{
  "data": {
    "a": {
      "slug": "liquid-d1",
      "name": "Liquid d1",
      "vendor": "Liquid AI",
      "vendorUrl": "https://www.liquid.ai",
      "kind": "model",
      "category": "decision-models",
      "summary": "d1 is Liquid AI's decision model family. It answers typed yes or no, choice and score questions about text and images with probabilities, through a hosted API and the open-weight d1-3B and d1-omni-600M models.",
      "url": "https://www.anchorterminal.com/tools/liquid-d1",
      "markdownUrl": "https://www.anchorterminal.com/tools/liquid-d1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/liquid-d1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/liquid-d1.json",
      "repo": "https://huggingface.co/LiquidAI/d1-3B",
      "license": "The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.liquid.ai/decisions/v1/systemone",
      "packages": [],
      "auth": "api-key",
      "authNotes": "A key created at console.liquid.ai under Dashboard, API Keys, after registering and joining an organisation, sent as a Bearer header. Keys start with `liquid_`. No scopes, expiry or rotation were found in the reviewed documentation. The weights download from Hugging Face without an account.",
      "pricing": "freemium",
      "pricingNotes": "The hosted `d1` model costs $0.04 per million input tokens and bills no output tokens, per the launch post of 5 October 2026 (https://www.liquid.ai/blog/d1-decision-model). Each question is billed as its own prompt, and an image counts 1.5 tokens per 32 by 32 pixel patch. A text-only `d1:free` model exists, with no published limits. Liquid's pricing page covers model licensing only. The open weights are free to run, and commercial use is free below $10 million in annual revenue.",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the d1 docs, the launch posts or the terms of service (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.liquid.ai/lfm/models/decision-models",
      "llmsTxt": "https://docs.liquid.ai/llms.txt",
      "capabilities": [
        "inference.decision"
      ],
      "tags": [
        "hosted",
        "model",
        "open-weights",
        "source-available",
        "self-hosted",
        "llms-txt",
        "free-tier",
        "usage-priced"
      ],
      "lastRelease": "2026-10-07",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 43.5,
        "grade": "E",
        "agentReady": false,
        "rank": 886,
        "ranked": true,
        "rankOf": 954,
        "categoryRank": 13,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 63,
          "maintenance": 60,
          "payments": 27,
          "reliability": 21,
          "schema": 59,
          "security": 35,
          "transparency": 54
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.",
        "bestFor": "Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.",
        "strengths": [
          "$0.04 per million input tokens with no output tokens billed, per the launch post, and a text-only `d1:free` model",
          "d1-3B (3.12B parameters, 32,768-token context) and d1-omni-600M are ungated on Hugging Face, with GGUF builds",
          "llama.cpp's server README documents `/v1/systemone` for d1, so the same request runs locally",
          "The hosted `d1` model accepts up to 8 images a request as Base64 data",
          "Docs are served as Markdown with an llms.txt index, and say when to use a language model instead"
        ],
        "weaknesses": [
          "The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found",
          "No status page, rate limits, SLA or error reference found for the hosted API",
          "The LFM Open Licence v1.0 ends free commercial use at $10 million in annual revenue, so the weights aren't open source",
          "No OpenAPI file, API changelog or versioned model IDs. The hosted models are `d1` and `d1:free`",
          "No SDK of its own. TypeSafe's SDKs are the documented clients and don't send images"
        ],
        "agentNotes": [
          "POST to https://api.liquid.ai/decisions/v1/systemone with a `liquid_` key as a Bearer header. This is not a chat-completions endpoint",
          "Use `d1` for images. `d1:free` is text-only and answers that it does not accept images",
          "Send images as Base64 data URLs in `images`, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused",
          "Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again",
          "Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "E",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 43.5
          }
        ],
        "editorialScores": {
          "ergonomics": 63,
          "maintenance": 60,
          "payments": 27,
          "reliability": 21,
          "schema": 59,
          "security": 35,
          "transparency": 46
        },
        "provenanceScore": 62
      },
      "connect": {
        "install": "pip install typesafe-sdk   # or: npm install @typesafe-ai/sdk",
