{ "data": { "a": { "slug": "jaredpalmer-kev", "name": "Kev", "vendor": "Jared Palmer", "vendorUrl": "https://github.com/jaredpalmer", "kind": "model", "category": "decision-models", "summary": "Kev is a family of four open-weight decision models by Jared Palmer, released together as Kev 1.0 on 1 October 2026 under Apache-2.0.", "url": "https://www.anchorterminal.com/tools/jaredpalmer-kev", "markdownUrl": "https://www.anchorterminal.com/tools/jaredpalmer-kev.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jaredpalmer-kev.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jaredpalmer-kev.json", "repo": "https://github.com/jaredpalmer/kev", "license": "Apache-2.0 (code, adapters and weights)", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "No account. `kev.serve` binds to 127.0.0.1 and is open by default. Setting `KEV_API_KEY` makes it require `Authorization: Bearer \u003ckey\u003e` on `/v1/*`, which the TypeSafe clients always send. The weights download from Hugging Face without an account.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy. You pay for the hardware. The Modal deploy skill lists $0.80 an hour for Kev-0.8B on an L4, $1.95 for Kev-4B on an L40S, $3.95 for Kev-9B on an H100 and $6.25 for Kev-27B on a B200 while a container is up, scaling to zero after five idle minutes (https://github.com/jaredpalmer/kev/blob/main/skills/kev-deploy/SKILL.md). Those are Modal's GPU rates as the skill records them, not a Kev price.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Kev is software you run, and its server has no payment route (checked 2026-10-02).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-02" }, "docsUrl": "https://github.com/jaredpalmer/kev#readme", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python" ], "lastRelease": "2026-10-01", "graded": true, "anchor": { "graded": true, "score": 67.4, "grade": "B", "agentReady": false, "rank": 194, "ranked": true, "rankOf": 629, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 83, "payments": 60, "reliability": 73, "schema": 77, "security": 49, "transparency": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models. No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv.", "bestFor": "Self-hosted classification, routing, triage and rubric scoring where a probability matters, especially for teams already calling Jev who want the same API on their own hardware.", "strengths": [ "Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models", "The same `/v1/systemone` request and answer shapes as Jev, and the README says TypeSafe's Python SDK works against it unchanged", "A fitted temperature per checkpoint, with Brier scores, calibration error and confident-error rates published for each model", "Runs on CUDA, ROCm and Apple Silicon, from a 4 GB GPU for Kev-0.8B to one 80 GB GPU for Kev-27B, and deploys to Modal with one command", "Release notes that list known failures with numbers, such as date arithmetic and Kev-0.8B's tool-routing accuracy" ], "weaknesses": [ "No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv", "Kev-0.8B, 4B and 9B are validated to 8,192 tokens of state, though the server accepts 65,536", "Jared Palmer wrote 312 of the 333 commits we cloned", "No SECURITY.md, disclosure policy or advisories, and the server is open unless `KEV_API_KEY` is set", "Below 27B it trails Jev on knowledge questions and date arithmetic, with MMLU-Pro at 0.59 for Kev-9B against Jev's 0.84" ], "agentNotes": [ "Install from the repository. The `kev` package on PyPI is an unrelated project", "Pin a checkpoint with `@v1.0`, as in `jaredpalmer/kev-4b@v1.0`, so tuned thresholds keep their meaning", "Keep states under 8,192 tokens on Kev-0.8B, 4B and 9B, or use Kev-27B for long documents", "Set `KEV_DATE_FACTS=1` when a decision depends on the gap between two dates", "Expect a 422 naming the token count when a state passes 65,536 tokens. The server refuses it instead of cutting it" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 67.4 } ], "editorialScores": { "ergonomics": 78, "maintenance": 83, "payments": 60, "reliability": 73, "schema": 77, "security": 49, "transparency": 70 }, "provenanceScore": 27 }, "connect": { "install": "git clone https://github.com/jaredpalmer/kev.git \u0026\u0026 cd kev \u0026\u0026 uv sync --extra serve\nuv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b@v1.0 --port 8009", "http": "curl -s localhost:8009/v1/systemone -H 'content-type: application/json' \\\n -d '{\"model\":\"kev-latest\",\"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\"}}}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/jaredpalmer-kev" }, "area": "models", "provenance": { "legalEntity": "", "domain": "github.com/jaredpalmer", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/jaredpalmer/kev/releases", "securityTxt": "none", "checked": "2026-10-01", "notes": [ "An individual's open-source project under Apache-2.0, with no company named in the licence, README or package metadata. The pyproject names Jared Palmer as author.", "No vendor domain. The code is at github.com/jaredpalmer/kev and the weights at huggingface.co/jaredpalmer, so the domain line names the GitHub account and scores no domain age.", "Software you run, so there's no hosted endpoint, terms or privacy policy to check.", "The changelog is the GitHub releases page (`kev-1.0`, 1 October 2026) and docs/releases/kev-1.0.md." ], "score": 27 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jaredpalmer-kev.json", "live": { "slug": "jaredpalmer-kev", "versions": [ { "registry": "github", "name": "jaredpalmer/kev", "version": "kev-1.0", "released": "2026-10-01", "seenAt": "2026-10-08T16:17:19.912761154Z" } ], "githubStars": 8720, "domain": { "domain": "github.com/jaredpalmer", "checkedAt": "2026-10-04T13:10:21.931207592Z" }, "updatedAt": "2026-10-08T16:17:19.912761154Z" } }, "answer": "Kev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust.", "b": { "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": 582, "ranked": true, "rankOf": 629, "categoryRank": 8, "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": "2026-10-08T19:08:51.964465194Z", "lastOk": true, "lastStatus": 404, "lastMs": 233, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 166, "p95ms24h": 391, "samples24h": 42, "samples30d": 42, "days": [ { "date": "2026-10-08", "probes": 42, "ok": 42 } ] }, "securityTxt": { "url": "https://liquid.