{ "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": "Vela 2.0 scores 70.4 (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": "vela", "name": "Vela 2.0", "vendor": "vLLM Semantic Router project (with KR Labs)", "vendorUrl": "https://vllm-sr.ai", "kind": "model", "category": "decision-models", "summary": "Vela 2.0 is a family of four open-weight decision models from the vLLM Semantic Router project and KR Labs, released on 6 October 2026 under Apache-2.0 for routing, safety checks, personal-data spans and hallucination checks.", "url": "https://www.anchorterminal.com/tools/vela", "markdownUrl": "https://www.anchorterminal.com/tools/vela.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vela.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vela.json", "dns": "vela.grade.anchorterminal.com", "repo": "https://github.com/vllm-project/semantic-router", "license": "Apache-2.0 (weights, code and documentation). The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data keeps its own licences", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "vllm-sr" } ], "auth": "none", "authNotes": "No account. The weights are public and ungated on Hugging Face. The bundled `vela2_serve.py` binds to 127.0.0.1 and ignores the Authorization header unless `VELA2_API_KEY` is set, after which it requires `Authorization: Bearer \u003ckey\u003e` and answers 401 otherwise. The router's model runtime has no authentication and publishes on 127.0.0.1 unless `--host` is passed.", "pricing": "free", "pricingNotes": "Free and open source, with nothing to buy and no hosted API. Hardware is the owner's cost. The 0.3B runs on a CPU, and the cards put GPU parameter memory at about 17 GB for the 4B. The router docs list about 32 GB for the 9B (checked 2026-10-08).", "priceSummary": "Free · OSS", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Vela 2.0 is software the owner runs, and neither server has a payment route (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 6054, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://huggingface.co/collections/vllm-sr/vela-20", "openapi": "https://raw.githubusercontent.com/vllm-project/semantic-router/main/src/model-runtime/vllm_srun/api/openapi.yaml", "capabilities": [ "inference.decision", "guard.pii", "guard.injection", "guard.moderation", "guard.self-host" ], "tags": [ "model", "open-source", "open-weights", "self-hosted", "local", "free", "python", "openapi" ], "lastRelease": "2026-10-06", "graded": true, "anchor": { "graded": true, "score": 70.4, "grade": "BB", "agentReady": true, "rank": 294, "ranked": true, "rankOf": 1524, "categoryRank": 7, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 79, "maintenance": 74, "payments": 90, "reliability": 57, "schema": 78, "security": 63, "transparency": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "One self-hosted call answers routing, prompt-attack, personal-data and unsupported-claim questions with probabilities and character offsets, under Apache-2.0 with SHA-256 manifests. The Hub repositories carry no version tags, the authors' own figures put the larger sizes below their Decision 2.0 bases, and Vela 3.0 replaced the 0.3B as the router's default on 28 October 2026.", "bestFor": "Self-hosted routing and guardrail checks in one call, where span offsets for personal data or unsupported claims matter.", "strengths": [ "Five question types in one request (choice, noul, score, set and span), with span answers as labelled character offsets and a probability each", "Apache-2.0 weights, code and documentation, ungated on Hugging Face, with safetensors files and a SHA256SUMS manifest in the three decoder repositories", "Two serving routes. A bundled FastAPI server on `POST /v1/systemone`, and the router's model runtime with an OpenAPI 3.0.3 contract and Prometheus metrics", "The model cards disclose evaluation protocol, including that the 0.3B release selection considered test results and that SQuAD v2 isn't zero-shot", "The router's release note lists where the 0.3B default is behind Vela 1.0, with numbers, and how to restore each Vela 1.0 model" ], "weaknesses": [ "No version tags on the four Hub repositories, and the 4B and 9B weights were replaced in place on 3 October 2026", "Loading with `transformers` needs `trust_remote_code=True`, which runs Python from the model repository", "The router's `vllm-sr serve MODEL` engine mode is newer than the 0.4.0 stable release and needs the development channel", "By the authors' figures the 4B scores 31.63 on the Jev Decision Index 0.2.1 against 42.55 for its Decision 2.0 base", "The model runtime has no authentication, and the bundled server is open unless `VELA2_API_KEY` is set", "Vela 3.0, launched on 26 October 2026, replaced Vela 2.0 0.3B as the router's default decision model on 28 October" ], "agentNotes": [ "Pin a commit hash with `revision=` when loading from the Hub. The repositories have no tags, and `main` changed after launch", "Send the served name in `model`, for example `vllm-sr/Vela-2.0-4B`. The bundled server answers 422 to any other name", "Name span questions `pii`, `halu` or `toxic`, or set `\"head\": \"router\"`, to get the trained router head. Other labels go to the broad head", "Keep input under 16,384 tokens a sequence (8,192 on the 0.3B). The bundled server answers 413 when the questions alone don't fit", "Set `VELA2_API_KEY` before binding the bundled server beyond 127.0.0.1, and keep the model runtime on a trusted network", "Name `vllm-sr/Vela-2.0-0.3B` as the `primary` deployment's artifact to keep Vela 2.0. Router builds since 28 October default to vela3-fast" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 4.