{ "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": "Gemini Decision 1 scores 63.2 (B) on agent readiness against GLiClass's 55.5 (C), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing.", "b": { "slug": "gliclass", "name": "GLiClass", "vendor": "Knowledgator", "vendorUrl": "https://www.knowledgator.com", "kind": "model", "category": "decision-models", "summary": "GLiClass is an open-source Python library and family of open-weight zero-shot text classifiers from Knowledgator. It scores every candidate label in one forward pass and runs locally through a pipeline or a Ray Serve endpoint.", "url": "https://www.anchorterminal.com/tools/gliclass", "markdownUrl": "https://www.anchorterminal.com/tools/gliclass.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gliclass.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gliclass.json", "dns": "gliclass.grade.anchorterminal.com", "repo": "https://github.com/Knowledgator/GLiClass", "license": "Apache-2.0 (library and the model weights we checked)", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "gliclass" } ], "auth": "none", "authNotes": "No account or key. The weights are public and ungated on Hugging Face. The bundled Ray Serve deployment has no authentication of any kind, and `python -m gliclass.serve` binds to 0.0.0.0 unless `--host` is passed, so the owner has to restrict the port.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy for the library or the weights. Hardware is the owner's cost. Knowledgator's site links a hosted platform with plans at platform.knowledgator.com, which we couldn't reach on 8 October 2026, so whether it serves GLiClass and at what price is unchecked.", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. GLiClass is software the owner runs, and its server has no payment route (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 555, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.knowledgator.com/docs/frameworks/gliclass/", "capabilities": [ "inference.decision" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "no-auth" ], "lastRelease": "2026-10-31", "graded": true, "anchor": { "graded": true, "score": 55.5, "grade": "C", "agentReady": false, "rank": 959, "ranked": true, "rankOf": 1524, "categoryRank": 17, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 64, "payments": 90, "reliability": 43, "schema": 49, "security": 38, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "An Apache-2.0 classifier family that scores a whole label set in one pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes, and new v4 models from 0.4B to 27B parameters. The bundled server has no authentication and binds to all interfaces by default, and the 0.1.22 process API is undocumented.", "bestFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "strengths": [ "Apache-2.0 code and weights, with `train.py`, the training datasets named on the model cards and an arXiv paper (2508.07662)", "One forward pass scores every label. The base v3.0 card reports 51.6 examples a second averaged over 1 to 128 labels on an A6000 (vendor figures)", "Single-label (softmax) and multi-label (sigmoid) modes, hierarchical label sets, task prompts and few-shot examples in one pipeline call", "Two tagged releases in October 2026 (0.1.21 and 0.1.22) added a calibrator, FP8 serving and a second HTTP interface", "35 public, ungated GLiClass repositories on Hugging Face, among them a v4 family of October 2026 (edge, 2B, 9B, 27B) and ONNX files for v4 edge" ], "weaknesses": [ "No calibration figures on the v3.0 cards, which report F1 only; the v4 2B and 27B repositories hold calibration report files we couldn't read", "The bundled server has no authentication, and `python -m gliclass.serve` binds to 0.0.0.0 by default", "The last three Tests runs on main we could read, on 24 September 2026, failed", "Still 0.1.x with no changelog file. 0.1.18 raised the `transformers` floor to 5.0 in a patch version", "No OpenAPI file, no llms.txt, no SECURITY.md, and the 0.1.22 process API and its error codes are undocumented" ], "agentNotes": [ "Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential", "On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA", "Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error", "Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1", "Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently", "The `/v1/process` interface in 0.1.22 isn't documented. Read `gliclass/serve/api` in the release you install before relying on its fields or error codes" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.5 } ], "editorialScores": { "ergonomics": 60, "maintenance": 64, "payments": 90, "reliability": 43, "schema": 49, "security": 38, "transparency": 60 }, "provenanceScore": 70, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-13", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install gliclass", "http": "pip install \"gliclass[serve]\"\npython -m gliclass.serve --model knowledgator/gliclass-edge-v3.0 --port 8000\ncurl -X POST http://localhost:8000/gliclass \\\n -H \"Content-Type: application/json\" \\\n -d '{\"text\": \"This is a great product.