{ "data": { "a": { "slug": "gemini-decision-1", "name": "Gemini Decision 1", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "decision-models", "summary": "Gemini Decision 1 is Google's model for closed-set decisions. It scores yes/no, multiple-choice and rubric questions in one pass and returns probabilities, not text, over text, images, audio, video and PDFs, at $0.10 per million input tokens.", "url": "https://www.anchorterminal.com/tools/gemini-decision-1", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-decision-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-decision-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-decision-1.json", "dns": "gemini-decision-1.grade.anchorterminal.com", "license": "Closed model, used through the Gemini API's Decisions endpoint under Google's terms. The google-genai SDK that calls it is Apache-2.0", "transports": [ "http" ], "packages": [], "auth": "api-key", "authNotes": "An `x-goog-api-key` created in Google AI Studio, the same key as the rest of the Gemini API. The free tier needs no billing; paid use needs billing on the Cloud project, per the Gemini API listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens on the paid tier for text, images, video, audio and PDFs, and no output charge, since the model generates no output tokens. Free of charge on the free tier, where content is used to improve Google's products. Shared input is billed once per request; each question adds its own instruction and criteria tokens. No context caching, Batch API, Flex or Priority. Per the pricing page, 11 November 2026.", "priceSummary": "Pay per use", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in Google's announcement of 11 November 2026 or the SDK. Paid use is billed to a Cloud project, per the Gemini API listing.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-11" }, "docsUrl": "https://aistudio.google.com/docs/decisions-api", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.decision" ], "tags": [ "official", "hosted", "model", "multimodal", "usage-priced", "free-tier", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-11", "graded": true, "anchor": { "graded": true, "score": 63.2, "grade": "B", "agentReady": false, "rank": 630, "ranked": true, "rankOf": 1524, "categoryRank": 13, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 85, "maintenance": 79, "payments": 60, "reliability": 40, "schema": 80, "security": 45, "transparency": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-11" }, "negative": 0, "verdict": "Google's decision model scores up to 1,000 yes/no, choice and rubric questions per request over a 262,144-token multimodal input, at $0.10 per million input tokens, no output charge and a free tier. It is on a v1beta endpoint with no caching or batch discount, so repeated input is paid in full, and every speed and accuracy figure is Google's.", "bestFor": "Routing, tool selection, policy checks and rubric grading over mixed media, where many fixed questions share one input and generated text isn't needed.", "strengths": [ "Price published without a login: $0.10 per million input tokens and $0 output, with the shared input billed once per request", "A no-cost tier in Google AI Studio, so an agent's operator can start with a key and no card", "Up to 1,000 questions in one request over text, images, audio, video and PDFs, within 262,144 tokens", "Typed request and response models in google-genai 3.2.0 (Apache-2.0), with stated ranges for options, levels and probabilities", "Every answer carries a confidence from 0 to 1, so uncertain cases can go to a larger model or a person" ], "weaknesses": [ "No context caching, Batch API, Flex or Priority: the guide says repeated input across requests counts in full", "Launch stage not stated, and the endpoint is on v1beta, which Google says may change", "Free-tier requests are used to improve Google's products, per the pricing page", "Google's latency figures cover contexts up to 16K tokens, not the 262,144-token maximum", "The public rate-limit page has no row for this model; limits are shown only in AI Studio" ], "agentNotes": [ "Call POST /v1beta/decisions or `client.decisions.create` (SDK 3.2 or later), not generateContent or the Interactions API", "Put every question about the same input in one request: there's no caching, and repeated input is billed in full", "Pass media inline (100 MB, 50 MB for PDFs), as a Files API URI or as an HTTPS URL up to 15 MB; YouTube and gs:// links are refused", "Branch on the HTTP 400 error code (safety, prohibited_content, blocklist, image_safety, content_blocked) before retrying", "Keep question keys to [A-Za-z0-9_-] and 64 characters, and options or levels between 2 and 255" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.2 } ], "editorialScores": { "ergonomics": 85, "maintenance": 79, "payments": 60, "reliability": 40, "schema": 80, "security": 45, "transparency": 50 }, "provenanceScore": 89, "checked": { "categories": { "ergonomics": "2026-11-11", "maintenance": "2026-11-11", "payments": "2026-11-11", "reliability": "2026-11-11", "schema": "2026-11-11", "security": "2026-11-11", "transparency": "2026-11-11" }, "last": "2026-11-11", "lastMoved": "2026-11-13", "oldest": "2026-11-11", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://generativelanguage.googleapis.com/v1beta/decisions\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"models/gemini-decision-1\",\"input\":\"The blue mug I ordered arrived cracked.\",\"questions\":{\"damaged\":{\"type\":\"noul\",\"instructions\":\"Did the item arrive damaged?\"},\"team\":{\"type\":\"choice\",\"instructions\":\"Which team handles this?