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 70.4 } ], "editorialScores": { "ergonomics": 79, "maintenance": 74, "payments": 90, "reliability": 57, "schema": 78, "security": 63, "transparency": 68 }, "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": "pip install torch \"transformers\u003e=5.17\" safetensors tokenizers numpy fastapi uvicorn\n# from a local snapshot of vllm-sr/Vela-2.0-4B\npython vela2_serve.py --model . --device cuda --port 8001", "http": "curl -s localhost:8001/v1/systemone -H 'content-type: application/json' \\\n -d '{\"model\":\"vllm-sr/Vela-2.0-4B\",\"state\":\"My card was charged twice and the parcel never arrived.\",\"questions\":{\"issues\":{\"type\":\"set\",\"instructions\":\"Which issues does the customer report?\",\"criteria\":{\"billing\":\"payments, charges, refunds or invoices\",\"shipping\":\"delivery of an order or a parcel\",\"login\":\"signing in, passwords or account access\"}}}}'" }, "letme": { "capability": "https://letme.dev/inference.decision", "tool": "https://letme.dev/vela" }, "sameCompany": [ "decision-3" ], "area": "models", "provenance": { "legalEntity": "", "domain": "vllm-sr.ai", "domainRegistered": "2026-07-13", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://vllm-sr.ai/docs/release-notes/vela-2-0-built-in-signals", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "An open-source project with no company named as publisher. The site footer reads vLLM Semantic Router Team, and the model cards credit KR Labs and vLLM Semantic Router.", "RDAP gives 13 July 2026 as the registration date of vllm-sr.ai.", "Software the owner runs, so there's no hosted endpoint, terms or privacy policy. The Apache-2.0 licence stands in for terms.", "vllm-sr.ai/.well-known/security.txt returns 404. SECURITY.md in the repository takes private reports through GitHub Security Advisories.", "The weights are on huggingface.co under the vllm-sr organisation, and the code for the router and its model runtime is at github.com/vllm-project/semantic-router." ], "score": 39 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vela.json", "live": { "slug": "vela", "versions": [ { "registry": "github", "name": "vllm-project/semantic-router", "version": "v0.4.0", "released": "2026-09-27", "seenAt": "2026-11-13T09:24:57.004610117Z" }, { "registry": "pypi", "name": "vllm-sr", "version": "0.4.0", "released": "2026-09-27", "seenAt": "2026-11-13T09:24:56.883324152Z" } ], "githubStars": 6464, "pypiWeekly": 4403, "securityTxt": { "url": "https://vllm-sr.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-13T16:15:28.998816012Z" }, "domain": { "domain": "vllm-sr.ai", "registered": "2026-07-13", "source": "https://rdap.identitydigital.services/rdap/domain/vllm-sr.ai", "checkedAt": "2026-11-08T15:09:18.410283496Z" }, "pages": [ { "url": "https://vllm-sr.ai/docs/release-notes/vela-2-0-built-in-signals", "kind": "changelog", "status": 200, "checkedAt": "2026-11-13T18:34:06.824905345Z", "changedAt": "2026-11-11T17:14:24.082152875Z", "fingerprint": "d243cdee83a8" } ], "github": { "repo": "vllm-project/semantic-router", "checkedAt": "2026-11-09T11:12:05.466756487Z", "stars": 6429, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-09T09:50:16Z", "openIssues": 365, "openPRs": 257, "issuesOpened90d": 1389, "issuesClosed90d": 1243, "prsMerged90d": 1206, "ci": { "branch": "main", "runs": 50, "success": 49, "failure": 1, "other": 0, "latest": "success", "latestAt": "2026-11-09T10:18:15Z" }, "advisories": [ { "ghsa": "GHSA-xr66-pq9r-2c8j", "severity": "high", "published": "2026-10-18T16:38:40Z" } ], "replies": { "sampled": 8, "unanswered": 4, "medianDays": 1.3 } }, "updatedAt": "2026-11-13T18:34:06.824905345Z" } }, "edges": [ { "by": 30, "category": "payments", "leads": "vela", "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. No payment protocol (0). There is no hosted API. Hardware is the owner's cost. 