\", \"labels\": [\"positive\", \"negative\", \"neutral\"], \"threshold\": 0.3, \"multi_label\": true}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/gliclass" }, "area": "models", "provenance": { "legalEntity": "Knowledgator Engineering Ltd.", "domain": "knowledgator.com", "domainRegistered": "2021-06-25", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/Knowledgator/GLiClass/releases", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "The terms of use and privacy policy linked from knowledgator.com name Knowledgator Engineering Ltd., London, United Kingdom. Both are dated 21 September 2023.", "Those two documents govern the website and Knowledgator's hosted services, not the library, so `terms` and `privacy` are left out. The Apache-2.0 licence governs what an agent would run.", "Software the owner runs, so there is no hosted endpoint and no status page for it.", "www.knowledgator.com/.well-known/security.txt returned 404 on 8 October, and the repository has no SECURITY.md.", "The code is on github.com under the Knowledgator organisation and the weights on huggingface.co under knowledgator.", "The terms of use (21 September 2023, re-read 4 November 2026) carry no ban on automated access to the website.", "The registration date is carried from the 8 October check, which took it from RDAP, a data service this run doesn't use; it has no first-hand source today." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gliclass.json", "live": { "slug": "gliclass", "versions": [ { "registry": "github", "name": "Knowledgator/GLiClass", "version": "v0.1.20", "released": "2026-07-21", "seenAt": "2026-11-13T08:37:58.812418907Z" }, { "registry": "pypi", "name": "gliclass", "version": "0.1.22", "released": "2026-10-31", "seenAt": "2026-11-13T08:37:56.268457197Z" } ], "githubStars": 581, "pypiWeekly": 22319, "securityTxt": { "url": "https://knowledgator.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-13T16:17:04.270228348Z" }, "domain": { "domain": "knowledgator.com", "registered": "2021-06-25", "source": "https://rdap.verisign.com/com/v1/domain/knowledgator.com", "checkedAt": "2026-11-08T14:46:56.188748811Z" }, "github": { "repo": "knowledgator/gliclass", "checkedAt": "2026-11-09T10:34:32.745715605Z", "stars": 580, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-31T16:28:46Z", "openIssues": 15, "openPRs": 0, "issuesOpened90d": 1, "issuesClosed90d": 1, "prsMerged90d": 6, "ci": { "branch": "main", "runs": 13, "success": 10, "failure": 3, "other": 0, "latest": "success", "latestAt": "2026-10-31T16:25:01Z" }, "replies": { "sampled": 20, "unanswered": 9, "medianDays": 5.9 } }, "updatedAt": "2026-11-13T16:17:04.270228348Z" } }, "edges": [ { "by": 31, "category": "schema", "leads": "gemini-decision-1", "name": "Schema \u0026 documentation", "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 published machine-readable contract for the Decisions endpoint; the request and response types are generated and typed in google-genai 3.2.0, Apache-2.0 (10 of 25). llms.txt with Markdown twins on the platform (10). The guide and model page say what the endpoint is for and list what it doesn't do (no caching, Batch, Flex, Priority, function calling, structured outputs or text generation; no YouTube or gs:// sources; history only as text) (18 of 20). Fields carry stated rules: question keys match [A-Za-z0-9_-]+ up to 64 characters, 1 to 1,000 questions, 2 to 255 options or levels, option keys 1 to 128 characters, P(true) between 0.01 and 0.99, nesting to 100 levels (14 of 15). Python, JavaScript and REST examples, and errors documented as HTTP 400 with codes safety, prohibited_content, blocklist, image_safety and content_blocked (13 of 15). Dated SDK changelog and the platform's dated changelog (15). 10 + 10 + 18 + 14 + 13 + 15 = 80.", "b": "Read for a model the owner serves. No OpenAPI file. The serving page documents the seven request fields of `POST /gliclass` and the response array in a table, and the pipeline's Python signatures carry type hints (10 of 25). docs.knowledgator.com has no llms.txt (404). The README, `docs/streaming.md` and the model cards are Markdown (5 of 10). The cards and docs name the intended uses (topic, sentiment, intent, natural language inference, reranking) and print per-dataset F1 that shows the weak cases, such as 0.3376 on sst5 for base v3.0. The base card has no limitations section and says nothing on calibration (11 of 20). Labels are free strings or a dict of groups, the mode is one of