\",\"criteria\":{\"returns\":\"Damaged or wrong items\",\"billing\":\"Charges and refunds\"}}}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/gemini-decision-1" }, "sameCompany": [ "gemini-4-carbon", "gemini-4-hydrogen", "vertex-ai", "embeddinggemma-2", "embeddinggemma-300m", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "shieldgemma", "google-imagen", "gemini-omni-1-2-flash", "google-veo", "google-lyria", "google-speech-to-text", "google-cloud-tts", "gemini-live", "google-adk", "google-memory-bank", "google-secret-manager", "google-cloud-document-ai", "google-weather-api", "chrome-devtools-mcp", "puppeteer", "mcp-toolbox-databases", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "antigravity-cli", "gemini-cli", "harness2", "jules", "gemini-spark", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it", "virustotal" ], "sameModel": [ "gemini-api" ], "area": "models", "unitPrices": [ { "item": "Gemini Decision 1 input", "unit": "1m-tokens", "usd": 0.1, "note": "Text, image, audio, video and PDF. Shared input billed once per request" }, { "item": "Gemini Decision 1 output", "unit": "1m-tokens", "usd": 0, "note": "No output tokens are generated" } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-11-11", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-decision-1.json", "live": { "slug": "gemini-decision-1", "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-13T16:15:44.621683591Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:54:24.506680546Z" }, "updatedAt": "2026-11-13T16:15:44.621683591Z" } }, "answer": "Kev scores 70.5 (BB) on agent readiness against Gemini Decision 1's 63.2 (B), and leads in 3 of 7 scored categories. Gemini Decision 1 leads on agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.", "b": { "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", "dns": "jaredpalmer-kev.grade.anchorterminal.com", "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": 70.5, "grade": "BB", "agentReady": true, "rank": 292, "ranked": true, "rankOf": 1524, "categoryRank": 6, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 63, "payments": 90, "reliability": 73, "schema": 77, "security": 52, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "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 184 of the 210 commits we cloned, and no release has followed Kev 1.0 of 1 October 2026", "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": "BB", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.5 } ], "editorialScores": { "ergonomics": 78, "maintenance": 63, "payments": 90, "reliability": 73, "schema": 77, "security": 52, "transparency": 70 }, "provenanceScore": 39, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-13", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "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": 39 }, "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": "2026-11-13T08:44:57.488445223Z" } ], "githubStars": 9972, "domain": { "domain": "github.com/jaredpalmer", "checkedAt": "2026-11-08T15:12:25.405968518Z" }, "github": { "repo": "jaredpalmer/kev", "checkedAt": "2026-11-09T10:32:44.025572008Z", "stars": 9906, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-03T00:46:58Z", "openIssues": 31, "openPRs": 32, "issuesOpened90d": 48, "issuesClosed90d": 17, "prsMerged90d": 153, "ci": { "branch": "main", "runs": 50, "success": 49, "failure": 1, "other": 0, "latest": "success", "latestAt": "2026-10-22T19:48:25Z" }, "replies": { "sampled": 25, "unanswered": 19, "medianDays": 11.9 } }, "updatedAt": "2026-11-13T08:44:57.488445223Z" } }, "edges": [ { "by": 33, "category": "reliability", "leads": "jaredpalmer-kev", "name": "Reliability", "reasons": { "a": "Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published for the platform, but the public rate-limit page, read on 11 November, has no row for Decision 1 and sends you to AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). The guide and the model page, read on 11 November, state no launch stage; the endpoint is v1beta and the model has one version, so half, as Gemini 4 Carbon was read (5 of 10). 10 + 5 + 5 + 15 + 0 + 5 = 40.", "b": "Scored on the local-package checklist, since Kev is a model you run. It isn't on a package index, because the PyPI name `kev` belongs to an unrelated 2021 project. It installs from the repository with uv and a lockfile, with Python 3.12 and 3.13 stated, or from release tarballs with SHA-256 checksums (10 of 20). Public CI on GitHub Actions runs the unit tests and the playground build on every push, and the latest runs on main had passed when we looked on 2 October. The model parity and API tests need weights and aren't in CI (20 of 25). One open issue, #8 from 20 September on date arithmetic, which the README lists as a limitation with a workaround, `KEV_DATE_FACTS=1` (23 of 25). The Kev 1.0 release notes say what changed, including the server refusing over-long states with a 422 where it used to cut them silently, but the Python package has stayed at 0.1.0 and there's no changelog file (10 of 15). Kev 1.0 fixes the four checkpoints as one versioned family, while the package classifier still says alpha (10 of 15). Re-read on 1 November from a clone. No new release since Kev 1.0, the CI workflow still runs the unit tests and the playground build on pushes and pull requests, and the CI results and issue list rest on the 2 October check, since GitHub's pages answered 403. 10 + 20 + 23 + 10 + 10 = 73." }, "scores": { "a": 40, "b": 73 } }, { "by": 30, "category": "payments", "leads": "jaredpalmer-kev", "name": "Payments \u0026 pricing", "reasons": { "a": "Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The pricing page lists $0.10 per million input tokens on the paid tier for every modality and no output charge, without a login (20). Free of charge on the free tier (20). A Google account and an AI Studio key come first (0). 0 + 20 + 20 + 0 = 40. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 40 is 60.", "b": "Free Apache-2.0 software with nothing to buy from Kev, so 20 + 20 + 20 for pricing, free use and no sign-up. No payment protocol (0). GPU time is yours, on your own hardware or on Modal at the hourly rates the deploy skill lists. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 60 is 90." }, "scores": { "a": 60, "b": 90 } }, { "by": 16, "category": "maintenance", "leads": "gemini-decision-1", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 11 November 2026, the day of this check (30). Deprecations with earliest dates and no stated minimum notice (4 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelogs: google-genai 3.2.0's entry adds the Decisions API; the platform changelog wasn't re-read today (11 of 15). SDK replies not sampled (5 of 10). google-genai 3.2.0 tagged the same day with `client.decisions.create`; the JavaScript SDK is per Google's post (15). Runtimes stated on the platform (10). 