{ "data": { "a": { "slug": "celeris-1-decision", "name": "Celeris-1 Decision", "vendor": "Celeris (Marqo Inc)", "vendorUrl": "https://celeris.ai", "kind": "model", "category": "decision-models", "summary": "Celeris-1 Decision is a hosted multimodal diffusion model from Celeris, a Marqo Inc research lab. Its HTTP API answers typed questions about text, structured data and images with probabilities and optional explanations.", "url": "https://www.anchorterminal.com/tools/celeris-1-decision", "markdownUrl": "https://www.anchorterminal.com/tools/celeris-1-decision.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/celeris-1-decision.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/celeris-1-decision.json", "license": "Proprietary hosted model under Celeris terms of service", "transports": [ "http" ], "remoteUrl": "https://inference.celeris.ai/celeris-1-decision/v1/systemone", "packages": [], "auth": "api-key", "authNotes": "Bearer ck_ API key from an activated workspace. Signup, a payment method and prepaid credit are required. Keys can be created, rotated and revoked in the console; changes can take one minute. Keys share workspace credit, with no per-key limits. Activation is usually immediate but can queue when capacity is full.", "pricing": "usage", "pricingNotes": "$0.04 per million input tokens, including cached input, with output tokens free. Images and request overhead count as input. Card-funded purchases start at $5 and credit expires after 30 days. No card-free tier was found. Requests still processing after disconnection may be charged. Executed order forms can override self-service terms.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 interface found in the reviewed pricing and API documentation on 9 October 2026. HTTP 402 means exhausted prepaid credit.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://docs.celeris.ai/decisions", "llmsTxt": "https://docs.celeris.ai/llms.txt", "capabilities": [ "inference.decision" ], "tags": [ "hosted", "model", "closed-source", "multimodal", "llms-txt", "usage-priced", "status-page", "beta" ], "lastRelease": "2026-10-08", "graded": true, "anchor": { "graded": true, "score": 49.3, "grade": "D", "agentReady": false, "rank": 708, "ranked": true, "rankOf": 842, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 40, "payments": 20, "reliability": 40, "schema": 60, "security": 45, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-09" }, "negative": 0, "verdict": "Typed probabilities, image inputs and optional explanations suit routing and classification inside an agent. Both System One and OpenAI Decisions request formats are documented. The service remains early access under its terms, activation can queue, and prepaid credit expires after 30 days. The published accuracy and latency figures are Celeris measurements, not Anchor Terminal tests.", "bestFor": "Multimodal classification, routing and bounded decisions with probabilities and optional explanations", "strengths": [ "Typed binary, choice and score answers, with multiple questions about one state per request", "Text, JSON and images through System One, with optional explanations", "Decision inputs cost $0.04 per million tokens and outputs are free", "Markdown documentation, error recovery guidance and revocable workspace keys" ], "weaknesses": [ "Early-access terms and capacity-dependent workspace activation", "Prepaid credit expires after 30 days, with a $5 minimum card purchase", "No numeric request-rate guarantee or decision-model context window found", "No explicit API training exclusion or prompt-retention period found in the reviewed policies", "Closed weights and no fine-tuning in this release" ], "agentNotes": [ "Use the decision model path and matching model field. This model has no chat, Responses or models endpoint.", "Use at most 512 questions per request, or 64 with explanations. Both endpoints reject streaming.", "Preserve x_celeris in a custom Jev SDK response model when reading explanations. The default response type drops it.", "Retry 429 with Retry-After and backoff. Stop on 402 until workspace credit is replenished.", "Read usage.input_tokens for cost, including images and request overhead. Track credit expiry separately from token consumption." ], "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": 49.3 } ], "editorialScores": { "ergonomics": 80, "maintenance": 40, "payments": 20, "reliability": 40, "schema": 60, "security": 45, "transparency": 45 }, "provenanceScore": 61 }, "connect": { "http": "curl https://inference.celeris.ai/celeris-1-decision/v1/systemone \\\n -H \"Authorization: Bearer $CELERIS_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"celeris-1-decision\",\"state\":\"The customer requests a refund for a duplicate charge.\",\"questions\":{\"refund\":{\"type\":\"noul\",\"instructions\":\"The customer is asking for a refund.\"}},\"x_celeris\":{\"explain\":true}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/celeris-1-decision" }, "area": "models", "unitPrices": [ { "item": "Decision input", "unit": "1m-tokens", "usd": 0.04, "note": "Includes cached input and image tokens. Prepaid credit expires after 30 days." }, { "item": "Decision output", "unit": "1m-tokens", "usd": 0, "note": "Includes optional explanations." } ], "provenance": { "legalEntity": "Marqo Inc", "domain": "celeris.