 32909, "pypiWeekly": 69748, "securityTxt": { "url": "https://convaiinnovations.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-14T15:47:37.965882998Z" }, "domain": { "domain": "convaiinnovations.com", "registered": "2021-09-01", "source": "https://rdap.verisign.com/com/v1/domain/convaiinnovations.com", "checkedAt": "2026-10-11T13:22:10.228622332Z" }, "updatedAt": "2026-10-14T23:46:28.704482505Z" } }, "answer": "Laya scores 69.2 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in every scored category.", "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": 880, "ranked": true, "rankOf": 1094, "categoryRank": 15, "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": "2026-10-15T14:03:43.707330463Z", "lastOk": true, "lastStatus": 403, "lastMs": 252, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 263, "p95ms24h": 308, "samples24h": 197, "samples30d": 197, "days": [ { "date": "2026-10-14", "probes": 47, "ok": 47 }, { "date": "2026-10-15", "probes": 150, "ok": 150 } ] }, "versions": [ { "registry": "pypi", "name": "langchain-scaledown", "version": "0.2.0", "released": "2026-10-12", "seenAt": "2026-10-15T00:19:27.075016002Z" } ], "githubStars": 0, "pypiWeekly": 229, "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": "2026-10-15T14:03:43.707330463Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Convai Innovations", "b": "Carbon ScaleDown, Inc.", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.scaledown.xyz", "name": "Hosted endpoint" }, { "a": "HTTP, stdio", "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", "b": "Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT", "name": "Licence" }, { "a": "8", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-01", "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": "29k stars", "b": "0 stars", "name": "Popularity" }, { "a": "2.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Laya scores 69.2 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in every scored category.", "question": "Which is better for AI agents, Laya or ScaleDown?" }, { "answer": "Laya needs no key. ScaleDown needs an API key.", "question": "Do Laya and ScaleDown need an API key?" }, { "answer": "Laya runs on your own machine, with no hosted endpoint listed. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.", "question": "Can an agent call Laya and ScaleDown without installing anything?" }, { "answer": "Laya is open source (Apache-2.0). No open-source release is listed for ScaleDown.", "question": "Are Laya and ScaleDown open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 65 against 20", "Schema \u0026 documentation, 80 against 69", "Security \u0026 auth, 57 against 48", "Payments \u0026 pricing, 60 against 40", "Maintenance \u0026 community, 83 against 56", "Transparency \u0026 trust, 62 against 54" ], "also": [ "No key needed to call it", "Runs on your own machine", "Open source" ], "goodFor": "Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.", "slug": "convai-laya", "watchFor": "Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning" }, { "aheadOn": null, "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-convai-laya.json", "title": "Celeris-1 Decision vs Laya", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya" }, { "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-convai-laya.json", "title": "Clef vs Laya", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya" }, { "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-decider.json", "title": "Laya vs Decider", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-decider" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-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-gliclass.json", "title": "Laya vs GLiClass", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev.json", "title": "Laya vs Kev", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev" }, { "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-microsoft-decision-1.json", "title": "Laya vs Microsoft-Decision-1", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-microsoft-decision-1" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-nace-drex.json", "title": "Laya vs Drex 1.5", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-nace-drex" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api.json", "title": "Laya vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-pplx-decider.json", "title": "Laya vs pplx-decider", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-pplx-decider" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-sprag-spev.json", "title": "Laya vs SPEV", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-sprag-spev" }, { "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/convai-laya-vs-typesafe-jev.json", "title": "Laya vs Jev", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/convai-laya-vs-vela.json", "title": "Laya vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-vela" }, { "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-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-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-sprag-spev.json", "title": "ScaleDown vs SPEV", "url": "https://www.anchorterminal.com/compare/scaledown-vs-sprag-spev" }, { "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": 45, "convai-laya": 65, "edge": "convai-laya", "key": "reliability", "name": "Reliability", "scaledown": 20, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 11, "convai-laya": 80, "edge": "convai-laya", "key": "schema", "name": "Schema \u0026 documentation", "scaledown": 69, "weight": 13 }, { "by": 4, "convai-laya": 80, "edge": "convai-laya", "key": "ergonomics", "name": "Agent ergonomics", "scaledown": 76, "weight": 13 }, { "by": 9, "convai-laya": 57, "edge": "convai-laya", "key": "security", "name": "Security \u0026 auth", "scaledown": 48, "weight": 14 }, { "by": 20, "convai-laya": 60, "edge": "convai-laya", "key": "payments", "name": "Payments \u0026 pricing", "scaledown": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 27, "convai-laya": 83, "edge": "convai-laya", "key": "maintenance", "name": "Maintenance \u0026 community", "scaledown": 56, "weight": 7 }, { "by": 8, "convai-laya": 62, "edge": "convai-laya", "key": "transparency", "name": "Transparency \u0026 trust", "scaledown": 54, "weight": 7 } ], "summary": "Laya scores 69.2 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in every scored category. Both do decision models.", "verdicts": { "convai-laya": "Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.", "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/convai-laya-vs-scaledown", "json": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.md", "slim": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.min.md" }, "markdown": "Laya scores 69.2 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in every scored category. Both do decision models.