two values and `threshold` is a number. The HTTP handler reads fields from the body without validation, and a list in `texts` is cut to its first item (8 of 15). Examples for every mode in the docs. The handler's 400 and 404 answers are not documented, and a request without `labels` gets a 404 (8 of 15). Versions on PyPI and version numbers in model names, with three one-line GitHub release notes and no changelog (7 of 15). Version 0.1.22 adds a second HTTP interface under `/v1/` (process, batch, models, capabilities, health) with coded errors carrying an HTTP status and a JSON path, in the code only; neither the docs site's serving page nor the README describes it on 4 November, so no line moves." }, "scores": { "a": 80, "b": 49 } }, { "by": 30, "category": "payments", "leads": "gliclass", "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, so 20 + 20 + 20 for pricing, free use and no sign-up under the self-hosted rule. No payment protocol (0). Knowledgator's hosted platform and its plans were unreachable from our network on 8 October 2026 and are not graded here. 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": 25, "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 the other decision models. The answer is a short list of labels and scores and no text is generated. Text and labels share one sequence, 1,024 tokens by default, with 25 labels by default (17 of 25). The caller sets a threshold, picks single-label or multi-label, and can ask for hierarchical output. The pipeline takes a batch of texts and a chunking pipeline handles long documents. The HTTP endpoint takes one text a request (15 of 20). Errors are `{\"error\": \"...\"}` with 400 or 404 and are undocumented. Truncation of long input is silent (7 of 20). Classification calls are stateless and safe to retry. The server has a request timeout and a queue capacity in its configuration. We found no `Retry-After` or retry guidance in the serve package or the docs (12 of 20). Loading takes a model, a tokeniser and a pipeline call, and a Python client ships with the server. Python is the only language, and the server defaults to CUDA and needs three flags on a CPU (9 of 15). The 0.1.22 `/v1/process` errors are actionable in the code (codes such as INPUT_TOO_LONG with a JSON path) but undocumented, so the error line holds." }, "scores": { "a": 85, "b": 60 } }, { "by": 15, "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 Kev, Vela 2.0, Decider and the other decision models listed as models. The repository tagged v0.1.22 on 31 October 2026 (FP8 serving and a new process API), after v0.1.21 on 16 October (calibration and multimodal training), and the v4 checkpoints were last changed on 24 October (30). No deprecation policy or notice period. Each generation lives in its own Hub repository (v1.0, v2.0, v3.0, Instruct, Multilang, v4) and none has been removed or replaced in place; the v3.0 repositories last changed on 12 August 2025 (5 of 12, level with Decider's and Kev's 5). Four v4 models (edge, 2B, 9B and 27B) arrived from 13 to 24 October 2026 as new repositories, with the earlier ones untouched (5 of 8). 34 commits from v0.1.20 to v0.1.22 under six author names, six of them merges of pull requests (#48 to #53). Issue reply times weren't checked, and the open-issue count is per the 8 October check, not re-read on 4 November. A Discord server is linked (10 of 25). The Python package is current and runs on transformers 5. No other SDK (10 of 15). CI and a `uv.lock` exist, and PyPI publishing is automated on tags, but the Tests workflow was failing on main, per the 8 October check, not re-read on 4 November (4 of 10). 30+5+5+10+10+4 = 64." }, "scores": { "a": 79, "b": 64 } }, { "by": 7, "category": "security", "leads": "gemini-decision-1", "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 Kev, Vela 2.0 and the other decision models listed as models. No account. The Ray Serve deployment checks no credential and has no option for one in 0.1.22, and the command line still binds to 0.0.0.0 by default (6 of 30). Self-hosted, so nothing reaches Knowledgator and no caller data can train a model, and the package has no telemetry code, but no project page says so, where Kev's cards and Vela 2.0's runtime reference state it for their 18 (15 of 20, level with Strands Decider and Granite Switch). The output is labels and scores, so no untrusted text comes back. The docs warn that the safety checkpoints can miss new or obfuscated attacks. Nothing documents how hostile text moves a score (10 of 15). The server logs start-up and label truncation and keeps request statistics, and the docs describe no per-call record (3 of 15). No SECURITY.md, no security.txt on knowledgator.com (404 per the 8 October check, not re-read on 4 November) and no bounty. Weights are safetensors, though the v4 2B repository also holds a pickled `training_args.bin` beside them. PyPI releases use trusted publishing. `gliclass/config.py` still loads an unknown encoder's configuration with `trust_remote_code=True` (4 of 20). 