        "http": "curl -s https://api.liquid.ai/decisions/v1/systemone \\\n  -H \"Authorization: Bearer $LIQUID_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\":\"d1\",\"state\":\"I have been waiting over three weeks for my order and nobody has responded to my emails.\",\"questions\":{\"is_complaint\":{\"type\":\"noul\",\"instructions\":\"Is this message a complaint from the customer?\"}}}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/liquid-d1"
      },
      "area": "models",
      "unitPrices": [
        {
          "item": "d1 input",
          "unit": "1m-tokens",
          "usd": 0.04,
          "note": "No output tokens. Each question is billed as its own prompt, images at 1.5 tokens per 32 by 32 pixel patch"
        }
      ],
      "provenance": {
        "legalEntity": "Liquid AI, Inc.",
        "domain": "liquid.ai",
        "domainRegistered": "2017-12-16",
        "endpointOnVendorDomain": true,
        "terms": "https://www.liquid.ai/terms-conditions",
        "privacy": "https://www.liquid.ai/privacy-policy",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-08",
        "notes": [
          "The terms of service (updated 30 September 2026) name Liquid AI, Inc., a Delaware corporation, and Massachusetts law. The privacy policy carries the same date.",
          "RDAP gives liquid.ai a registration date of 16 December 2017 and a transfer on 20 April 2023. The site footer says the company was established in 2023.",
          "The hosted endpoint is on api.liquid.ai. The weights sit on huggingface.co under the LiquidAI organisation.",
          "/.well-known/security.txt returned 404 on www.liquid.ai, liquid.ai and api.liquid.ai.",
          "No status page is linked from the site, the docs or the launch posts, and status.liquid.ai did not answer our reader. No changelog for the API or the docs was found. docs.liquid.ai/changelog returned 404.",
          "A trust centre at trust.liquid.ai is hosted by Vanta and renders only with JavaScript, so its contents are unread."
        ],
        "score": 62
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/liquid-d1.json",
      "live": {
        "slug": "liquid-d1",
        "probe": {
          "target": "https://api.liquid.ai/decisions/v1/systemone",
          "method": "get",
          "lastAt": "2026-10-10T13:02:17.98852199Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 188,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 145,
          "p95ms24h": 385,
          "samples24h": 246,
          "samples30d": 477,
          "days": [
            {
              "date": "2026-10-08",
              "probes": 93,
              "ok": 93
            },
            {
              "date": "2026-10-09",
              "probes": 250,
              "ok": 250
            },
            {
              "date": "2026-10-10",
              "probes": 134,
              "ok": 134
            }
          ]
        },
        "securityTxt": {
          "url": "https://liquid.ai/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-09T15:40:17.371919029Z"
        },
        "llmsTxt": {
          "url": "https://docs.liquid.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-09T14:02:14.249419519Z"
        },
        "pages": [
          {
            "url": "https://www.liquid.ai/privacy-policy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-10-09T18:51:38.5062864Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "42d9e4ba3a39"
          },
          {
            "url": "https://www.liquid.ai/terms-conditions",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-10-09T18:51:40.723441493Z",
            "changedAt": "0001-01-01T00:00:00Z",
            "fingerprint": "8fd8f3573ffc"
          }
        ],
        "updatedAt": "2026-10-10T13:02:17.98852199Z"
      }
    },
    "answer": "Liquid d1 scores 43.5 (E) on agent readiness against Microsoft-Decision-1's 39 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability and security \u0026 auth.",
    "b": {
      "slug": "microsoft-decision-1",
      "name": "Microsoft-Decision-1",
      "vendor": "Microsoft",
      "vendorUrl": "https://www.microsoft.com",
      "kind": "model",
      "category": "decision-models",
      "summary": "Microsoft-Decision-1 is a Microsoft model, post-trained from Qwen3.5-9B, that scores fixed answer options with a calibrated probability for each. It is in public preview on Microsoft Foundry and OpenRouter, at $0.042 per million input tokens.",
      "url": "https://www.anchorterminal.com/tools/microsoft-decision-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/microsoft-decision-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/microsoft-decision-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/microsoft-decision-1.json",
      "license": "Not stated. The announcement and OpenRouter's model page name no licence, and no weights repository was found.",
      "transports": [
        "http"
      ],
      "packages": [],
      "auth": "mixed",