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://celeris.ai/terms", "privacy": "https://celeris.ai/privacy", "statusPage": "https://status.celeris.ai/", "changelog": "", "securityTxt": "none", "checked": "2026-10-09", "notes": [ "Terms dated 8 September 2026 and privacy notice dated 23 July 2026 name Marqo Inc operating as Celeris.", "No dedicated decision-model changelog or dated deprecation policy found.", "The public status history records an 11-hour-20-minute outage for the separate Celeris-1 model on 19 August 2026. It predates this decision model and is not counted as its outage.", "The standard well-known security.txt and docs OpenAPI JSON addresses returned HTTP 404. Domain registration was not checked." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/celeris-1-decision.json", "live": { "slug": "celeris-1-decision", "probe": { "target": "https://inference.celeris.ai/celeris-1-decision/v1/systemone", "method": "get", "lastAt": "2026-10-09T11:46:25.06310241Z", "lastOk": true, "lastStatus": 404, "lastMs": 341, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 343, "p95ms24h": 515, "samples24h": 24, "samples30d": 24, "days": [ { "date": "2026-10-09", "probes": 24, "ok": 24 } ] }, "vendorStatus": { "page": "https://status.celeris.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-09T09:35:43.34493788Z" }, "updatedAt": "2026-10-09T11:46:25.06310241Z" } }, "answer": "Laya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories.", "b": { "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": 183, "ranked": true, "rankOf": 842, "categoryRank": 3, "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": "v0.4.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:59.058927231Z" }, { "registry": "pypi", "name": "laya", "version": "0.4.0", "released": "2026-10-07", "seenAt": "2026-10-08T16:06:58.941467636Z" } ], "githubStars": 31667, "pypiWeekly": 77372, "securityTxt": { "url": "https://convaiinnovations.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:09.474636066Z" }, "domain": { "domain": "convaiinnovations.com", "registered": "2021-09-01", "source": "https://rdap.verisign.com/com/v1/domain/convaiinnovations.com", "checkedAt": "2026-10-04T13:08:27.291006176Z" }, "updatedAt": "2026-10-08T16:06:59.058927231Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Celeris (Marqo Inc)", "b": "Convai Innovations", "name": "Vendor" }, { "a": "https://inference.celeris.ai/celeris-1-decision/v1/systemone", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP, stdio", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "free", "b": "free", "name": "Price for inference decision" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary hosted model under Celeris terms of service", "b": "Apache-2.0", "name": "Licence" }, { "a": "none", "b": "8", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-10-08", "b": "2026-10-01", "name": "Last release" }, { "a": "2026-09-08", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-07-23", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "29k stars", "name": "Popularity" }, { "a": "none", "b": "2.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Laya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Celeris-1 Decision or Laya?" }, { "answer": "Celeris-1 Decision, at free against free for Laya. These are the vendors' published prices for the job.", "question": "Which is cheaper for inference decision, Celeris-1 Decision or Laya?" }, { "answer": "Celeris-1 Decision needs an API key. Laya needs no key.", "question": "Do Celeris-1 Decision and Laya need an API key?" }, { "answer": "Celeris-1 Decision has a hosted endpoint at https://inference.celeris.ai/celeris-1-decision/v1/systemone. Laya runs on your own machine, with no hosted endpoint listed.", "question": "Can an agent call Celeris-1 Decision and Laya without installing anything?" }, { "answer": "No open-source release is listed for Celeris-1 Decision. Laya is open source (Apache-2.0).", "question": "Are Celeris-1 Decision and Laya open source?" } ], "goodFor": [ { "aheadOn": null, "also": [ "Cheaper for inference decision, $0 against $0 per 1M tokens", "A hosted endpoint, with nothing to install" ], "goodFor": "Multimodal classification, routing and bounded decisions with probabilities and optional explanations", "slug": "celeris-1-decision", "watchFor": "Early-access terms and capacity-dependent workspace activation" }, { "aheadOn": [ "Reliability, 65 against 40", "Schema \u0026 documentation, 80 against 60", "Security \u0026 auth, 57 against 45", "Payments \u0026 pricing, 60 against 20", "Maintenance \u0026 community, 83 against 40", "Transparency \u0026 trust, 62 against 53" ], "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" } ], "job": { "capability": "inference.decision", "name": "Inference decision" }, "others": [ { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-cloudflare-clef.json", "title": "Celeris-1 Decision vs Clef", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-cloudflare-clef" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider.json", "title": "Celeris-1 