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:39:08.129742302Z" }, "pages": [ { "url": "https://www.liquid.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:28:45.318974429Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "42d9e4ba3a39" }, { "url": "https://www.liquid.ai/terms-conditions", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:28:47.564454912Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "8fd8f3573ffc" } ], "updatedAt": "2026-10-08T19:08:51.964465194Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Jared Palmer", "b": "Liquid AI", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.liquid.ai/decisions/v1/systemone", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (code, adapters and weights)", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-01", "b": "2026-10-07", "name": "Last release" }, { "a": "no document linked", "b": "2026-09-30", "name": "Terms last updated" }, { "a": "no document linked", "b": "2026-09-30", "name": "Privacy policy last updated" }, { "a": "", "b": "yes", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Kev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust.", "question": "Which is better for AI agents, Kev or Liquid d1?" }, { "answer": "Kev needs no key. Liquid d1 needs an API key.", "question": "Do Kev and Liquid d1 need an API key?" }, { "answer": "No hosted endpoint is listed for Kev. Liquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone.", "question": "Can an agent call Kev and Liquid d1 without installing anything?" }, { "answer": "Kev is open source (Apache-2.0 (code, adapters and weights)). No open-source release is listed for Liquid d1.", "question": "Are Kev and Liquid d1 open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 73 against 21", "Schema \u0026 documentation, 77 against 59", "Agent ergonomics, 78 against 63", "Security \u0026 auth, 49 against 35", "Payments \u0026 pricing, 60 against 27", "Maintenance \u0026 community, 83 against 60" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "Self-hosted classification, routing, triage and rubric scoring where a probability matters, especially for teams already calling Jev who want the same API on their own hardware.", "slug": "jaredpalmer-kev", "watchFor": "No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv" }, { "aheadOn": [ "Transparency \u0026 trust, 54 against 49" ], "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" } ], "job": { "capability": "inference.decision", "name": "Inference decision" }, "others": [ { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-jaredpalmer-kev.json", "title": "Clef vs Kev", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1.json", "title": "Clef vs Liquid d1", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/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/jaredpalmer-kev-vs-openai-decisions-api.json", "title": "Kev vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider.json", "title": "Kev vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev.json", "title": "Kev vs Jev", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela.json", "title": "Kev vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.json", "title": "Liquid d1 vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider.json", "title": "Liquid d1 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev.json", "title": "Liquid d1 vs Jev", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela.json", "title": "Liquid d1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela" } ], "scores": [ { "by": 52, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 73, "key": "reliability", "liquid-d1": 21, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 18, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 77, "key": "schema", "liquid-d1": 59, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 15, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 78, "key": "ergonomics", "liquid-d1": 63, "name": "Agent ergonomics", "weight": 13 }, { "by": 14, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 49, "key": "security", "liquid-d1": 35, "name": "Security \u0026 auth", "weight": 14 }, { "by": 33, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 60, "key": "payments", "liquid-d1": 27, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 23, "edge": "jaredpalmer-kev", "jaredpalmer-kev": 83, "key": "maintenance", "liquid-d1": 60, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 5, "edge": "liquid-d1", "jaredpalmer-kev": 49, "key": "transparency", "liquid-d1": 54, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Kev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust. Both do inference decision.", "verdicts": { "jaredpalmer-kev": "Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models. No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1", "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.md", "slim": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.min.md" }, "markdown": "Kev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust. Both do inference decision.