      "authNotes": "Foundry route: a Microsoft Entra bearer token for https://cognitiveservices.azure.com/.default, against a deployment in a Foundry resource, so an Azure subscription and deployment come first. OpenRouter route: an OpenRouter API key, read from OPENROUTER_API_KEY in OpenRouter's SDK example. Signup and key creation on OpenRouter were not checked.",
      "pricing": "usage",
      "pricingNotes": "$0.042 per million input tokens, with output free, on Foundry US and EU Datazone deployments and on OpenRouter (checked 10 October 2026). Microsoft says usage charges apply. No free tier or trial without a card was found.",
      "priceSummary": "Pay per use",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in Microsoft's announcement, the Tech Community post, the Foundry sample or OpenRouter's model page, checked 10 October 2026.",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-10"
      },
      "docsUrl": "https://commandline.microsoft.com/microsoft-decision-1-model-foundry/",
      "capabilities": [
        "inference.decision"
      ],
      "tags": [
        "hosted",
        "model",
        "usage-priced",
        "status-page",
        "beta"
      ],
      "lastRelease": "2026-10-09",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 39,
        "grade": "E",
        "agentReady": false,
        "rank": 921,
        "ranked": true,
        "rankOf": 954,
        "categoryRank": 14,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 50,
          "maintenance": 40,
          "payments": 20,
          "reliability": 30,
          "schema": 43,
          "security": 52,
          "transparency": 32
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-10"
        },
        "negative": 0,
        "verdict": "Microsoft publishes $0.042 per million input tokens, with output free, and the model is callable through Microsoft Foundry and OpenRouter. It is in public preview. No licence, model-specific retention statement, rate limit or deprecation policy was found, and the Foundry route needs an Azure deployment and an Entra token. Latency and accuracy figures are Microsoft's claims.",
        "bestFor": "Routing, classification, prioritisation and rubric checks over text, where a fixed set of options and a low price per token matter more than generated text.",
        "strengths": [
          "Price published without a login: $0.042 per million input tokens, with output free, on the US and EU Datazone deployments and on OpenRouter",
          "Callable today through two routes, Microsoft Foundry and OpenRouter, both linked from Microsoft's 9 October 2026 announcement",
          "Each answer is a probability for one fixed option, which Microsoft describes as calibrated, so an application can send uncertain results to review",
          "OpenRouter lists a 32,768-token context window, text input and decisions as output",
          "OpenRouter's documentation lists a Decisions method in its Python, TypeScript and Go SDKs, marked alpha"
        ],
        "weaknesses": [
          "Public preview only. Microsoft's post gives no general availability date and states no licence for the hosted model",
          "No model-specific rate limit, retention statement or deprecation policy was found. Microsoft's Foundry data page covers Models sold by Azure in general",
          "The Foundry route needs an Azure subscription, a deployment of the model and a Microsoft Entra token, all set up by a person",
          "Microsoft describes its Xbox Research result as competitive with GPT-5 or GPT-6 Sol on quality, not better, and the two Microsoft posts name different baselines for it",
          "Azure status history lists a Foundry Models incident on 29 September 2026 in Sweden Central, with intermittent failures and HTTP 5xx for about six hours"
        ],
        "agentNotes": [
          "Use OpenRouter's Decisions method, not an OpenAI chat-completions SDK. OpenRouter says chat completions SDKs will not work with this model",
          "On Foundry, send the request to the deployment's /providers/microsoft/v1/systemone path with a Microsoft Entra token for https://cognitiveservices.azure.com/.default, not an API key",
          "Take the deployment name from the Foundry quickstart before the first call. Microsoft says to confirm the route and authentication header, and the pages reviewed do not give the name",
          "Keep each request within OpenRouter's 32,768-token context and send only fixed options, since the model is not intended for open-ended generation",
          "Measure latency and calibration on your own labelled cases before relying on Microsoft's latency and 'nine times out of 10' statements"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "E",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 39
          }
        ],
        "editorialScores": {
          "ergonomics": 50,
          "maintenance": 40,
          "payments": 20,
          "reliability": 30,
          "schema": 43,
          "security": 52,
          "transparency": 22
        },
        "provenanceScore": 41
      },
      "connect": {