6+15+10+3+4 = 38." }, "scores": { "a": 45, "b": 38 } }, { "by": 5, "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 library and the weights, with `train.py`, the training datasets named on the card and an arXiv paper (30). Self-hosted, so inputs stay on the owner's hardware, though no project page says so. The company's terms of use and privacy policy of 21 September 2023 (terms re-read on 4 November) covers its website and services, not the library (18 of 30). No deprecation policy. Older model generations stay on the Hub without a notice of which are current (2 of 20). We found no telemetry code in the package and no statement either way. Weights download from the Hugging Face Hub (10 of 20)." }, "scores": { "a": 70, "b": 65 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Google", "b": "Knowledgator", "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 (library and the model weights we checked)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-11", "b": "2026-10-31", "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": "555 stars", "name": "Popularity" } ], "faq": [ { "answer": "Gemini Decision 1 scores 63.2 (B) on agent readiness against GLiClass's 55.5 (C), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Gemini Decision 1 or GLiClass?" }, { "answer": "Gemini Decision 1, at free against free for GLiClass. These are the vendors' published prices for the job.", "question": "Which is cheaper for decision models, Gemini Decision 1 or GLiClass?" }, { "answer": "Gemini Decision 1 needs an API key. GLiClass needs no key.", "question": "Do Gemini Decision 1 and GLiClass need an API key?" }, { "answer": "No hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for GLiClass.", "question": "Can an agent call Gemini Decision 1 and GLiClass without installing anything?" }, { "answer": "No open-source release is listed for Gemini Decision 1. GLiClass is open source (Apache-2.0 (library and the model weights we checked)).", "question": "Are Gemini Decision 1 and GLiClass open source?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 80 against 49", "Agent ergonomics, 85 against 60", "Security \u0026 auth, 45 against 38", "Maintenance \u0026 community, 79 against 64", "Transparency \u0026 trust, 70 against 65" ], "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": [ "Payments \u0026 pricing, 90 against 60" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.", "slug": "gliclass", "watchFor": "No calibration figures on the v3.0 cards, which report F1 only; the v4 2B and 27B repositories hold calibration report files we couldn't read" } ], "history": { "gemini-decision-1": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 2.5, "from": 60.7, "fromGrade": "C", "to": 63.2, "toGrade": "B" } ], "gliclass": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 3.7, "from": 51.8, "fromGrade": "D", "to": 55.5, "toGrade": "C" }, { "causes": [ "correction" ], "date": "2026-11-04", "delta": 1.8, "from": 50, "fromGrade": "D", "to": 51.8, "toGrade": "D" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.2, "from": 50.2, "fromGrade": "D", "to": 50, "toGrade": "D" } ] }, "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-gliclass.json", "title": "Celeris-1 Decision vs GLiClass", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass" }, { "json": "https://www.anchorterminal.com/compare/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-gliclass.json", "title": "Clef vs GLiClass", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass" }, { "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-gliclass.json", "title": "Laya vs GLiClass", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-gliclass" }, { "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-gliclass.json", "title": "Decider vs GLiClass", "url": "https://www.anchorterminal.com/compare/decider-vs-gliclass" }, { "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-gliclass.json", "title": "Decision 3.0 vs GLiClass", "url": "https://www.anchorterminal.com/compare/decision-3-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-jaredpalmer-kev.json", "title": "Gemini Decision 1 vs Kev", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev" }, { "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-granite-switch.json", "title": "GLiClass vs Granite Switch 4.1", "url": "https://www.anchorterminal.com/compare/gliclass-vs-granite-switch" }, { "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/gliclass-vs-liquid-d1.json", "title": "GLiClass vs Liquid d1", "url": "https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1.json", "title": "GLiClass