30 + 4 + 4 + 11 + 5 + 15 + 10 = 79. The model is hours old, so these lines are the platform's habits more than this model's record.", "b": "Read as a model, for deprecation notice and model churn rather than release counts, on the lines used for Decider, Laya, Jev and the other decision models listed as models. Kev 1.0 on 1 October 2026, a GitHub release and `v1.0` tags on all four Hub repositories, is the last release and the last model change. The Hub's last commits on each repository, on 1 October, change only the model card, and the code on main has changed since (fixes to device probing, the fine-tuning skill and tests, to 23 October) without a release. 31 days on 1 November (20). No deprecation policy or notice period. Each new version replaced the weights on main on the day it shipped, and earlier versions stay pinnable under Hub tags (v7-base on the three smaller models, v1 on Kev-9B, v1-lora on Kev-27B), with the retired `kev-family` release pointing to `kev-1.0` (5 of 12, level with Decider's 5). Kev-4B had several weight updates from 20 to 24 September and Kev-9B v2 and Kev-27B v2 replaced main on 30 September, and since Kev 1.0 fixed the four as one versioned family no weights have changed in 31 days, with pins still working (5 of 8, level with Laya's and above Decider's 4 for continuing replacements). Outside pull requests are still merged (#171 and #226 in October), but Jared Palmer wrote 184 of the 210 commits we cloned and a Devin bot 16 more, and issue replies rest on the 2 October check (18 of 25). No SDK of its own. Kev reuses TypeSafe's Python SDK, and agent skills handle deploys and fine-tunes. Not an MCP server, so no registry entry applies (8 of 15). A CI workflow and a uv lockfile, with torch capped below 2.9, and CI results per the 2 October check (7 of 10). 20 + 5 + 5 + 18 + 8 + 7 = 63." }, "scores": { "a": 79, "b": 63 } }, { "by": 15, "category": "transparency", "leads": "gemini-decision-1", "name": "Transparency \u0026 trust", "reasons": { "a": "Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). The pricing page states that free-tier content is used to improve products and paid-tier content isn't; abuse retention has no number (15 of 30). Deprecations table; the model page names no deprecation for this model (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). Every benchmark, latency and cost comparison is Google's own, and the post gives charts without scores in its text. 15 + 15 + 15 + 5 = 50.", "b": "Apache-2.0 for the code, adapters and heads, on Apache-2.0 Qwen bases. The model cards give the training recipe, and the document and skill training partitions are on the Hub. Kev-27B's base is Qwen's post-trained release, whose training data is unknown, and the card says so (30). Self-hosted, so inputs stay on your hardware, which the model card states. There's no other data statement, and nothing on what a Modal deploy logs (20 of 30). Earlier versions stay at Hub tags, and the retired `kev-family` release points to `kev-1.0`. No deprecation policy (10 of 20). We found no telemetry code in the `kev` package and the docs say nothing either way. Weights download through the Hugging Face Hub client (10 of 20)." }, "scores": { "a": 70, "b": 55 } }, { "by": 7, "category": "ergonomics", "leads": "gemini-decision-1", "name": "Agent ergonomics", "reasons": { "a": "Read on the tool lines, as the other decision-model listings are. Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Answers are probabilities only, and the shared input is billed once across up to 1,000 questions (25 of 25). Limits are written down: 262,144 tokens shared by the input and all questions, 1,000 questions, 2 to 255 options, inline data to 100 MB (50 MB for PDFs), 15 MB per external URL (20 of 20). Errors return HTTP 400 with a named code an agent can branch on, plus the platform's backoff advice (15 of 20). SDK retries and backoff guidance, no idempotency key; nothing is written, so a repeat only costs input again (10 of 20). Three required fields, SDK 3.2 or later in Python and JavaScript, and REST (15 of 15). 25 + 20 + 15 + 10 + 15 = 85.", "b": "Read as an API an agent calls for a decision, as with Jev and Clef. Answers are a probability per allowed option, so output stays small, and the server caches the state, so more questions about the same document pay only for the questions. Validated context is 8,192 tokens on the three smaller models and 65,536 on Kev-27B (20 of 25). The caller sets the output shape, with any number of questions a request, and `/v1/systemone/separate` and `/v1/systemone/permute` check question isolation and option order (18 of 20). A 422 names the state's token count and the limit, and a missing key gets a 401 saying how to send it. Other errors aren't documented (15 of 20). Calls are stateless and safe to retry. There's no retry guidance and no rate limiting in the server (15 of 20). TypeSafe's Python SDK works unchanged per the README, and `kev-latest` is the default model, but setup is a clone, a uv sync and a GPU or a Mac (10 of 15)." }, "scores": { "a": 85, "b": 78 } }, { "by": 7, "category": "security", "leads": "jaredpalmer-kev", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). The pricing page, read on 11 November, says free-tier content is used to improve Google's products and paid-tier content isn't (10 of 20, the platform's reading). Abuse retention with no number (5 of 15). Request logs for billed projects are described for Generate Content; the Decisions guide says nothing on logging (10 of 15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 10 + 5 = 45.", "b": "Read as a model, for retention, training and zero-retention options in place of read-only modes, on the lines used for the other decision models listed as models. No account. `kev.serve` binds to 127.0.0.1 and is open by default, and one optional bearer key (`KEV_API_KEY`) guards `/v1/*`. The deploy skill tells agents to always set it on Modal (15 of 30). Self-hosted, so nothing reaches the author and no caller data can train a model. Each model card says self-hosting keeps inputs on your own hardware and to apply your own data-handling policy. On the Modal route the operator's own Modal logs show every request, and nothing says what Modal keeps (18 of 20, level with Cloudflare's Clef, which keeps nothing unless stored). Caller text is tokenised so it can't produce Kev's delimiter tokens, questions can't read each other, and the playground has presets for fake delimiters. Nothing documents how hostile text in the state can move an answer, and the cards say not to make consequential decisions about people without human review (9 of 15). Each response carries a request ID, token usage and model time. The server keeps no log of calls (6 of 15). No SECURITY.md, disclosure policy, certifications or advisories. Weights are pinned by Hub revision and the release tarballs carry SHA-256 checksums. The pointer head is a pickled `head.pt` loaded with `torch.load`, which PyTorch 2.6 and later, the versions Kev requires, read in weights-only mode by default (4 of 20). 