Decision vs Decider", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.json", "title": "Celeris-1 Decision vs GLiClass", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-jaredpalmer-kev.json", "title": "Celeris-1 Decision vs Kev", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-jaredpalmer-kev" }, { "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-openai-decisions-api.json", "title": "Celeris-1 Decision vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-openai-decisions-api" }, { "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/celeris-1-decision-vs-typesafe-jev.json", "title": "Celeris-1 Decision vs Jev", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-vela.json", "title": "Celeris-1 Decision vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-vela" }, { "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/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-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-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-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" } ], "scores": [ { "by": 25, "celeris-1-decision": 40, "convai-laya": 65, "edge": "convai-laya", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 20, "celeris-1-decision": 60, "convai-laya": 80, "edge": "convai-laya", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 0, "celeris-1-decision": 80, "convai-laya": 80, "edge": "", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 12, "celeris-1-decision": 45, "convai-laya": 57, "edge": "convai-laya", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 40, "celeris-1-decision": 20, "convai-laya": 60, "edge": "convai-laya", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 43, "celeris-1-decision": 40, "convai-laya": 83, "edge": "convai-laya", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 9, "celeris-1-decision": 53, "convai-laya": 62, "edge": "convai-laya", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Laya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories. Both do inference decision. Celeris-1 Decision is cheaper for inference decision, $0 against $0 per 1M tokens.", "verdicts": { "celeris-1-decision": "Typed probabilities, image inputs and optional explanations suit routing and classification inside an agent. Both System One and OpenAI Decisions request formats are documented. The service remains early access under its terms, activation can queue, and prepaid credit expires after 30 days. The published accuracy and latency figures are Celeris measurements, not Anchor Terminal tests.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya", "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.md", "slim": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.min.md" }, "markdown": "Laya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories. Both do inference decision. Celeris-1 Decision is cheaper for inference decision, $0 against $0 per 1M tokens.\n\n- Celeris-1 Decision: grade D, 49.3/100, rank #708 of 842. Markdown https://www.anchorterminal.com/tools/celeris-1-decision.md · JSON https://www.anchorterminal.com/api/v1/tools/celeris-1-decision.json\n- Laya: grade B, 69.2/100, rank #183 of 842. Markdown https://www.anchorterminal.com/tools/convai-laya.md · JSON https://www.anchorterminal.com/api/v1/tools/convai-laya.json\n\n## Which one, for what\n\n### Celeris-1 Decision (D)\n\nGood for: Multimodal classification, routing and bounded decisions with probabilities and optional explanations\n\nAlso in its favour:\n- Cheaper for inference decision, $0 against $0 per 1M tokens\n- A hosted endpoint, with nothing to install\n\nWatch for: Early-access terms and capacity-dependent workspace activation\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 40\n- Schema \u0026 documentation, 80 against 60\n- Security \u0026 auth, 57 against 45\n- Payments \u0026 pricing, 60 against 20\n- Maintenance \u0026 community, 83 against 40\n- Transparency \u0026 trust, 62 against 53\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\n## Score by category\n\n| Category | Weight | Celeris-1 Decision | Laya | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 65 | Laya +25 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 80 | Laya +20 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 80 | even |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 57 | Laya +12 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 60 | Laya +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 40 | 83 | Laya +43 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 62 | Laya +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **49.3 · D** | **69.2 · B** | |\n\n## Facts side by side\n\n| Fact | Celeris-1 Decision | Laya |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Celeris (Marqo Inc) | Convai Innovations |\n| Hosted endpoint | `https://inference.celeris.ai/celeris-1-decision/v1/systemone` | no (local only) |\n| Transports | HTTP | HTTP, stdio |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| Price for inference decision | free | free |\n| x402 | no | no |\n| Licence | Proprietary hosted model under Celeris terms of service | Apache-2.0 |\n| Tools exposed | none | 8 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-08 | 2026-10-01 |\n| Terms last updated | 2026-09-08 | no document linked |\n| Privacy policy last updated | 2026-07-23 | no document linked |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | none | 29k stars |\n| Agent reviews | none | 2.5/5 (2) |\n\n## Verdicts\n\n**Celeris-1 Decision.