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": 18, "category": "security", "leads": "vela", "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 and the other decision models listed as models. No account. The bundled `vela2_serve.py` binds to 127.0.0.1 and is open unless `VELA2_API_KEY` is set, after which it requires one bearer key, compared in constant time. The router's model runtime has no authentication, and its docs say to expose it only on a trusted network (13 of 30). Self-hosted, so nothing reaches the authors and no caller data can train a model. The model runtime reference says request text is never logged and metrics carry no request content. Nothing says what the router's other components keep (18 of 20, level with Kev). The models are trained to flag prompt attacks, each question sees only the state and its own block, and the router's SECURITY.md names adversarial input to classification models in its threat model. Nothing documents how hostile text in the state can move an answer (10 of 15). The runtime exposes Prometheus metrics and, on request, the revision, digest and timings that answered a call. The bundled server keeps no record of calls (9 of 15). SECURITY.md takes private reports through GitHub Security Advisories, with severity classes and a disclosure policy, re-read in the clone on 1 November. Weights are safetensors with a SHA256SUMS manifest in the decoder repositories, and the runtime checks every file's SHA-256 and never runs code from a model repository. The `transformers` route does run repository code through `trust_remote_code=True`, with no tag to pin. No bug bounty, and no security.txt on vllm-sr.ai (404 on 1 November) (13 of 20). 13 + 18 + 10 + 9 + 13 = 63." }, "scores": { "a": 45, "b": 63 } }, { "by": 17, "category": "reliability", "leads": "vela", "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 Vela 2.0 is a model the owner runs. There are two routes. The model cards load it with `pip install torch \"transformers\u003e=5.17\"` and `AutoModel.from_pretrained(..., trust_remote_code=True)`, with no Python version stated. The router's `vllm-sr` package on PyPI states Python 3.10 or newer, but serving a model with it is newer than the 0.4.0 stable release and needs the development channel (12 of 20). The semantic-router repository has public CI whose model-runtime suite includes five `test_vela2_*` files, and each model repository ships a parity record against the research scorer with zero decision differences on 230 rows. We couldn't read today's CI result on main, since the GitHub API was rate-limited and the Actions page we got was stale (15 of 25). The router repository showed 408 open issues on 8 October, 23 of them mentioning Vela, against 200 commits between 23 September and 8 October. The four Hub repositories have no discussions. Issue #4668 of 7 October tracks the 0.3B's CPU latency, which the release note puts at 79 ms against 16 ms for Vela 1.0 (14 of 25). The Hub repositories have no version tags, and the 4B and 9B weights were replaced in place on 3 October. The router pins the revisions it loads and publishes a dated release note (6 of 15). Released as 2.0 with no beta label on the cards, though the `vllm-sr` package is classified beta and the family is two days past launch (10 of 15). Re-read on 1 November from a clone. No stable release since 0.4.0 (27 September), the `vllm-sr` package on main is 0.5.0, still classified beta and needing Python 3.10 or newer, and the open issue count and CI results rest on the 8 October check, since GitHub's pages answered 403. 12 + 15 + 14 + 6 + 10 = 57." }, "scores": { "a": 40, "b": 57 } }, { "by": 16, "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 weights, code and documentation, on Apache-2.0 Decision 2.0 and Qwen3.5 bases, with MODIFICATIONS.md and ATTRIBUTIONS.md in each repository. The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data isn't redistributed and keeps its own licences, some CC BY-SA. We found no training code (26 of 30). Self-hosted, so inputs stay on the owner's hardware. The runtime docs say request text is never logged and metrics carry no request content. There's no privacy statement for the project (22 of 30). The router's support matrix defines a Deprecated class and says nothing is in it, and the release note gives the lines that restore each Vela 1.0 model. No policy with notice periods (10 of 20). We found no telemetry statement either way and didn't audit the runtime's code for it. Weights download from the Hugging Face Hub (10 of 20)." }, "scores": { "a": 70, "b": 54 } }, { "by": 6, "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, Clef and Kev. Answers are probabilities and spans, `output_tokens` is always 0, and the decoders read the state once and fork it per question. Input is 16,384 tokens a sequence, or 8,192 on the 0.3B (20 of 25). The caller fixes the output shape with any mix of the five types, a `threshold` per question, a `head` override for spans and `/v1/bundle` for several tasks in one call (18 of 20). The runtime answers with `{error: {code, message}}`, and a failed question carries its own code without failing the others. The bundled server answers 422 with `loc`, `msg` and `type`, and 413 for oversize questions (16 of 20). Calls are stateless and safe to retry. The runtime answers 429 `overloaded` past 256 queued requests a model. We found no `Retry-After` header in the runtime code and no retry guidance (15 of 20). `typesafe-sdk` works against the bundled server per USAGE.md, and loading is one `AutoModel` call. Setup needs `trust_remote_code`, about 17 GB of GPU memory for the 4B, or the router's development channel (10 of 15)." }, "scores": { "a": 85, "b": 79 } }, { "by": 5, "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, Decider, Laya and the other decision models listed as models. Vela 2.0 launched on 6 October 2026, and the four Hub repositories were last changed on 7 October (card edits only). 