15 + 18 + 9 + 6 + 4 = 52." }, "scores": { "a": 45, "b": 52 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Google", "b": "Jared Palmer", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "free", "b": "free", "name": "Price for decision models" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through the Gemini API's Decisions endpoint under Google's terms. The google-genai SDK that calls it is Apache-2.0", "b": "Apache-2.0 (code, adapters and weights)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-11", "b": "2026-10-01", "name": "Last release" }, { "a": "2026-04-28", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-10-01", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not read yet", "name": "Customer content may train models" }, { "a": "yes", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kev scores 70.5 (BB) on agent readiness against Gemini Decision 1's 63.2 (B), and leads in 3 of 7 scored categories. Gemini Decision 1 leads on agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.", "question": "Which is better for AI agents, Gemini Decision 1 or Kev?" }, { "answer": "Gemini Decision 1, at free against free for Kev. These are the vendors' published prices for the job.", "question": "Which is cheaper for decision models, Gemini Decision 1 or Kev?" }, { "answer": "Gemini Decision 1 needs an API key. Kev needs no key.", "question": "Do Gemini Decision 1 and Kev need an API key?" }, { "answer": "No hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for Kev.", "question": "Can an agent call Gemini Decision 1 and Kev without installing anything?" }, { "answer": "No open-source release is listed for Gemini Decision 1. Kev is open source (Apache-2.0 (code, adapters and weights)).", "question": "Are Gemini Decision 1 and Kev open source?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 85 against 78", "Maintenance \u0026 community, 79 against 63", "Transparency \u0026 trust, 70 against 55" ], "also": [ "Cheaper for decision models, $0 against $0 per 1M tokens" ], "goodFor": "Routing, tool selection, policy checks and rubric grading over mixed media, where many fixed questions share one input and generated text isn't needed.", "slug": "gemini-decision-1", "watchFor": "No context caching, Batch API, Flex or Priority: the guide says repeated input across requests counts in full" }, { "aheadOn": [ "Reliability, 73 against 40", "Security \u0026 auth, 52 against 45", "Payments \u0026 pricing, 90 against 60" ], "also": [ "Agent-ready, a grade of BB or better", "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" } ], "history": { "gemini-decision-1": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 2.5, "from": 60.7, "fromGrade": "C", "to": 63.2, "toGrade": "B" } ], "jaredpalmer-kev": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 3.8, "from": 66.7, "fromGrade": "B", "to": 70.5, "toGrade": "BB" }, { "causes": [ "correction" ], "date": "2026-11-01", "delta": -1.2, "from": 67.9, "fromGrade": "B", "to": 66.7, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.5, "from": 67.4, "fromGrade": "B", "to": 67.9, "toGrade": "B" } ] }, "job": { "capability": "inference.decision", "name": "Decision models" }, "others": [ { "json": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gemini-decision-1.json", "title": "Celeris-1 Decision vs Gemini Decision 1", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gemini-decision-1" }, { "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/cloudflare-clef-vs-gemini-decision-1.json", "title": "Clef vs Gemini Decision 1", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gemini-decision-1" }, { "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/convai-laya-vs-gemini-decision-1.json", "title": "Laya vs Gemini Decision 1", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gemini-decision-1" }, { "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/decider-vs-gemini-decision-1.json", "title": "Decider vs Gemini Decision 1", "url": "https://www.anchorterminal.com/compare/decider-vs-gemini-decision-1" }, { "json": "https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev.json", "title": "Decider vs Kev", "url": "https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/decision-3-vs-gemini-decision-1.json", "title": "Decision 3.0 vs Gemini Decision 1", "url": "https://www.anchorterminal.com/compare/decision-3-vs-gemini-decision-1" }, { "json": "https://www.anchorterminal.com/compare/decision-3-vs-jaredpalmer-kev.json", "title": "Decision 3.0 vs Kev", "url": "https://www.anchorterminal.com/compare/decision-3-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.json", "title": "Gemini Decision 1 vs GLiClass", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-granite-switch.json", "title": "Gemini Decision 1 vs Granite Switch 4.1", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-granite-switch" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-liquid-d1.json", "title": "Gemini Decision 1 vs Liquid d1", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-microsoft-decision-1.json", "title": "Gemini Decision 1 vs Microsoft-Decision-1", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-microsoft-decision-1" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-nace-drex.json", "title": "Gemini Decision 1 vs Drex 1.5", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-nace-drex" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-openai-decisions-api.json", "title": "Gemini Decision 1 vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider.json", "title": "Gemini Decision 1 vs pplx-decider", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider-flash.json", "title": "Gemini Decision 1 vs pplx-decider-flash", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-rightnow-system-zero.json", "title": "Gemini