        "http": "curl -X POST \"$AZURE_ENDPOINT/providers/microsoft/v1/systemone\" \\\n  -H \"Authorization: Bearer $ENTRA_TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\":\"\u003cdeployment-name\u003e\",\"state\":\"The customer requests a refund for a duplicate charge.\",\"questions\":{\"team\":{\"type\":\"choice\",\"instructions\":\"Which team should handle this request?\",\"criteria\":{\"billing\":\"Charges, invoices, refunds or subscription payments\",\"technical\":\"Software errors or integration failures\"}}}}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.decision",
        "tool": "https://letme.dev/microsoft-decision-1"
      },
      "sameCompany": [
        "azure-foundry-fine-tuning",
        "azure-ai-content-safety",
        "azure-speech-to-text",
        "azure-text-to-speech",
        "microsoft-agent-framework",
        "microsoft-execution-containers",
        "microsoft-entra-agent-id",
        "azure-key-vault",
        "azure-document-intelligence",
        "azure-devops-mcp",
        "microsoft-learn-mcp",
        "playwright-mcp",
        "azure-mcp",
        "azure-maps",
        "azure-translator",
        "microsoft-graph-calendar",
        "azure-blob-storage",
        "onedrive-sharepoint",
        "microsoft-teams",
        "dynamics-365-sales",
        "power-automate",
        "foundry-local",
        "microsoft-advertising-api",
        "microsoft-excel-graph",
        "outlook-mail-graph"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Decision input",
          "unit": "1m-tokens",
          "usd": 0.042,
          "note": "US and EU Datazone on Foundry and OpenRouter. Microsoft's post says usage charges apply."
        },
        {
          "item": "Decision output",
          "unit": "1m-tokens",
          "usd": 0,
          "note": "Free per Microsoft's post and OpenRouter. The Foundry price table shows N/A."
        }
      ],
      "provenance": {
        "legalEntity": "",
        "domain": "microsoft.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/openai/data-privacy",
        "statusPage": "https://azure.status.microsoft/en-us/status/history/",
        "changelog": "",
        "securityTxt": "expired",
        "checked": "2026-10-10",
        "notes": [
          "The Foundry endpoint is account-specific, given as AZURE_ENDPOINT in Microsoft's sample, so endpointOnVendorDomain is not set.",
          "The legal entity name and Microsoft's product terms were not read, so terms is empty.",
          "Microsoft's security.txt at www.microsoft.com/.well-known/security.txt gives an Expires date of 2026-09-23, which has passed.",
          "The Azure status history page shows a Foundry Models component and dated incidents. It is shared by all Foundry models, not specific to this one.",
          "The Foundry model catalogue page at ai.azure.com is rendered by script and returned no model content when read."
        ],
        "score": 41
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/microsoft-decision-1.json",
      "live": {
        "slug": "microsoft-decision-1",
        "vendorStatus": {
          "page": "https://azure.status.microsoft/en-us/status/history",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-10T11:53:10.031474948Z"
        },
        "updatedAt": "2026-10-10T11:53:10.031474948Z"
      }
    },
    "facts": [
      {
        "a": "Model API",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "Liquid AI",
        "b": "Microsoft",
        "name": "Vendor"
      },
      {
        "a": "https://api.liquid.ai/decisions/v1/systemone",
        "b": "no (local only)",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "OAuth or key",
        "name": "Auth"
      },
      {
        "a": "Freemium",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "not published",
        "b": "free",
        "name": "Price for decision models"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published",
        "b": "Not stated. The announcement and OpenRouter's model page name no licence, and no weights repository was found.",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-07",
        "b": "2026-10-09",
        "name": "Last release"
      },
      {
        "a": "2026-09-30",
        "b": "no document linked",
        "name": "Terms last updated"
      },
      {
        "a": "2026-09-30",
        "b": "",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes",
        "b": "",
        "name": "Customer content may train models"
      },
      {
        "a": "not found in the text",
        "b": "",
        "name": "Terms restrict automated access"
      },
      {
        "a": "not found in the text",
        "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"
      }
    ],
    "faq": [
      {
        "answer": "Liquid d1 scores 43.5 (E) on agent readiness against Microsoft-Decision-1's 39 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability and security \u0026 auth.",
        "question": "Which is better for AI agents, Liquid d1 or Microsoft-Decision-1?"