vs Microsoft-Decision-1", "url": "https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-nace-drex.json", "title": "GLiClass vs Drex 1.5", "url": "https://www.anchorterminal.com/compare/gliclass-vs-nace-drex" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.json", "title": "GLiClass vs OpenAI Decisions API", "url": "https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider.json", "title": "GLiClass vs pplx-decider", "url": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash.json", "title": "GLiClass vs pplx-decider-flash", "url": "https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-rightnow-system-zero.json", "title": "GLiClass vs RightNow System Zero", "url": "https://www.anchorterminal.com/compare/gliclass-vs-rightnow-system-zero" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-scaledown.json", "title": "GLiClass vs ScaleDown", "url": "https://www.anchorterminal.com/compare/gliclass-vs-scaledown" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev.json", "title": "GLiClass vs SPEV", "url": "https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.json", "title": "GLiClass vs kettei", "url": "https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.json", "title": "GLiClass vs Strands Decider 2B", "url": "https://www.anchorterminal.com/compare/gliclass-vs-strands-decider" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.json", "title": "GLiClass vs Jev", "url": "https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev" }, { "json": "https://www.anchorterminal.com/compare/gliclass-vs-vela.json", "title": "GLiClass vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/gliclass-vs-vela" } ], "panel": null, "scores": [ { "by": 3, "edge": "gliclass", "gemini-decision-1": 40, "gliclass": 43, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 31, "edge": "gemini-decision-1", "gemini-decision-1": 80, "gliclass": 49, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 25, "edge": "gemini-decision-1", "gemini-decision-1": 85, "gliclass": 60, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 7, "edge": "gemini-decision-1", "gemini-decision-1": 45, "gliclass": 38, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "edge": "gliclass", "gemini-decision-1": 60, "gliclass": 90, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 15, "edge": "gemini-decision-1", "gemini-decision-1": 79, "gliclass": 64, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 5, "edge": "gemini-decision-1", "gemini-decision-1": 70, "gliclass": 65, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini Decision 1 scores 63.2 (B) on agent readiness against GLiClass's 55.5 (C), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing. Both do decision models. Gemini Decision 1 is cheaper for decision models, $0 against $0 per 1M tokens.", "tasks": null, "tutorials": [], "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.", "gliclass": "An Apache-2.0 classifier family that scores a whole label set in one pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes, and new v4 models from 0.4B to 27B parameters. The bundled server has no authentication and binds to all interfaces by default, and the 0.1.22 process API is undocumented." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass", "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.min.md" }, "markdown": "Gemini Decision 1 scores 63.2 (B) on agent readiness against GLiClass's 55.5 (C), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing. 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- GLiClass: grade C, 55.5/100, rank #959 of 1524. Markdown https://www.anchorterminal.com/tools/gliclass.md · JSON https://www.anchorterminal.com/api/v1/tools/gliclass.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- Schema \u0026 documentation, 80 against 49\n- Agent ergonomics, 85 against 60\n- Security \u0026 auth, 45 against 38\n- Maintenance \u0026 community, 79 against 64\n- Transparency \u0026 trust, 70 against 65\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### GLiClass (C)\n\nGood for: Topic, intent and sentiment routing over a known label set on the owner's own CPU or GPU, where many labels must be scored at once.