26 days on 1 November (30). No deprecation policy or notice period. The 4B and 9B weights were replaced in place on 3 October, the repositories have no tags, and the router pins the revisions it loads. When Vela 3.0's vela3-fast replaced Vela 2.0 0.3B as the router's default on 28 October (pull request #5512), the docs gave the line that restores the 0.3B the same day, and the support matrix still lists nothing as deprecated (5 of 12, level with Kev's and Decider's 5). No Vela 2.0 weights have changed since 3 October and pinned revisions still load, but Vela 3.0, six new models launched on 26 October, took over the router's default 22 days after Vela 2.0's launch (5 of 8, level with Laya's 5 for a default switch with a restore path). The router repository's 200 newest commits span 13 October to 1 November under 57 author names, and the four Hub repositories have no discussions. Issue reply times rest on the 8 October check, since GitHub's pages answered 403 (17 of 25). No SDK of its own. The cards point to `typesafe-sdk`, and the clone's publish workflow ships the `vllm-sr` CLI to PyPI on a stable and a development channel, with 0.4.0 the last stable tag. pypi.org didn't resolve from the shell on 1 November, and the 8 October reading of PyPI's JSON endpoint, a path its robots.txt disallows, is struck (10 of 15). The router repository runs CI, a security scan and a package check, with today's result on main unread (7 of 10). 30 + 5 + 5 + 17 + 10 + 7 = 74." }, "scores": { "a": 79, "b": 74 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Google", "b": "vLLM Semantic Router project (with KR Labs)", "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 (weights, code and documentation). The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data keeps its own licences", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-11", "b": "2026-10-06", "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": "6.1k stars", "name": "Popularity" }, { "a": "none", "b": "4.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Vela 2.0 scores 70.4 (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 Vela 2.0?" }, { "answer": "Gemini Decision 1, at free against free for Vela 2.0. These are the vendors' published prices for the job.", "question": "Which is cheaper for decision models, Gemini Decision 1 or Vela 2.0?" }, { "answer": "Gemini Decision 1 needs an API key. Vela 2.0 needs no key.", "question": "Do Gemini Decision 1 and Vela 2.0 need an API key?" }, { "answer": "No hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for Vela 2.0.", "question": "Can an agent call Gemini Decision 1 and Vela 2.0 without installing anything?" }, { "answer": "No open-source release is listed for Gemini Decision 1. Vela 2.0 is open source (Apache-2.0 (weights, code and documentation). The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data keeps its own licences).", "question": "Are Gemini Decision 1 and Vela 2.0 open source?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 85 against 79", "Maintenance \u0026 community, 79 against 74", "Transparency \u0026 trust, 70 against 54" ], "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, 57 against 40", "Security \u0026 auth, 63 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 routing and guardrail checks in one call, where span offsets for personal data or unsupported claims matter.", "slug": "vela", "watchFor": "No version tags on the four Hub repositories, and the 4B and 9B weights were replaced in place on 3 October 2026" } ], "history": { "gemini-decision-1": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 2.5, "from": 60.7, "fromGrade": "C", "to": 63.2, "toGrade": "B" } ], "vela": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 3.8, "from": 66.6, "fromGrade": "B", "to": 70.4, "toGrade": "BB" }, { "causes": [ "correction" ], "date": "2026-11-01", "delta": -0.4, "from": 67, "fromGrade": "B", "to": 66.6, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.5, "from": 66.5, "fromGrade": "B", "to": 67, "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-vela.json", "title": "Celeris-1 Decision vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/celeris-1-decision-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-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-vela.json", "title": "Clef vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/cloudflare-clef-vs-vela" }, { "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-vela.json", "title": "Laya vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/convai-laya-vs-vela" }, { "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-vela.json", "title": "Decider vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/decider-vs-vela" }, { "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-vela.json", "title": "Decision 