      },
      {
        "answer": "Liquid d1 needs an API key. Microsoft-Decision-1 takes an API key or an OAuth sign-in.",
        "question": "Do Liquid d1 and Microsoft-Decision-1 need an API key?"
      },
      {
        "answer": "Liquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone. No hosted endpoint is listed for Microsoft-Decision-1.",
        "question": "Can an agent call Liquid d1 and Microsoft-Decision-1 without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Schema \u0026 documentation, 59 against 43",
          "Agent ergonomics, 63 against 50",
          "Payments \u0026 pricing, 27 against 20",
          "Maintenance \u0026 community, 60 against 40",
          "Transparency \u0026 trust, 54 against 32"
        ],
        "also": [
          "A hosted endpoint, with nothing to install"
        ],
        "goodFor": "Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.",
        "slug": "liquid-d1",
        "watchFor": "The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found"
      },
      {
        "aheadOn": [
          "Reliability, 30 against 21",
          "Security \u0026 auth, 52 against 35"
        ],
        "also": null,
        "goodFor": "Routing, classification, prioritisation and rubric checks over text, where a fixed set of options and a low price per token matter more than generated text.",
        "slug": "microsoft-decision-1",
        "watchFor": "Public preview only. Microsoft's post gives no general availability date and states no licence for the hosted model"
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        "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"
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        "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-microsoft-decision-1.json",
        "title": "Celeris-1 Decision vs Microsoft-Decision-1",
        "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-microsoft-decision-1"
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      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1.json",
        "title": "Clef vs Liquid d1",
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        "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-microsoft-decision-1.json",
        "title": "Clef vs Microsoft-Decision-1",
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/decider-vs-liquid-d1.json",
        "title": "Decider vs Liquid d1",
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        "json": "https://www.anchorterminal.com/compare/decider-vs-microsoft-decision-1.json",
        "title": "Decider vs Microsoft-Decision-1",
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        "url": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1"
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      {
        "json": "https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1.json",
        "title": "GLiClass vs Microsoft-Decision-1",
        "url": "https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1"
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      {
        "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.json",
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        "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1"
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        "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-microsoft-decision-1.json",
        "title": "Kev vs Microsoft-Decision-1",
        "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-microsoft-decision-1"
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        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex.json",
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        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex"
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      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.json",
        "title": "Liquid d1 vs OpenAI Decisions API",
        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api"
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      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-pplx-decider.json",
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.json",
        "title": "Liquid d1 vs Jev",
        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev"
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      {
        "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela.json",
        "title": "Liquid d1 vs Vela 2.0",
        "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela"
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      {
        "json": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-nace-drex.json",
        "title": "Microsoft-Decision-1 vs Drex 1.5",
        "url": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-nace-drex"
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        "json": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-openai-decisions-api.json",
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        "weight": 10
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        "microsoft-decision-1": 43,
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        "weight": 13
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        "weight": 13
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        "weight": 14
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        "edge": "liquid-d1",
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        "weight": 10
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        "weight": 10
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        "key": "maintenance",
        "liquid-d1": 60,
        "microsoft-decision-1": 40,
        "name": "Maintenance \u0026 community",
        "weight": 7
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        "edge": "liquid-d1",
        "key": "transparency",
        "liquid-d1": 54,
        "microsoft-decision-1": 32,
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      "liquid-d1": "The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.",
      "microsoft-decision-1": "Microsoft publishes $0.042 per million input tokens, with output free, and the model is callable through Microsoft Foundry and OpenRouter. It is in public preview. No licence, model-specific retention statement, rate limit or deprecation policy was found, and the Foundry route needs an Azure deployment and an Entra token. Latency and accuracy figures are Microsoft's claims."