\n\nAhead on:\n- Payments \u0026 pricing, 90 against 60\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: No calibration figures on the v3.0 cards, which report F1 only; the v4 2B and 27B repositories hold calibration report files we couldn't read\n\n\n## Why the edge\n\n### Schema \u0026 documentation: Gemini Decision 1 +31\n\n- Gemini Decision 1 (80): 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 published machine-readable contract for the Decisions endpoint; the request and response types are generated and typed in google-genai 3.2.0, Apache-2.0 (10 of 25). llms.txt with Markdown twins on the platform (10). The guide and model page say what the endpoint is for and list what it doesn't do (no caching, Batch, Flex, Priority, function calling, structured outputs or text generation; no YouTube or gs:// sources; history only as text) (18 of 20). Fields carry stated rules: question keys match [A-Za-z0-9_-]+ up to 64 characters, 1 to 1,000 questions, 2 to 255 options or levels, option keys 1 to 128 characters, P(true) between 0.01 and 0.99, nesting to 100 levels (14 of 15). Python, JavaScript and REST examples, and errors documented as HTTP 400 with codes safety, prohibited_content, blocklist, image_safety and content_blocked (13 of 15). Dated SDK changelog and the platform's dated changelog (15). 10 + 10 + 18 + 14 + 13 + 15 = 80.\n- GLiClass (49): Read for a model the owner serves. No OpenAPI file. The serving page documents the seven request fields of `POST /gliclass` and the response array in a table, and the pipeline's Python signatures carry type hints (10 of 25). docs.knowledgator.com has no llms.txt (404). The README, `docs/streaming.md` and the model cards are Markdown (5 of 10). The cards and docs name the intended uses (topic, sentiment, intent, natural language inference, reranking) and print per-dataset F1 that shows the weak cases, such as 0.3376 on sst5 for base v3.0. The base card has no limitations section and says nothing on calibration (11 of 20). Labels are free strings or a dict of groups, the mode is one of two values and `threshold` is a number. The HTTP handler reads fields from the body without validation, and a list in `texts` is cut to its first item (8 of 15). Examples for every mode in the docs. The handler's 400 and 404 answers are not documented, and a request without `labels` gets a 404 (8 of 15). Versions on PyPI and version numbers in model names, with three one-line GitHub release notes and no changelog (7 of 15). Version 0.1.22 adds a second HTTP interface under `/v1/` (process, batch, models, capabilities, health) with coded errors carrying an HTTP status and a JSON path, in the code only; neither the docs site's serving page nor the README describes it on 4 November, so no line moves.\n\n### Payments \u0026 pricing: GLiClass +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- GLiClass (90): Free Apache-2.0 software with nothing to buy, so 20 + 20 + 20 for pricing, free use and no sign-up under the self-hosted rule. No payment protocol (0). Knowledgator's hosted platform and its plans were unreachable from our network on 8 October 2026 and are not graded here. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 60 is 90.\n\n### Agent ergonomics: Gemini Decision 1 +25\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- GLiClass (60): Read as an API an agent calls for a decision, as with the other decision models. The answer is a short list of labels and scores and no text is generated. Text and labels share one sequence, 1,024 tokens by default, with 25 labels by default (17 of 25). The caller sets a threshold, picks single-label or multi-label, and can ask for hierarchical output. The pipeline takes a batch of texts and a chunking pipeline handles long documents. The HTTP endpoint takes one text a request (15 of 20). Errors are `{\"error\": \"...\"}` with 400 or 404 and are undocumented. Truncation of long input is silent (7 of 20). Classification calls are stateless and safe to retry. The server has a request timeout and a queue capacity in its configuration. We found no `Retry-After` or retry guidance in the serve package or the docs (12 of 20). Loading takes a model, a tokeniser and a pipeline call, and a Python client ships with the server. Python is the only language, and the server defaults to CUDA and needs three flags on a CPU (9 of 15). The 0.1.22 `/v1/process` errors are actionable in the code (codes such as INPUT_TOO_LONG with a JSON path) but undocumented, so the error line holds.\n\n### Maintenance \u0026 community: Gemini Decision 1 +15\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- GLiClass (64): Read as a model, for deprecation notice and model churn rather than release counts, on the lines used for Kev, Vela 2.0, Decider and the other decision models listed as models. The repository tagged v0.1.22 on 31 October 2026 (FP8 serving and a new process API), after v0.1.21 on 16 October (calibration and multimodal training), and the v4 checkpoints were last changed on 24 October (30). No deprecation policy or notice period. Each generation lives in its own Hub repository (v1.0, v2.0, v3.0, Instruct, Multilang, v4) and none has been removed or replaced in place; the v3.0 repositories last changed on 12 August 2025 (5 of 12, level with Decider's and Kev's 5). Four v4 models (edge, 2B, 9B and 27B) arrived from 13 to 24 October 2026 as new repositories, with the earlier ones untouched (5 of 8). 