3.0 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/decision-3-vs-vela" }, { "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-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/gliclass-vs-vela.json", "title": "GLiClass vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/gliclass-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/granite-switch-vs-vela.json", "title": "Granite Switch 4.1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/granite-switch-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela.json", "title": "Kev vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela.json", "title": "Liquid d1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/liquid-d1-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-vela.json", "title": "Microsoft-Decision-1 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/microsoft-decision-1-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/nace-drex-vs-vela.json", "title": "Drex 1.5 vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/nace-drex-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-vela.json", "title": "OpenAI Decisions API vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/openai-decisions-api-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/pplx-decider-flash-vs-vela.json", "title": "pplx-decider-flash vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/pplx-decider-flash-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/pplx-decider-vs-vela.json", "title": "pplx-decider vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/pplx-decider-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/rightnow-system-zero-vs-vela.json", "title": "RightNow System Zero vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/rightnow-system-zero-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/scaledown-vs-vela.json", "title": "ScaleDown vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/scaledown-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/sprag-spev-vs-vela.json", "title": "SPEV vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/sprag-spev-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela.json", "title": "kettei vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/strands-decider-vs-vela.json", "title": "Strands Decider 2B vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/strands-decider-vs-vela" }, { "json": "https://www.anchorterminal.com/compare/typesafe-jev-vs-vela.json", "title": "Jev vs Vela 2.0", "url": "https://www.anchorterminal.com/compare/typesafe-jev-vs-vela" } ], "panel": null, "scores": [ { "by": 17, "edge": "vela", "gemini-decision-1": 40, "key": "reliability", "name": "Reliability", "vela": 57, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 2, "edge": "gemini-decision-1", "gemini-decision-1": 80, "key": "schema", "name": "Schema \u0026 documentation", "vela": 78, "weight": 13 }, { "by": 6, "edge": "gemini-decision-1", "gemini-decision-1": 85, "key": "ergonomics", "name": "Agent ergonomics", "vela": 79, "weight": 13 }, { "by": 18, "edge": "vela", "gemini-decision-1": 45, "key": "security", "name": "Security \u0026 auth", "vela": 63, "weight": 14 }, { "by": 30, "edge": "vela", "gemini-decision-1": 60, "key": "payments", "name": "Payments \u0026 pricing", "vela": 90, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 5, "edge": "gemini-decision-1", "gemini-decision-1": 79, "key": "maintenance", "name": "Maintenance \u0026 community", "vela": 74, "weight": 7 }, { "by": 16, "edge": "gemini-decision-1", "gemini-decision-1": 70, "key": "transparency", "name": "Transparency \u0026 trust", "vela": 54, "weight": 7 } ], "summary": "Vela 2.0 scores 70.4 (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": "Find contact details in a message with Vela", "tool": "vela", "url": "https://www.anchorterminal.com/tutorials/vela/" } ], "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.", "vela": "One self-hosted call answers routing, prompt-attack, personal-data and unsupported-claim questions with probabilities and character offsets, under Apache-2.0 with SHA-256 manifests. The Hub repositories carry no version tags, the authors' own figures put the larger sizes below their Decision 2.0 bases, and Vela 3.0 replaced the 0.3B as the router's default on 28 October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela", "json": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.min.md" }, "markdown": "Vela 2.0 scores 70.4 (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- Vela 2.0: grade BB, 70.4/100, rank #294 of 1524. Markdown https://www.anchorterminal.com/tools/vela.md · JSON https://www.anchorterminal.com/api/v1/tools/vela.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 79\n- Maintenance \u0026 community, 79 against 74\n- Transparency \u0026 trust, 70 against 54\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### Vela 2.0 (BB)\n\nGood for: Self-hosted routing and guardrail checks in one call, where span offsets for personal data or unsupported claims matter.