Decision 1 vs RightNow System Zero", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-rightnow-system-zero" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-scaledown.json", "title": "Gemini Decision 1 vs ScaleDown", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-scaledown" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-sprag-spev.json", "title": "Gemini Decision 1 vs SPEV", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-sprag-spev" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-sqwish-kettei.json", "title": "Gemini Decision 1 vs kettei", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-sqwish-kettei" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-strands-decider.json", "title": "Gemini Decision 1 vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-typesafe-jev.json", "title": "Gemini Decision 1 vs Jev", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.json", "title": "Gemini Decision 1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.json", "title": "GLiClass vs Kev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/granite-switch-vs-jaredpalmer-kev.json", "title": "Granite Switch 4.1 vs Kev", "url": "https://www.anchorterminal.com/compare/granite-switch-vs-jaredpalmer-kev" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1.json", "title": "Kev vs Liquid d1", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-liquid-d1" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-nace-drex.json", "title": "Kev vs Drex 1.5", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-nace-drex" }, { "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-pplx-decider.json", "title": "Kev vs pplx-decider", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-pplx-decider" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-pplx-decider-flash.json", "title": "Kev vs pplx-decider-flash", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-pplx-decider-flash" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-rightnow-system-zero.json", "title": "Kev vs RightNow System Zero", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-rightnow-system-zero" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown.json", "title": "Kev vs ScaleDown", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sprag-spev.json", "title": "Kev vs SPEV", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sprag-spev" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei.json", "title": "Kev vs kettei", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei" }, { "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" } ], "panel": null, "scores": [ { "by": 33, "edge": "jaredpalmer-kev", "gemini-decision-1": 40, "jaredpalmer-kev": 73, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 3, "edge": "gemini-decision-1", "gemini-decision-1": 80, "jaredpalmer-kev": 77, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 7, "edge": "gemini-decision-1", "gemini-decision-1": 85, "jaredpalmer-kev": 78, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 7, "edge": "jaredpalmer-kev", "gemini-decision-1": 45, "jaredpalmer-kev": 52, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "edge": "jaredpalmer-kev", "gemini-decision-1": 60, "jaredpalmer-kev": 90, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 16, "edge": "gemini-decision-1", "gemini-decision-1": 79, "jaredpalmer-kev": 63, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 15, "edge": "gemini-decision-1", "gemini-decision-1": 70, "jaredpalmer-kev": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Kev scores 70.5 (BB) on agent readiness against Gemini Decision 1's 63.2 (B), and leads in 3 of 7 scored categories. Gemini Decision 1 leads on agent ergonomics, maintenance \u0026 community and transparency \u0026 trust. Both do decision models. Gemini Decision 1 is cheaper for decision models, $0 against $0 per 1M tokens.", "tasks": null, "tutorials": [ { "title": "Classify a delivery problem with Kev", "tool": "jaredpalmer-kev", "url": "https://www.anchorterminal.com/tutorials/jaredpalmer-kev/" } ], "verdicts": { "gemini-decision-1": "Google's decision model scores up to 1,000 yes/no, choice and rubric questions per request over a 262,144-token multimodal input, at $0.10 per million input tokens, no output charge and a free tier. It is on a v1beta endpoint with no caching or batch discount, so repeated input is paid in full, and every speed and accuracy figure is Google's.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev", "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.min.md" }, "markdown": "Kev scores 70.5 (BB) on agent readiness against Gemini Decision 1's 63.2 (B), and leads in 3 of 7 scored categories. Gemini Decision 1 leads on agent ergonomics, maintenance \u0026 community and transparency \u0026 trust. Both do decision models. Gemini Decision 1 is cheaper for decision models, $0 against $0 per 1M tokens.\n\n- Gemini Decision 1: grade B, 63.2/100, rank #630 of 1524. Markdown https://www.anchorterminal.com/tools/gemini-decision-1.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-decision-1.json\n- Kev: grade BB, 70.5/100, rank #292 of 1524. Markdown https://www.anchorterminal.com/tools/jaredpalmer-kev.md · JSON https://www.anchorterminal.com/api/v1/tools/jaredpalmer-kev.json\n- Best decision models for AI agents: https://www.anchorterminal.com/best/decision-models/index.md\n- All 241 decisions comparisons: https://www.anchorterminal.com/compare/decision-models/index.md\n\n## Which one, for what\n\n### Gemini Decision 1 (B)\n\nGood for: Routing, tool selection, policy checks and rubric grading over mixed media, where many fixed questions share one input and generated text isn't needed.\n\nAhead on:\n- Agent ergonomics, 85 against 78\n- Maintenance \u0026 community, 79 against 63\n- Transparency \u0026 trust, 70 against 55\n\nAlso in its favour:\n- Cheaper for decision models, $0 against $0 per 1M tokens\n\nWatch for: No context caching, Batch API, Flex or Priority: the guide says repeated input across requests counts in full\n\n### Kev (BB)\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 40\n- Security \u0026 auth, 52 against 45\n- Payments \u0026 pricing, 90 against 60\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\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\n## Why the edge\n\n### Reliability: Kev +33\n\n- Gemini Decision 1 (40): Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published for the platform, but the public rate-limit page, read on 11 November, has no row for Decision 1 and sends you to AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). The guide and the model page, read on 11 November, state no launch stage; the endpoint is v1beta and the model has one version, so half, as Gemini 4 Carbon was read (5 of 10). 