** Typed probabilities, image inputs and optional explanations suit routing and classification inside an agent. Both System One and OpenAI Decisions request formats are documented. The service remains early access under its terms, activation can queue, and prepaid credit expires after 30 days. The published accuracy and latency figures are Celeris measurements, not Anchor Terminal tests.\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## Before you call either\n\n### Celeris-1 Decision\n\n1. Use the decision model path and matching model field. This model has no chat, Responses or models endpoint.\n2. Use at most 512 questions per request, or 64 with explanations. Both endpoints reject streaming.\n3. Preserve x_celeris in a custom Jev SDK response model when reading explanations. The default response type drops it.\n4. Retry 429 with Retry-After and backoff. Stop on 402 until workspace credit is replenished.\n5. Read usage.input_tokens for cost, including images and request overhead. Track credit expiry separately from token consumption.\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## Questions\n\n### Which is better for AI agents, Celeris-1 Decision or Laya?\n\nLaya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories.\n\n### Which is cheaper for inference decision, Celeris-1 Decision or Laya?\n\nCeleris-1 Decision, at free against free for Laya. These are the vendors' published prices for the job.\n\n### Do Celeris-1 Decision and Laya need an API key?\n\nCeleris-1 Decision needs an API key. Laya needs no key.\n\n### Can an agent call Celeris-1 Decision and Laya without installing anything?\n\nCeleris-1 Decision has a hosted endpoint at https://inference.celeris.ai/celeris-1-decision/v1/systemone. Laya runs on your own machine, with no hosted endpoint listed.\n\n### Are Celeris-1 Decision and Laya open source?\n\nNo open-source release is listed for Celeris-1 Decision. Laya is open source (Apache-2.0).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.json, and with the fewest tokens: https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"celeris-1-decision\", \"b\": \"convai-laya\"}`. From a terminal: `anchor compare celeris-1-decision convai-laya`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/celeris-1-decision.json and https://www.anchorterminal.com/api/v1/tools/convai-laya.json\n\n## Other comparisons with Celeris-1 Decision or Laya\n\n- [Celeris-1 Decision vs Clef](https://www.anchorterminal.com/compare/celeris-1-decision-vs-cloudflare-clef.md)\n- [Celeris-1 Decision vs Decider](https://www.anchorterminal.com/compare/celeris-1-decision-vs-decider.md)\n- [Celeris-1 Decision vs GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Celeris-1 Decision vs Kev](https://www.anchorterminal.com/compare/celeris-1-decision-vs-jaredpalmer-kev.md)\n- [Celeris-1 Decision vs Liquid d1](https://www.anchorterminal.com/compare/celeris-1-decision-vs-liquid-d1.md)\n- [Celeris-1 Decision vs OpenAI Decisions API](https://www.anchorterminal.com/compare/celeris-1-decision-vs-openai-decisions-api.md)\n- [Celeris-1 Decision vs Strands Decider 2B](https://www.anchorterminal.com/compare/celeris-1-decision-vs-strands-decider.md)\n- [Celeris-1 Decision vs Jev](https://www.anchorterminal.com/compare/celeris-1-decision-vs-typesafe-jev.md)\n- [Celeris-1 Decision vs Vela 2.0](https://www.anchorterminal.com/compare/celeris-1-decision-vs-vela.md)\n- [Clef vs Laya](https://www.anchorterminal.com/compare/cloudflare-clef-vs-convai-laya.md)\n- [Laya vs Decider](https://www.anchorterminal.com/compare/convai-laya-vs-decider.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 OpenAI Decisions API](https://www.anchorterminal.com/compare/convai-laya-vs-openai-decisions-api.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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "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" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Celeris-1 Decision vs Laya", "url": "" } ], "description": "Laya scores 69.2 (B) on agent readiness against Celeris-1 Decision's 49.3 (D), and leads in 6 of 7 scored categories. Both do inference decision. Celeris-1 Decision is cheaper for inference decision, $0 against $0 per 1M tokens. Category scores, facts, verdicts and agent notes…", "facts": [ "Celeris-1 Decision D 49.3", "Laya B 69.2", "scores" ], "h1": "Celeris-1 Decision vs Laya", "image": "https://www.anchorterminal.com/assets/og/compare-celeris-1-decision-vs-convai-laya.png", "path": "/compare/celeris-1-decision-vs-convai-laya", "published": "2026-10-01", "section": "tools", "title": "Celeris-1 Decision vs Laya for AI agents, D 49.3 vs B 69.2", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-convai-laya" }, "tokens": { "markdown": 2400, "slim": 730 }, "version": 1 }