34 commits from v0.1.20 to v0.1.22 under six author names, six of them merges of pull requests (#48 to #53). Issue reply times weren't checked, and the open-issue count is per the 8 October check, not re-read on 4 November. A Discord server is linked (10 of 25). The Python package is current and runs on transformers 5. No other SDK (10 of 15). CI and a `uv.lock` exist, and PyPI publishing is automated on tags, but the Tests workflow was failing on main, per the 8 October check, not re-read on 4 November (4 of 10). 30+5+5+10+10+4 = 64.\n\n### Security \u0026 auth: Gemini Decision 1 +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- GLiClass (38): Read as a model, for retention, training and zero-retention options in place of read-only modes, on the lines used for Kev, Vela 2.0 and the other decision models listed as models. No account. The Ray Serve deployment checks no credential and has no option for one in 0.1.22, and the command line still binds to 0.0.0.0 by default (6 of 30). Self-hosted, so nothing reaches Knowledgator and no caller data can train a model, and the package has no telemetry code, but no project page says so, where Kev's cards and Vela 2.0's runtime reference state it for their 18 (15 of 20, level with Strands Decider and Granite Switch). The output is labels and scores, so no untrusted text comes back. The docs warn that the safety checkpoints can miss new or obfuscated attacks. Nothing documents how hostile text moves a score (10 of 15). The server logs start-up and label truncation and keeps request statistics, and the docs describe no per-call record (3 of 15). No SECURITY.md, no security.txt on knowledgator.com (404 per the 8 October check, not re-read on 4 November) and no bounty. Weights are safetensors, though the v4 2B repository also holds a pickled `training_args.bin` beside them. PyPI releases use trusted publishing. `gliclass/config.py` still loads an unknown encoder's configuration with `trust_remote_code=True` (4 of 20). 6+15+10+3+4 = 38.\n\n### Transparency \u0026 trust: Gemini Decision 1 +5\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- GLiClass (65): Apache-2.0 for the library and the weights, with `train.py`, the training datasets named on the card and an arXiv paper (30). Self-hosted, so inputs stay on the owner's hardware, though no project page says so. The company's terms of use and privacy policy of 21 September 2023 (terms re-read on 4 November) covers its website and services, not the library (18 of 30). No deprecation policy. Older model generations stay on the Hub without a notice of which are current (2 of 20). We found no telemetry code in the package and no statement either way. Weights download from the Hugging Face Hub (10 of 20).\n\n## Score by category\n\n| Category | Weight | Gemini Decision 1 | GLiClass | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 43 | GLiClass +3 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 49 | Gemini Decision 1 +31 |\n| Agent ergonomics | 13% (16.2 this run) | 85 | 60 | Gemini Decision 1 +25 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 38 | Gemini Decision 1 +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 90 | GLiClass +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 79 | 64 | Gemini Decision 1 +15 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 65 | Gemini Decision 1 +5 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **55.5 · C** | |\n\n## Facts side by side\n\n| Fact | Gemini Decision 1 | GLiClass |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Google | Knowledgator |\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 (library and the model weights we checked) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-11 | 2026-10-31 |\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| Popularity | none | 555 stars |\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### GLiClass\n\n- +3.7 on 13 November 2026 (D to C): checklist\n- +1.8 on 4 November 2026: correction\n- -0.2 on 28 October 2026: correction\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**GLiClass.** An Apache-2.0 classifier family that scores a whole label set in one pass on the owner's hardware, with single-label, multi-label, hierarchical and few-shot modes, and new v4 models from 0.4B to 27B parameters. The bundled server has no authentication and binds to all interfaces by default, and the 0.1.22 process API is undocumented.