\n\nAhead on:\n- Reliability, 57 against 40\n- Security \u0026 auth, 63 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 version tags on the four Hub repositories, and the 4B and 9B weights were replaced in place on 3 October 2026\n\n\n## Why the edge\n\n### Payments \u0026 pricing: Vela 2.0 +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- Vela 2.0 (90): Free Apache-2.0 software with nothing to buy, so 20 + 20 + 20 for pricing, free use and no sign-up. No payment protocol (0). There is no hosted API. Hardware is the owner's cost. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 60 is 90.\n\n### Security \u0026 auth: Vela 2.0 +18\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- Vela 2.0 (63): Read as a model, for retention, training and zero-retention options in place of read-only modes, on the lines used for Kev and the other decision models listed as models. No account. The bundled `vela2_serve.py` binds to 127.0.0.1 and is open unless `VELA2_API_KEY` is set, after which it requires one bearer key, compared in constant time. The router's model runtime has no authentication, and its docs say to expose it only on a trusted network (13 of 30). Self-hosted, so nothing reaches the authors and no caller data can train a model. The model runtime reference says request text is never logged and metrics carry no request content. Nothing says what the router's other components keep (18 of 20, level with Kev). The models are trained to flag prompt attacks, each question sees only the state and its own block, and the router's SECURITY.md names adversarial input to classification models in its threat model. Nothing documents how hostile text in the state can move an answer (10 of 15). The runtime exposes Prometheus metrics and, on request, the revision, digest and timings that answered a call. The bundled server keeps no record of calls (9 of 15). SECURITY.md takes private reports through GitHub Security Advisories, with severity classes and a disclosure policy, re-read in the clone on 1 November. Weights are safetensors with a SHA256SUMS manifest in the decoder repositories, and the runtime checks every file's SHA-256 and never runs code from a model repository. The `transformers` route does run repository code through `trust_remote_code=True`, with no tag to pin. No bug bounty, and no security.txt on vllm-sr.ai (404 on 1 November) (13 of 20). 13 + 18 + 10 + 9 + 13 = 63.\n\n### Reliability: Vela 2.0 +17\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- Vela 2.0 (57): Scored on the local-package checklist, since Vela 2.0 is a model the owner runs. There are two routes. The model cards load it with `pip install torch \"transformers\u003e=5.17\"` and `AutoModel.from_pretrained(..., trust_remote_code=True)`, with no Python version stated. The router's `vllm-sr` package on PyPI states Python 3.10 or newer, but serving a model with it is newer than the 0.4.0 stable release and needs the development channel (12 of 20). The semantic-router repository has public CI whose model-runtime suite includes five `test_vela2_*` files, and each model repository ships a parity record against the research scorer with zero decision differences on 230 rows. We couldn't read today's CI result on main, since the GitHub API was rate-limited and the Actions page we got was stale (15 of 25). The router repository showed 408 open issues on 8 October, 23 of them mentioning Vela, against 200 commits between 23 September and 8 October. The four Hub repositories have no discussions. Issue #4668 of 7 October tracks the 0.3B's CPU latency, which the release note puts at 79 ms against 16 ms for Vela 1.0 (14 of 25). The Hub repositories have no version tags, and the 4B and 9B weights were replaced in place on 3 October. The router pins the revisions it loads and publishes a dated release note (6 of 15). Released as 2.0 with no beta label on the cards, though the `vllm-sr` package is classified beta and the family is two days past launch (10 of 15). Re-read on 1 November from a clone. No stable release since 0.4.0 (27 September), the `vllm-sr` package on main is 0.5.0, still classified beta and needing Python 3.10 or newer, and the open issue count and CI results rest on the 8 October check, since GitHub's pages answered 403. 12 + 15 + 14 + 6 + 10 = 57.\n\n### Transparency \u0026 trust: Gemini Decision 1 +16\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- Vela 2.0 (54): Apache-2.0 for the weights, code and documentation, on Apache-2.0 Decision 2.0 and Qwen3.5 bases, with MODIFICATIONS.md and ATTRIBUTIONS.md in each repository. The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data isn't redistributed and keeps its own licences, some CC BY-SA. We found no training code (26 of 30). Self-hosted, so inputs stay on the owner's hardware. The runtime docs say request text is never logged and metrics carry no request content. There's no privacy statement for the project (22 of 30). The router's support matrix defines a Deprecated class and says nothing is in it, and the release note gives the lines that restore each Vela 1.0 model. No policy with notice periods (10 of 20). We found no telemetry statement either way and didn't audit the runtime's code for it. Weights download from the Hugging Face Hub (10 of 20).