\n\n- Kev: grade B, 67.4/100, rank #194 of 629. Markdown https://www.anchorterminal.com/tools/jaredpalmer-kev.md · JSON https://www.anchorterminal.com/api/v1/tools/jaredpalmer-kev.json\n- Liquid d1: grade E, 43.5/100, rank #582 of 629. Markdown https://www.anchorterminal.com/tools/liquid-d1.md · JSON https://www.anchorterminal.com/api/v1/tools/liquid-d1.json\n\n## Which one, for what\n\n### Kev (B)\n\nGood for: Self-hosted classification, routing, triage and rubric scoring where a probability matters, especially for teams already calling Jev who want the same API on their own hardware.\n\nAhead on:\n- Reliability, 73 against 21\n- Schema \u0026 documentation, 77 against 59\n- Agent ergonomics, 78 against 63\n- Security \u0026 auth, 49 against 35\n- Payments \u0026 pricing, 60 against 27\n- Maintenance \u0026 community, 83 against 60\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv\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- Transparency \u0026 trust, 54 against 49\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\n## Score by category\n\n| Category | Weight | Kev | Liquid d1 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 73 | 21 | Kev +52 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 77 | 59 | Kev +18 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 63 | Kev +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 49 | 35 | Kev +14 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 27 | Kev +33 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 60 | Kev +23 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 49 | 54 | Liquid d1 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **67.4 · B** | **43.5 · E** | |\n\n## Facts side by side\n\n| Fact | Kev | Liquid d1 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Jared Palmer | Liquid AI |\n| Hosted endpoint | no (local only) | `https://api.liquid.ai/decisions/v1/systemone` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Freemium |\n| x402 | no | no |\n| Licence | Apache-2.0 (code, adapters and weights) | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-01 | 2026-10-07 |\n| Terms last updated | no document linked | 2026-09-30 |\n| Privacy policy last updated | no document linked | 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| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**Kev.** Apache-2.0 code, adapters and heads on Apache-2.0 Qwen bases, with release tarballs and SHA-256 checksums for the 0.8B, 4B and 9B models. No package. `pip install kev` installs an unrelated 2021 ORM, so Kev runs from a Git clone with uv.\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## Before you call either\n\n### Kev\n\n1. Install from the repository. The `kev` package on PyPI is an unrelated project\n2. Pin a checkpoint with `@v1.0`, as in `jaredpalmer/kev-4b@v1.0`, so tuned thresholds keep their meaning\n3. Keep states under 8,192 tokens on Kev-0.8B, 4B and 9B, or use Kev-27B for long documents\n4. Set `KEV_DATE_FACTS=1` when a decision depends on the gap between two dates\n5. Expect a 422 naming the token count when a state passes 65,536 tokens. The server refuses it instead of cutting it\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## Questions\n\n### Which is better for AI agents, Kev or Liquid d1?\n\nKev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust.\n\n### Do Kev and Liquid d1 need an API key?\n\nKev needs no key. Liquid d1 needs an API key.\n\n### Can an agent call Kev and Liquid d1 without installing anything?\n\nNo hosted endpoint is listed for Kev. Liquid d1 has a hosted endpoint at https://api.liquid.ai/decisions/v1/systemone.\n\n### Are Kev and Liquid d1 open source?\n\nKev is open source (Apache-2.0 (code, adapters and weights)). No open-source release is listed for Liquid d1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"jaredpalmer-kev\", \"b\": \"liquid-d1\"}`. From a terminal: `anchor compare jaredpalmer-kev liquid-d1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jaredpalmer-kev.json and https://www.anchorterminal.com/api/v1/tools/liquid-d1.json\n\n## Other comparisons with Kev or Liquid d1\n\n- [Clef vs Kev](https://www.anchorterminal.com/compare/cloudflare-clef-vs-jaredpalmer-kev.md)\n- [Clef vs Liquid d1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-liquid-d1.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- [Kev vs OpenAI Decisions API](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-openai-decisions-api.md)\n- [Kev vs Strands Decider 2B](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-strands-decider.md)\n- [Kev vs Jev](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-typesafe-jev.md)\n- [Kev vs Vela 2.0](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela.md)\n- [Liquid d1 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/liquid-d1-vs-openai-decisions-api.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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-08", "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": "Kev vs Liquid d1", "url": "" } ], "description": "Kev scores 67.4 (B) on agent readiness against Liquid d1's 43.5 (E), and leads in 6 of 7 scored categories. Liquid d1 leads on transparency \u0026 trust. Both do inference decision. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Kev B 67.4", "Liquid d1 E 43.5", "scores" ], "h1": "Kev vs Liquid d1", "image": "https://www.anchorterminal.com/assets/og/compare-jaredpalmer-kev-vs-liquid-d1.png", "path": "/compare/jaredpalmer-kev-vs-liquid-d1", "published": "2026-10-01", "section": "tools", "title": "Kev vs Liquid d1 for AI agents, B 67.4 vs E 43.5 | Anchor Terminal", "toc": null, "updated": "2026-10-08", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1" }, "tokens": { "markdown": 2150, "slim": 780 }, "version": 1 }