\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### GLiClass\n\n1. Pass `--host 127.0.0.1` to `python -m gliclass.serve`, or put the port behind your own gateway. The server checks no credential\n2. On a machine without a GPU add `--device cpu --dtype float32 --num-gpus-per-replica 0`. The default configuration expects CUDA\n3. Send one text a request to `POST /gliclass`. An array in `texts` is cut to its first item without an error\n4. Set `multi_label` to false for one label from a set. The default scores each label independently, so scores do not sum to 1\n5. Keep text plus labels under the pipeline's 1,024-token `max_length`, or use `ZeroShotClassificationWithChunkingPipeline`. Longer input is truncated silently\n6. The `/v1/process` interface in 0.1.22 isn't documented. Read `gliclass/serve/api` in the release you install before relying on its fields or error codes\n\n## Questions\n\n### Which is better for AI agents, Gemini Decision 1 or GLiClass?\n\nGemini Decision 1 scores 63.2 (B) on agent readiness against GLiClass's 55.5 (C), and leads in 5 of 7 scored categories. GLiClass leads on payments \u0026 pricing.\n\n### Which is cheaper for decision models, Gemini Decision 1 or GLiClass?\n\nGemini Decision 1, at free against free for GLiClass. These are the vendors' published prices for the job.\n\n### Do Gemini Decision 1 and GLiClass need an API key?\n\nGemini Decision 1 needs an API key. GLiClass needs no key.\n\n### Can an agent call Gemini Decision 1 and GLiClass without installing anything?\n\nNo hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for GLiClass.\n\n### Are Gemini Decision 1 and GLiClass open source?\n\nNo open-source release is listed for Gemini Decision 1. GLiClass is open source (Apache-2.0 (library and the model weights we checked)).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-decision-1\", \"b\": \"gliclass\"}`. From a terminal: `anchor compare gemini-decision-1 gliclass`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-decision-1.json and https://www.anchorterminal.com/api/v1/tools/gliclass.json\n\n## Other comparisons with Gemini Decision 1 or GLiClass\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 GLiClass](https://www.anchorterminal.com/compare/celeris-1-decision-vs-gliclass.md)\n- [Clef vs Gemini Decision 1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gemini-decision-1.md)\n- [Clef vs GLiClass](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gliclass.md)\n- [Laya vs Gemini Decision 1](https://www.anchorterminal.com/compare/convai-laya-vs-gemini-decision-1.md)\n- [Laya vs GLiClass](https://www.anchorterminal.com/compare/convai-laya-vs-gliclass.md)\n- [Decider vs Gemini Decision 1](https://www.anchorterminal.com/compare/decider-vs-gemini-decision-1.md)\n- [Decider vs GLiClass](https://www.anchorterminal.com/compare/decider-vs-gliclass.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 GLiClass](https://www.anchorterminal.com/compare/decision-3-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 Kev](https://www.anchorterminal.com/compare/gemini-decision-1-vs-jaredpalmer-kev.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 Granite Switch 4.1](https://www.anchorterminal.com/compare/gliclass-vs-granite-switch.md)\n- [GLiClass vs Kev](https://www.anchorterminal.com/compare/gliclass-vs-jaredpalmer-kev.md)\n- [GLiClass vs Liquid d1](https://www.anchorterminal.com/compare/gliclass-vs-liquid-d1.md)\n- [GLiClass vs Microsoft-Decision-1](https://www.anchorterminal.com/compare/gliclass-vs-microsoft-decision-1.md)\n- [GLiClass vs Drex 1.5](https://www.anchorterminal.com/compare/gliclass-vs-nace-drex.md)\n- [GLiClass vs OpenAI Decisions API](https://www.anchorterminal.com/compare/gliclass-vs-openai-decisions-api.md)\n- [GLiClass vs pplx-decider](https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider.md)\n- [GLiClass vs pplx-decider-flash](https://www.anchorterminal.com/compare/gliclass-vs-pplx-decider-flash.md)\n- [GLiClass vs RightNow System Zero](https://www.anchorterminal.com/compare/gliclass-vs-rightnow-system-zero.md)\n- [GLiClass vs ScaleDown](https://www.anchorterminal.com/compare/gliclass-vs-scaledown.md)\n- [GLiClass vs SPEV](https://www.anchorterminal.com/compare/gliclass-vs-sprag-spev.md)\n- [GLiClass vs kettei](https://www.anchorterminal.com/compare/gliclass-vs-sqwish-kettei.md)\n- [GLiClass vs Strands Decider 2B](https://www.anchorterminal.com/compare/gliclass-vs-strands-decider.md)\n- [GLiClass vs Jev](https://www.anchorterminal.com/compare/gliclass-vs-typesafe-jev.md)\n- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-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 GLiClass", "url": "" } ], "description": "Gemini Decision 1 scores 63.2 (B) to GLiClass's 55.5 (C) 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", "GLiClass C 55.5", "scores" ], "h1": "Gemini Decision 1 vs GLiClass", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gemini-decision-1-vs-gliclass", "published": "2026-10-01", "section": "tools", "title": "Gemini Decision 1 vs GLiClass for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-gliclass" }, "tokens": { "markdown": 6200, "slim": 1130 }, "version": 1 }