\n\n### Agent ergonomics: Gemini Decision 1 +6\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- Vela 2.0 (79): Read as an API an agent calls for a decision, as with Jev, Clef and Kev. Answers are probabilities and spans, `output_tokens` is always 0, and the decoders read the state once and fork it per question. Input is 16,384 tokens a sequence, or 8,192 on the 0.3B (20 of 25). The caller fixes the output shape with any mix of the five types, a `threshold` per question, a `head` override for spans and `/v1/bundle` for several tasks in one call (18 of 20). The runtime answers with `{error: {code, message}}`, and a failed question carries its own code without failing the others. The bundled server answers 422 with `loc`, `msg` and `type`, and 413 for oversize questions (16 of 20). Calls are stateless and safe to retry. The runtime answers 429 `overloaded` past 256 queued requests a model. We found no `Retry-After` header in the runtime code and no retry guidance (15 of 20). `typesafe-sdk` works against the bundled server per USAGE.md, and loading is one `AutoModel` call. Setup needs `trust_remote_code`, about 17 GB of GPU memory for the 4B, or the router's development channel (10 of 15).\n\n### Maintenance \u0026 community: Gemini Decision 1 +5\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- Vela 2.0 (74): Read as a model, for deprecation notice and model churn rather than release counts, on the lines used for Kev, Decider, Laya and the other decision models listed as models. Vela 2.0 launched on 6 October 2026, and the four Hub repositories were last changed on 7 October (card edits only). 26 days on 1 November (30). No deprecation policy or notice period. The 4B and 9B weights were replaced in place on 3 October, the repositories have no tags, and the router pins the revisions it loads. When Vela 3.0's vela3-fast replaced Vela 2.0 0.3B as the router's default on 28 October (pull request #5512), the docs gave the line that restores the 0.3B the same day, and the support matrix still lists nothing as deprecated (5 of 12, level with Kev's and Decider's 5). No Vela 2.0 weights have changed since 3 October and pinned revisions still load, but Vela 3.0, six new models launched on 26 October, took over the router's default 22 days after Vela 2.0's launch (5 of 8, level with Laya's 5 for a default switch with a restore path). The router repository's 200 newest commits span 13 October to 1 November under 57 author names, and the four Hub repositories have no discussions. Issue reply times rest on the 8 October check, since GitHub's pages answered 403 (17 of 25). No SDK of its own. The cards point to `typesafe-sdk`, and the clone's publish workflow ships the `vllm-sr` CLI to PyPI on a stable and a development channel, with 0.4.0 the last stable tag. pypi.org didn't resolve from the shell on 1 November, and the 8 October reading of PyPI's JSON endpoint, a path its robots.txt disallows, is struck (10 of 15). The router repository runs CI, a security scan and a package check, with today's result on main unread (7 of 10). 30 + 5 + 5 + 17 + 10 + 7 = 74.\n\n## Score by category\n\n| Category | Weight | Gemini Decision 1 | Vela 2.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 57 | Vela 2.0 +17 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 78 | Gemini Decision 1 +2 |\n| Agent ergonomics | 13% (16.2 this run) | 85 | 79 | Gemini Decision 1 +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 63 | Vela 2.0 +18 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 90 | Vela 2.0 +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 79 | 74 | Gemini Decision 1 +5 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 70 | 54 | Gemini Decision 1 +16 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.2 · B** | **70.4 · BB** | |\n\n## Facts side by side\n\n| Fact | Gemini Decision 1 | Vela 2.0 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Google | vLLM Semantic Router project (with KR Labs) |\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 (weights, code and documentation). The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data keeps its own licences |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-11 | 2026-10-06 |\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 | 6.1k stars |\n| Agent reviews | none | 4.3/5 (4) |\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### Vela 2.0\n\n- +3.8 on 13 November 2026 (B to BB): checklist\n- -0.4 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**Vela 2.0.** One self-hosted call answers routing, prompt-attack, personal-data and unsupported-claim questions with probabilities and character offsets, under Apache-2.0 with SHA-256 manifests. The Hub repositories carry no version tags, the authors' own figures put the larger sizes below their Decision 2.0 bases, and Vela 3.0 replaced the 0.3B as the router's default on 28 October 2026.