10 + 5 + 5 + 15 + 0 + 5 = 40.\n- Kev (73): Scored on the local-package checklist, since Kev is a model you run. It isn't on a package index, because the PyPI name `kev` belongs to an unrelated 2021 project. It installs from the repository with uv and a lockfile, with Python 3.12 and 3.13 stated, or from release tarballs with SHA-256 checksums (10 of 20). Public CI on GitHub Actions runs the unit tests and the playground build on every push, and the latest runs on main had passed when we looked on 2 October. The model parity and API tests need weights and aren't in CI (20 of 25). One open issue, #8 from 20 September on date arithmetic, which the README lists as a limitation with a workaround, `KEV_DATE_FACTS=1` (23 of 25). The Kev 1.0 release notes say what changed, including the server refusing over-long states with a 422 where it used to cut them silently, but the Python package has stayed at 0.1.0 and there's no changelog file (10 of 15). Kev 1.0 fixes the four checkpoints as one versioned family, while the package classifier still says alpha (10 of 15). Re-read on 1 November from a clone. No new release since Kev 1.0, the CI workflow still runs the unit tests and the playground build on pushes and pull requests, and the CI results and issue list rest on the 2 October check, since GitHub's pages answered 403. 10 + 20 + 23 + 10 + 10 = 73.\n\n### Payments \u0026 pricing: Kev +30\n\n- Gemini Decision 1 (60): Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The pricing page lists $0.10 per million input tokens on the paid tier for every modality and no output charge, without a login (20). Free of charge on the free tier (20). A Google account and an AI Studio key come first (0). 0 + 20 + 20 + 0 = 40. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 40 is 60.\n- Kev (90): Free Apache-2.0 software with nothing to buy from Kev, so 20 + 20 + 20 for pricing, free use and no sign-up. No payment protocol (0). GPU time is yours, on your own hardware or on Modal at the hourly rates the deploy skill lists. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 60 is 90.\n\n### Maintenance \u0026 community: Gemini Decision 1 +16\n\n- Gemini Decision 1 (79): Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 11 November 2026, the day of this check (30). Deprecations with earliest dates and no stated minimum notice (4 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelogs: google-genai 3.2.0's entry adds the Decisions API; the platform changelog wasn't re-read today (11 of 15). SDK replies not sampled (5 of 10). google-genai 3.2.0 tagged the same day with `client.decisions.create`; the JavaScript SDK is per Google's post (15). Runtimes stated on the platform (10). 30 + 4 + 4 + 11 + 5 + 15 + 10 = 79. The model is hours old, so these lines are the platform's habits more than this model's record.\n- Kev (63): Read as a model, for deprecation notice and model churn rather than release counts, on the lines used for Decider, Laya, Jev and the other decision models listed as models. Kev 1.0 on 1 October 2026, a GitHub release and `v1.0` tags on all four Hub repositories, is the last release and the last model change. The Hub's last commits on each repository, on 1 October, change only the model card, and the code on main has changed since (fixes to device probing, the fine-tuning skill and tests, to 23 October) without a release. 31 days on 1 November (20). No deprecation policy or notice period. Each new version replaced the weights on main on the day it shipped, and earlier versions stay pinnable under Hub tags (v7-base on the three smaller models, v1 on Kev-9B, v1-lora on Kev-27B), with the retired `kev-family` release pointing to `kev-1.0` (5 of 12, level with Decider's 5). Kev-4B had several weight updates from 20 to 24 September and Kev-9B v2 and Kev-27B v2 replaced main on 30 September, and since Kev 1.0 fixed the four as one versioned family no weights have changed in 31 days, with pins still working (5 of 8, level with Laya's and above Decider's 4 for continuing replacements). Outside pull requests are still merged (#171 and #226 in October), but Jared Palmer wrote 184 of the 210 commits we cloned and a Devin bot 16 more, and issue replies rest on the 2 October check (18 of 25). No SDK of its own. Kev reuses TypeSafe's Python SDK, and agent skills handle deploys and fine-tunes. Not an MCP server, so no registry entry applies (8 of 15). A CI workflow and a uv lockfile, with torch capped below 2.9, and CI results per the 2 October check (7 of 10). 20 + 5 + 5 + 18 + 8 + 7 = 63.\n\n### Transparency \u0026 trust: Gemini Decision 1 +15\n\n- Gemini Decision 1 (70): Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). The pricing page states that free-tier content is used to improve products and paid-tier content isn't; abuse retention has no number (15 of 30). Deprecations table; the model page names no deprecation for this model (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). Every benchmark, latency and cost comparison is Google's own, and the post gives charts without scores in its text. 15 + 15 + 15 + 5 = 50.\n- Kev (55): Apache-2.0 for the code, adapters and heads, on Apache-2.0 Qwen bases. The model cards give the training recipe, and the document and skill training partitions are on the Hub. Kev-27B's base is Qwen's post-trained release, whose training data is unknown, and the card says so (30). Self-hosted, so inputs stay on your hardware, which the model card states. There's no other data statement, and nothing on what a Modal deploy logs (20 of 30). Earlier versions stay at Hub tags, and the retired `kev-family` release points to `kev-1.0`. No deprecation policy (10 of 20). We found no telemetry code in the `kev` package and the docs say nothing either way. Weights download through the Hugging Face Hub client (10 of 20).\n\n### Agent ergonomics: Gemini Decision 1 +7\n\n- Gemini Decision 1 (85): Read on the tool lines, as the other decision-model listings are. Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Answers are probabilities only, and the shared input is billed once across up to 1,000 questions (25 of 25). Limits are written down: 262,144 tokens shared by the input and all questions, 1,000 questions, 2 to 255 options, inline data to 100 MB (50 MB for PDFs), 15 MB per external URL (20 of 20). Errors return HTTP 400 with a named code an agent can branch on, plus the platform's backoff advice (15 of 20). SDK retries and backoff guidance, no idempotency key; nothing is written, so a repeat only costs input again (10 of 20). Three required fields, SDK 3.2 or later in Python and JavaScript, and REST (15 of 15). 25 + 20 + 15 + 10 + 15 = 85.\n- Kev (78): Read as an API an agent calls for a decision, as with Jev and Clef. Answers are a probability per allowed option, so output stays small, and the server caches the state, so more questions about the same document pay only for the questions. Validated context is 8,192 tokens on the three smaller models and 65,536 on Kev-27B (20 of 25). The caller sets the output shape, with any number of questions a request, and `/v1/systemone/separate` and `/v1/systemone/permute` check question isolation and option order (18 of 20). A 422 names the state's token count and the limit, and a missing key gets a 401 saying how to send it. Other errors aren't documented (15 of 20). Calls are stateless and safe to retry. There's no retry guidance and no rate limiting in the server (15 of 20). TypeSafe's Python SDK works unchanged per the README, and `kev-latest` is the default model, but setup is a clone, a uv sync and a GPU or a Mac (10 of 15).\n\n### Security \u0026 auth: Kev +7\n\n- Gemini Decision 1 (45): Model reading for gemini-decision-1 on the Gemini API's Decisions endpoint. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). The pricing page, read on 11 November, says free-tier content is used to improve Google's products and paid-tier content isn't (10 of 20, the platform's reading). Abuse retention with no number (5 of 15). Request logs for billed projects are described for Generate Content; the Decisions guide says nothing on logging (10 of 15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 10 + 5 = 45.\n- Kev (52): Read as a model, for retention, training and zero-retention options in place of read-only modes, on the lines used for the other decision models listed as models. No account. `kev.serve` binds to 127.0.0.1 and is open by default, and one optional bearer key (`KEV_API_KEY`) guards `/v1/*`. The deploy skill tells agents to always set it on Modal (15 of 30). Self-hosted, so nothing reaches the author and no caller data can train a model. Each model card says self-hosting keeps inputs on your own hardware and to apply your own data-handling policy. On the Modal route the operator's own Modal logs show every request, and nothing says what Modal keeps (18 of 20, level with Cloudflare's Clef, which keeps nothing unless stored). Caller text is tokenised so it can't produce Kev's delimiter tokens, questions can't read each other, and the playground has presets for fake delimiters. Nothing documents how hostile text in the state can move an answer, and the cards say not to make consequential decisions about people without human review (9 of 15). Each response carries a request ID, token usage and model time. The server keeps no log of calls (6 of 15). No SECURITY.md, disclosure policy, certifications or advisories. Weights are pinned by Hub revision and the release tarballs carry SHA-256 checksums. The pointer head is a pickled `head.pt` loaded with `torch.load`, which PyTorch 2.6 and later, the versions Kev requires, read in weights-only mode by default (4 of 20). 15 + 18 + 9 + 6 + 4 = 52.\n\n## Score by category\n\n| Category | Weight | Gemini Decision 1 | Kev | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 73 | Kev +33 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 77 | Gemini Decision 1 +3 |\n| Agent ergonomics | 13% (16.2 this run) | 85 | 78 | Gemini Decision 1 +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 52 | Kev +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 90 | Kev +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 79 | 63 | Gemini Decision 1 +16 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 55 | Gemini Decision 1 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **70.5 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemini Decision 1 | Kev |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Google | Jared Palmer |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| Price for decision models | free | free |\n| x402 | no | no |\n| Licence | Closed model, used through the Gemini API's Decisions endpoint under Google's terms. The google-genai SDK that calls it is Apache-2.0 | Apache-2.0 (code, adapters and weights) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-11 | 2026-10-01 |\n| Terms last updated | 2026-04-28 | no document linked |\n| Privacy policy last updated | 2026-10-01 | no document linked |\n| Customer content may train models | yes | not read yet |\n| Terms restrict automated access | yes | not read yet |\n| Terms restrict benchmarking | yes | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | none | 3.5/5 (2) |\n\n## How the scores have moved\n\n### Gemini Decision 1\n\n- +2.5 on 13 November 2026 (C to B): checklist\n\n### Kev\n\n- +3.8 on 13 November 2026 (B to BB): checklist\n- -1.2 on 1 November 2026: correction\n- +0.5 on 27 October 2026: checklist\n\n## Verdicts\n\n**Gemini Decision 1.** Google's decision model scores up to 1,000 yes/no, choice and rubric questions per request over a 262,144-token multimodal input, at $0.10 per million input tokens, no output charge and a free tier. It is on a v1beta endpoint with no caching or batch discount, so repeated input is paid in full, and every speed and accuracy figure is Google's.