\n\n- Laya: grade B, 69.2/100, rank #202 of 1094. Markdown https://www.anchorterminal.com/tools/convai-laya.md · JSON https://www.anchorterminal.com/api/v1/tools/convai-laya.json\n- ScaleDown: grade D, 48.6/100, rank #880 of 1094. 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 136 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 125 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md\n\n## Which one, for what\n\n### Laya (B)\n\nGood for: Fast, cheap classification and routing on short text in many languages, as a base to fine-tune on your own labels, and as an MCP or LangGraph routing step.\n\nAhead on:\n- Reliability, 65 against 20\n- Schema \u0026 documentation, 80 against 69\n- Security \u0026 auth, 57 against 48\n- Payments \u0026 pricing, 60 against 40\n- Maintenance \u0026 community, 83 against 56\n- Transparency \u0026 trust, 62 against 54\n\nAlso in its favour:\n- No key needed to call it\n- Runs on your own machine\n- Open source\n\nWatch for: Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning\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\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 | Laya | ScaleDown | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 65 | 20 | Laya +45 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 69 | Laya +11 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 76 | Laya +4 |\n| Security \u0026 auth | 14% (17.5 this run) | 57 | 48 | Laya +9 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 40 | Laya +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 56 | Laya +27 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 62 | 54 | Laya +8 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **69.2 · B** | **48.6 · D** | |\n\n## Facts side by side\n\n| Fact | Laya | ScaleDown |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Convai Innovations | Carbon ScaleDown, Inc. |\n| Hosted endpoint | no (local only) | `https://api.scaledown.xyz` |\n| Transports | HTTP, stdio | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 | Proprietary hosted models under ScaleDown's Terms of Service. The langchain-scaledown integration is MIT |\n| Tools exposed | 8 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-01 | 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 | 29k stars | 0 stars |\n| Agent reviews | 2.5/5 (2) | none |\n\n## Verdicts\n\n**Laya.** Apache-2.0 code and weights, installed with `pip install laya`, with Python 3.10 to 3.13 tested in CI. Base checkpoints score 0.362 and 0.352 on the maintainers' typed-decisions benchmark against a 0.318 random baseline, so it needs fine-tuning.\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### Laya\n\n1. Set `LAYA_API_KEY` before starting `laya-serve`. It listens on every interface by default\n2. Gate on `answer_confidence`, not `confidence`, which measures entropy and doesn't match Jev's field\n3. Shortlist choice questions with more than about 20 options using `predict_shortlist` or the `laya_shortlist` tool\n4. Use semantic or opaque labels such as `A` and `B`, not `yes` and `no`, in choice questions. The checkpoints can follow the label text\n5. Pass `model=\"multilingual\"` and `max_len=8192` for long documents. The English checkpoint stops at 512 tokens\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, Laya or ScaleDown?\n\nLaya scores 69.2 (B) on agent readiness against ScaleDown's 48.6 (D), and leads in every scored category.\n\n### Do Laya and ScaleDown need an API key?\n\nLaya needs no key. ScaleDown needs an API key.\n\n### Can an agent call Laya and ScaleDown without installing anything?\n\nLaya runs on your own machine, with no hosted endpoint listed. ScaleDown has a hosted endpoint at https://api.scaledown.xyz.\n\n### Are Laya and ScaleDown open source?\n\nLaya is open source (Apache-2.0). No open-source release is listed for ScaleDown.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.json, and with the fewest tokens: https://www.anchorterminal.com/compare/convai-laya-vs-scaledown.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"convai-laya\", \"b\": \"scaledown\"}`. From a terminal: `anchor compare convai-laya scaledown`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/convai-laya.json and https://www.anchorterminal.com/api/v1/tools/scaledown.json\n\n## Other comparisons with Laya or ScaleDown\n\n- [Celeris-1 Decision vs Laya](https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.md)\n- [Celeris-1 Decision vs ScaleDown](https://www.anchorterminal.com/compare/celeris-1-decision-vs-scaledown.md)\n- [Clef vs Laya](https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya.md)\n- [Clef vs ScaleDown](https://www.anchorterminal.com/compare/cloudflare-clef-vs-scaledown.md)\n- [Laya vs Decider](https://www.anchorterminal.com/compare/convai-laya-vs-decider.md)\n- [Laya vs Decision 3.0](https://www.anchorterminal.com/compare/convai-laya-vs-decision-3.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Laya vs Kev](https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev.md)\n- [Laya vs Liquid d1](https://www.anchorterminal.com/compare/convai-laya-vs-liquid-d1.md)\n- [Laya vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/convai-laya-vs-microsoft-decision-1.md)\n- [Laya vs Drex 1.5](https://www.anchorterminal.com/compare/convai-laya-vs-nace-drex.md)\n- [Laya vs OpenAI Decisions API](https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api.md)\n- [Laya vs pplx-decider](https://www.anchorterminal.com/compare/convai-laya-vs-pplx-decider.md)\n- [Laya vs SPEV](https://www.anchorterminal.com/compare/convai-laya-vs-sprag-spev.md)\n- [Laya vs Strands Decider 2B](https://www.anchorterminal.com/compare/convai-laya-vs-strands-decider.md)\n- [Laya vs Jev](https://www.anchorterminal.com/compare/convai-laya-vs-typesafe-jev.md)\n- [Laya vs Vela 2.0](https://www.anchorterminal.com/compare/convai-laya-vs-vela.md)\n- [Decider vs ScaleDown](https://www.anchorterminal.com/compare/decider-vs-scaledown.md)\n- [Decision 3.0 vs ScaleDown](https://www.anchorterminal.com/compare/decision-3-vs-scaledown.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 SPEV](https://www.anchorterminal.com/compare/scaledown-vs-sprag-spev.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": "2026-10-15", "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": "Laya vs ScaleDown", "url": "" } ], "description": "Laya scores 69.2 (B) to ScaleDown's 48.6 (D) for decision models. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Laya B 69.2", "ScaleDown D 48.6", "scores" ], "h1": "Laya vs ScaleDown", "image": "https://www.anchorterminal.com/assets/og/compare-convai-laya-vs-scaledown.png", "path": "/compare/convai-laya-vs-scaledown", "published": "2026-10-01", "section": "tools", "title": "Laya vs ScaleDown for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-scaledown" }, "tokens": { "markdown": 2600, "slim": 730 }, "version": 1 }