\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### Vela 2.0\n\n1. Pin a commit hash with `revision=` when loading from the Hub. The repositories have no tags, and `main` changed after launch\n2. Send the served name in `model`, for example `vllm-sr/Vela-2.0-4B`. The bundled server answers 422 to any other name\n3. Name span questions `pii`, `halu` or `toxic`, or set `\"head\": \"router\"`, to get the trained router head. Other labels go to the broad head\n4. Keep input under 16,384 tokens a sequence (8,192 on the 0.3B). The bundled server answers 413 when the questions alone don't fit\n5. Set `VELA2_API_KEY` before binding the bundled server beyond 127.0.0.1, and keep the model runtime on a trusted network\n6. Name `vllm-sr/Vela-2.0-0.3B` as the `primary` deployment's artifact to keep Vela 2.0. Router builds since 28 October default to vela3-fast\n\n## Tutorials\n\n- [Find contact details in a message with Vela](https://www.anchorterminal.com/tutorials/vela/), for Vela 2.0\n\n## Questions\n\n### Which is better for AI agents, Gemini Decision 1 or Vela 2.0?\n\nVela 2.0 scores 70.4 (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 Vela 2.0?\n\nGemini Decision 1, at free against free for Vela 2.0. These are the vendors' published prices for the job.\n\n### Do Gemini Decision 1 and Vela 2.0 need an API key?\n\nGemini Decision 1 needs an API key. Vela 2.0 needs no key.\n\n### Can an agent call Gemini Decision 1 and Vela 2.0 without installing anything?\n\nNo hosted endpoint is listed for Gemini Decision 1. No hosted endpoint is listed for Vela 2.0.\n\n### Are Gemini Decision 1 and Vela 2.0 open source?\n\nNo open-source release is listed for Gemini Decision 1. Vela 2.0 is open source (Apache-2.0 (weights, code and documentation). The 0.3B's tokeniser keeps the Gemma Terms of Use, and training data keeps its own licences).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-decision-1\", \"b\": \"vela\"}`. From a terminal: `anchor compare gemini-decision-1 vela`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-decision-1.json and https://www.anchorterminal.com/api/v1/tools/vela.json\n\n## Other comparisons with Gemini Decision 1 or Vela 2.0\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 Vela 2.0](https://www.anchorterminal.com/compare/celeris-1-decision-vs-vela.md)\n- [Clef vs Gemini Decision 1](https://www.anchorterminal.com/compare/cloudflare-clef-vs-gemini-decision-1.md)\n- [Clef vs Vela 2.0](https://www.anchorterminal.com/compare/cloudflare-clef-vs-vela.md)\n- [Laya vs Gemini Decision 1](https://www.anchorterminal.com/compare/convai-laya-vs-gemini-decision-1.md)\n- [Laya vs Vela 2.0](https://www.anchorterminal.com/compare/convai-laya-vs-vela.md)\n- [Decider vs Gemini Decision 1](https://www.anchorterminal.com/compare/decider-vs-gemini-decision-1.md)\n- [Decider vs Vela 2.0](https://www.anchorterminal.com/compare/decider-vs-vela.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 Vela 2.0](https://www.anchorterminal.com/compare/decision-3-vs-vela.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 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- [GLiClass vs Vela 2.0](https://www.anchorterminal.com/compare/gliclass-vs-vela.md)\n- [Granite Switch 4.1 vs Vela 2.0](https://www.anchorterminal.com/compare/granite-switch-vs-vela.md)\n- [Kev vs Vela 2.0](https://www.anchorterminal.com/compare/jaredpalmer-kev-vs-vela.md)\n- [Liquid d1 vs Vela 2.0](https://www.anchorterminal.com/compare/liquid-d1-vs-vela.md)\n- [Microsoft-Decision-1 vs Vela 2.0](https://www.anchorterminal.com/compare/microsoft-decision-1-vs-vela.md)\n- [Drex 1.5 vs Vela 2.0](https://www.anchorterminal.com/compare/nace-drex-vs-vela.md)\n- [OpenAI Decisions API vs Vela 2.0](https://www.anchorterminal.com/compare/openai-decisions-api-vs-vela.md)\n- [pplx-decider-flash vs Vela 2.0](https://www.anchorterminal.com/compare/pplx-decider-flash-vs-vela.md)\n- [pplx-decider vs Vela 2.0](https://www.anchorterminal.com/compare/pplx-decider-vs-vela.md)\n- [RightNow System Zero vs Vela 2.0](https://www.anchorterminal.com/compare/rightnow-system-zero-vs-vela.md)\n- [ScaleDown vs Vela 2.0](https://www.anchorterminal.com/compare/scaledown-vs-vela.md)\n- [SPEV vs Vela 2.0](https://www.anchorterminal.com/compare/sprag-spev-vs-vela.md)\n- [kettei vs Vela 2.0](https://www.anchorterminal.com/compare/sqwish-kettei-vs-vela.md)\n- [Strands Decider 2B vs Vela 2.0](https://www.anchorterminal.com/compare/strands-decider-vs-vela.md)\n- [Jev vs Vela 2.0](https://www.anchorterminal.com/compare/typesafe-jev-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 Vela 2.0", "url": "" } ], "description": "Vela 2.0 scores 70.4 (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", "Vela 2.0 BB 70.4", "scores" ], "h1": "Gemini Decision 1 vs Vela 2.0", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gemini-decision-1-vs-vela", "published": "2026-10-01", "section": "tools", "title": "Gemini Decision 1 vs Vela 2.0 for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/gemini-decision-1-vs-vela" }, "tokens": { "markdown": 6500, "slim": 1180 }, "version": 1 }