\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## Before you call either\n\n### Gemini Decision 1\n\n1. Call POST /v1beta/decisions or `client.decisions.create` (SDK 3.2 or later), not generateContent or the Interactions API\n2. Put every question about the same input in one request: there's no caching, and repeated input is billed in full\n3. Pass media inline (100 MB, 50 MB for PDFs), as a Files API URI or as an HTTPS URL up to 15 MB; YouTube and gs:// links are refused\n4. Branch on the HTTP 400 error code (safety, prohibited_content, blocklist, image_safety, content_blocked) before retrying\n5. Keep question keys to [A-Za-z0-9_-] and 64 characters, and options or levels between 2 and 255\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## Tutorials\n\n- [Classify a delivery problem with Kev](https://www.anchorterminal.com/tutorials/jaredpalmer-kev/), for Kev\n\n## Questions\n\n### Which is better for AI agents, Gemini Decision 1 or Kev?\n\nKev scores 70.5 (BB) on agent readiness against Gemini Decision 1's 63.2 (B), and leads in 3 of 7 scored categories. Gemini Decision 1 leads on agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.\n\n### Which is cheaper for decision models, Gemini Decision 1 or Kev?\n\nGemini Decision 1, at free against free for Kev. These are the vendors' published prices for the job.\n\n### Do Gemini Decision 1 and Kev need an API key?\n\nGemini Decision 1 needs an API key. Kev needs no key.\n\n### Can an agent call Gemini Decision 1 and Kev without installing anything?\n\nNo hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for Kev.\n\n### Are Gemini Decision 1 and Kev open source?\n\nNo open-source release is listed for Gemini Decision 1. Kev is open source (Apache-2.0 (code, adapters and weights)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-decision-1\", \"b\": \"jaredpalmer-kev\"}`. From a terminal: `anchor compare gemini-decision-1 jaredpalmer-kev`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-decision-1.json and https://www.anchorterminal.com/api/v1/tools/jaredpalmer-kev.json\n\n## Other comparisons with Gemini Decision 1 or Kev\n\n- [Celeris-1 Decision vs Gemini Decision 1](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gemini-decision-1.md)\n- [Celeris-1 Decision vs Kev](https://www.anchorterminal.com/compare/celeris-1-decision-vs-jaredpalmer-kev.md)\n- [Clef vs Gemini Decision 1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gemini-decision-1.md)\n- [Clef vs Kev](https://www.anchorterminal.com/compare/cloudflare-clef-vs-jaredpalmer-kev.md)\n- [Laya vs Gemini Decision 1](https://www.anchorterminal.com/compare/convai-laya-vs-gemini-decision-1.md)\n- [Laya vs Kev](https://www.anchorterminal.com/compare/convai-laya-vs-jaredpalmer-kev.md)\n- [Decider vs Gemini Decision 1](https://www.anchorterminal.com/compare/decider-vs-gemini-decision-1.md)\n- [Decider vs Kev](https://www.anchorterminal.com/compare/decider-vs-jaredpalmer-kev.md)\n- [Decision 3.0 vs Gemini Decision 1](https://www.anchorterminal.com/compare/decision-3-vs-gemini-decision-1.md)\n- [Decision 3.0 vs Kev](https://www.anchorterminal.com/compare/decision-3-vs-jaredpalmer-kev.md)\n- [Gemini Decision 1 vs GLiClass](https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.md)\n- [Gemini Decision 1 vs Granite Switch 4.1](https://www.anchorterminal.com/compare/gemini-decision-1-vs-granite-switch.md)\n- [Gemini Decision 1 vs Liquid d1](https://www.anchorterminal.com/compare/gemini-decision-1-vs-liquid-d1.md)\n- [Gemini Decision 1 vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/gemini-decision-1-vs-microsoft-decision-1.md)\n- [Gemini Decision 1 vs Drex 1.5](https://www.anchorterminal.com/compare/gemini-decision-1-vs-nace-drex.md)\n- [Gemini Decision 1 vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gemini-decision-1-vs-openai-decisions-api.md)\n- [Gemini Decision 1 vs pplx-decider](https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider.md)\n- [Gemini Decision 1 vs pplx-decider-flash](https://www.anchorterminal.com/compare/gemini-decision-1-vs-pplx-decider-flash.md)\n- [Gemini Decision 1 vs RightNow System Zero](https://www.anchorterminal.com/compare/gemini-decision-1-vs-rightnow-system-zero.md)\n- [Gemini Decision 1 vs ScaleDown](https://www.anchorterminal.com/compare/gemini-decision-1-vs-scaledown.md)\n- [Gemini Decision 1 vs SPEV](https://www.anchorterminal.com/compare/gemini-decision-1-vs-sprag-spev.md)\n- [Gemini Decision 1 vs kettei](https://www.anchorterminal.com/compare/gemini-decision-1-vs-sqwish-kettei.md)\n- [Gemini Decision 1 vs Strands Decider 2B](https://www.anchorterminal.com/compare/gemini-decision-1-vs-strands-decider.md)\n- [Gemini Decision 1 vs Jev](https://www.anchorterminal.com/compare/gemini-decision-1-vs-typesafe-jev.md)\n- [Gemini Decision 1 vs Vela 2.0](https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.md)\n- [GLiClass vs Kev](https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.md)\n- [Granite Switch 4.1 vs Kev](https://www.anchorterminal.com/compare/granite-switch-vs-jaredpalmer-kev.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- [Kev vs Drex 1.5](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-nace-drex.md)\n- [Kev vs OpenAI Decisions API](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-openai-decisions-api.md)\n- [Kev vs pplx-decider](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-pplx-decider.md)\n- [Kev vs pplx-decider-flash](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-pplx-decider-flash.md)\n- [Kev vs RightNow System Zero](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-rightnow-system-zero.md)\n- [Kev vs ScaleDown](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-scaledown.md)\n- [Kev vs SPEV](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sprag-spev.md)\n- [Kev vs kettei](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-sqwish-kettei.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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-14", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.8", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemini Decision 1 vs Kev", "url": "" } ], "description": "Kev scores 70.5 (BB) to Gemini Decision 1's 63.2 (B) for decision models. Gemini Decision 1 is cheaper for decision models, $0 against $0 per 1M tokens. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemini Decision 1 B 63.2", "Kev BB 70.5", "scores" ], "h1": "Gemini Decision 1 vs Kev", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gemini-decision-1-vs-jaredpalmer-kev", "published": "2026-10-01", "section": "tools", "title": "Gemini Decision 1 vs Kev for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev" }, "tokens": { "markdown": 6100, "slim": 1130 }, "version": 1 }