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  "markdown": "Liquid d1 scores 43.5 (E) on agent readiness against Microsoft-Decision-1's 39 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability and security \u0026 auth. Both do decision models.\n\n- Liquid d1: grade E, 43.5/100, rank #886 of 954. Markdown https://www.anchorterminal.com/tools/liquid-d1.md · JSON https://www.anchorterminal.com/api/v1/tools/liquid-d1.json\n- Microsoft-Decision-1: grade E, 39/100, rank #921 of 954. Markdown https://www.anchorterminal.com/tools/microsoft-decision-1.md · JSON https://www.anchorterminal.com/api/v1/tools/microsoft-decision-1.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 91 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n\n## Which one, for what\n\n### Liquid d1 (E)\n\nGood for: Classification, routing, yes or no gates and rubric scoring over text and images at a low per-token price, or on your own hardware with d1-3B where data can't leave.\n\nAhead on:\n- Schema \u0026 documentation, 59 against 43\n- Agent ergonomics, 63 against 50\n- Payments \u0026 pricing, 27 against 20\n- Maintenance \u0026 community, 60 against 40\n- Transparency \u0026 trust, 54 against 32\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: The terms of 30 September 2026 allow Liquid to train on API content, with no opt-out or zero-retention option found\n\n### Microsoft-Decision-1 (E)\n\nGood for: Routing, classification, prioritisation and rubric checks over text, where a fixed set of options and a low price per token matter more than generated text.\n\nAhead on:\n- Reliability, 30 against 21\n- Security \u0026 auth, 52 against 35\n\nWatch for: Public preview only. Microsoft's post gives no general availability date and states no licence for the hosted model\n\n\n## Score by category\n\n| Category | Weight | Liquid d1 | Microsoft-Decision-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 21 | 30 | Microsoft-Decision-1 +9 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 59 | 43 | Liquid d1 +16 |\n| Agent ergonomics | 13% (16.2 this run) | 63 | 50 | Liquid d1 +13 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 52 | Microsoft-Decision-1 +17 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 27 | 20 | Liquid d1 +7 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 60 | 40 | Liquid d1 +20 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 54 | 32 | Liquid d1 +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **43.5 · E** | **39 · E** | |\n\n## Facts side by side\n\n| Fact | Liquid d1 | Microsoft-Decision-1 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Liquid AI | Microsoft |\n| Hosted endpoint | `https://api.liquid.ai/decisions/v1/systemone` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Freemium | Pay per use |\n| Price for decision models | not published | free |\n| x402 | no | no |\n| Licence | The hosted d1 model is proprietary under Liquid AI's terms of service. The d1-3B and d1-omni-600M weights and model code are under the LFM Open Licence v1.0, which is based on Apache-2.0 and conditions commercial use on annual revenue under $10 million. Training code and data aren't published | Not stated. The announcement and OpenRouter's model page name no licence, and no weights repository was found. |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-10-07 | 2026-10-09 |\n| Terms last updated | 2026-09-30 | no document linked |\n| Privacy policy last updated | 2026-09-30 |  |\n| Customer content may train models | yes |  |\n| Terms restrict automated access | not found in the text |  |\n| Terms restrict benchmarking | not found in the text |  |\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\n## Verdicts\n\n**Liquid d1.** The hosted API costs $0.04 per million input tokens and takes TypeSafe's System One request, and the d1-3B weights run locally through Transformers or llama.cpp. Liquid's terms allow it to train on API content, and no status page, rate limits or error reference were found. The family launched between 22 September and 7 October 2026.\n\n**Microsoft-Decision-1.** Microsoft publishes $0.042 per million input tokens, with output free, and the model is callable through Microsoft Foundry and OpenRouter. It is in public preview. No licence, model-specific retention statement, rate limit or deprecation policy was found, and the Foundry route needs an Azure deployment and an Entra token. Latency and accuracy figures are Microsoft's claims.\n\n## Before you call either\n\n### Liquid d1\n\n1. POST to https://api.liquid.ai/decisions/v1/systemone with a `liquid_` key as a Bearer header. This is not a chat-completions endpoint\n2. Use `d1` for images. `d1:free` is text-only and answers that it does not accept images\n3. Send images as Base64 data URLs in `images`, at most 8, with the whole JSON body under 4.5 MB. Remote image URLs are refused\n4. Budget tokens per question. Each question is billed as its own prompt, with its text and every image counted again\n5. Don't send confidential content to the hosted API, since the terms allow training on it. Run d1-3B locally for that data\n\n### Microsoft-Decision-1\n\n1. Use OpenRouter's Decisions method, not an OpenAI chat-completions SDK. OpenRouter says chat completions SDKs will not work with this model\n2. On Foundry, send the request to the deployment's /providers/microsoft/v1/systemone path with a Microsoft Entra token for https://cognitiveservices.azure.com/.default, not an API key\n3. Take the deployment name from the Foundry quickstart before the first call. Microsoft says to confirm the route and authentication header, and the pages reviewed do not give the name\n4. Keep each request within OpenRouter's 32,768-token context and send only fixed options, since the model is not intended for open-ended generation\n5. Measure latency and calibration on your own labelled cases before relying on Microsoft's latency and 'nine times out of 10' statements\n\n## Questions\n\n### Which is better for AI agents, Liquid d1 or Microsoft-Decision-1?\n\nLiquid d1 scores 43.5 (E) on agent readiness against Microsoft-Decision-1's 39 (E), and leads in 5 of 7 scored categories. Microsoft-Decision-1 leads on reliability and security \u0026 auth.\n\n### Do Liquid d1 and Microsoft-Decision-1 need an API key?\n\nLiquid d1 needs an API key. Microsoft-Decision-1 takes an API key or an OAuth sign-in.\n\n### Can an agent call Liquid d1 and Microsoft-Decision-1 without installing anything?\n\nLiquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone. No hosted endpoint is listed for Microsoft-Decision-1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/liquid-d1-vs-microsoft-decision-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/liquid-d1-vs-microsoft-decision-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"liquid-d1\", \"b\": \"microsoft-decision-1\"}`. From a terminal: `anchor compare liquid-d1 microsoft-decision-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/liquid-d1.json and https://www.anchorterminal.com/api/v1/tools/microsoft-decision-1.json\n\n## Other comparisons with Liquid d1 or Microsoft-Decision-1\n\n- [Celeris-1 Decision vs Liquid d1](https://www.anchorterminal.com/compare/celeris-1-decision-vs-liquid-d1.md)\n- [Celeris-1 Decision vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/celeris-1-decision-vs-microsoft-decision-1.md)\n- [Clef vs Liquid d1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1.md)\n- [Clef vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-microsoft-decision-1.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- [Decider vs Liquid d1](https://www.anchorterminal.com/compare/decider-vs-liquid-d1.md)\n- [Decider vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/decider-vs-microsoft-decision-1.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1.md)\n- [Kev vs Liquid d1](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.md)\n- [Kev vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-microsoft-decision-1.md)\n- [Liquid d1 vs Drex 1.5](https://www.anchorterminal.com/compare/liquid-d1-vs-nace-drex.md)\n- [Liquid d1 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.md)\n- [Liquid d1 vs pplx-decider](https://www.anchorterminal.com/compare/liquid-d1-vs-pplx-decider.md)\n- [Liquid d1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.md)\n- [Liquid d1 vs Jev](https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.md)\n- [Liquid d1 vs Vela 2.0](https://www.anchorterminal.com/compare/liquid-d1-vs-vela.md)\n- [Microsoft-Decision-1 vs Drex 1.5](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-nace-drex.md)\n- [Microsoft-Decision-1 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-openai-decisions-api.md)\n- [Microsoft-Decision-1 vs pplx-decider](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-pplx-decider.md)\n- [Microsoft-Decision-1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-strands-decider.md)\n- [Microsoft-Decision-1 vs Jev](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-typesafe-jev.md)\n- [Microsoft-Decision-1 vs Vela 2.0](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-vela.md)\n",
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