{ "data": { "a": { "slug": "siliconflow", "name": "SiliconFlow", "vendor": "SiliconFlow Labs Pte. Ltd.", "vendorUrl": "https://www.siliconflow.com", "kind": "model", "category": "inference", "summary": "SiliconFlow is a hosted inference API for open-weight models, covering chat, vision, embeddings, reranking, image, video and speech. It answers OpenAI-style and Anthropic-style calls at api.siliconflow.com with a Bearer key.", "url": "https://www.anchorterminal.com/tools/siliconflow", "markdownUrl": "https://www.anchorterminal.com/tools/siliconflow.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/siliconflow.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/siliconflow.json", "dns": "siliconflow.grade.anchorterminal.com", "license": "Proprietary service under the SiliconFlow Terms of Use", "transports": [ "http" ], "remoteUrl": "https://api.siliconflow.com/v1", "packages": [], "auth": "api-key", "authNotes": "Self-serve Bearer key from the console at https://cloud.siliconflow.com/account/ak after a browser sign-up with a Google or GitHub account. The Anthropic-style `/messages` route also takes the key in `x-api-key`. No scopes, expiry or per-key limits are documented, and rate limits apply to the account, not the key. The error page says some models answer 403 until real-name authentication is done.", "pricing": "usage", "pricingNotes": "Pay per token, image, video or byte of speech text, with $1 in free credits for a new account. Whether a card is needed to claim the credit was not established. DeepSeek-V4.1-Flash costs $0.15 in, $0.003 cached and $0.60 out per 1M tokens. The FAQ says monthly spending limits can be set in the dashboard (https://www.siliconflow.com/pricing, checked 2026-10-09).", "priceSummary": "from $0.15 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI file or the pricing page (checked 2026-10-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://docs.siliconflow.com/en/userguide/quickstart", "rateLimitsUrl": "https://docs.siliconflow.com/cn/userguide/rate-limits/rate-limit-and-upgradation", "llmsTxt": "https://docs.siliconflow.com/llms.txt", "openapi": "https://docs.siliconflow.com/cn/api-reference/openapi.yaml", "capabilities": [ "inference.llm", "inference.open-weights", "embed.text", "rerank", "image.generate", "video.generate", "speech.tts", "speech.stt" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "free-credit", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "singapore" ], "lastRelease": "2026-09-14", "graded": true, "anchor": { "graded": true, "score": 42.9, "grade": "E", "agentReady": false, "rank": 1266, "ranked": true, "rankOf": 1510, "categoryRank": 39, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 37, "payments": 35, "reliability": 33, "schema": 65, "security": 39, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-05" }, "negative": -3, "negativeNotes": [ "2025-12-31 to 2026-06-11. SiliconFlow's release notes carry six model removal entries dated the day the models were removed, with no earlier entry announcing them, and on 11 June 2026 all traffic for `zai-org/GLM-5` and `moonshotai/Kimi-K2.5` was routed to successor models in an entry of the same date. Requests naming those models stopped working or changed model with notice only on the day. Read at the floor and folded into one term, as together-inference's and fireworks-inference's same-day serverless removals were, -3 (https://docs.siliconflow.com/en/release-notes/overview, per the 9 October reading)." ], "verdict": "Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.", "bestFor": "Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.", "strengths": [ "Per-token, per-image and per-video prices are public for every listed model, such as DeepSeek-V4.1-Flash at $0.15 in and $0.60 out per 1M tokens", "One Bearer key reaches 22 operations in a public OpenAPI 3.0 file, with OpenAI-style `/chat/completions` and Anthropic-style `/messages`", "`llms.txt` and a Markdown copy of every docs page, in English and Chinese", "Rate limits are published by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5", "The terms say SiliconFlow will not access Interaction Data, has no obligation to store it, and uses it only to supply the service" ], "weaknesses": [ "No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment", "Release notes stop at 11 June 2026. Six notices from December 2025 to June 2026 give a removal date equal to the notice date", "The Terms of Use of 9 September 2026 forbid use for any commercial purposes, scraping tools and benchmarking. This matters before any probe is run", "The OpenAPI model list and the function calling guide name models already removed, and omit every model added since July 2026", "No security.txt, disclosure policy, certification, DPA or sub-processor list was found, and no official SDK is published" ], "agentNotes": [ "Set the OpenAI client's base URL to `https://api.siliconflow.com/v1`, or the Anthropic client's to `https://api.siliconflow.com/`, and send the key as a Bearer token", "Take model ids from the model pages or `GET /v1/models`, not from the OpenAPI enum or the function calling guide, which list removed models", "Check the `model` field of each response. On 11 June 2026 traffic for GLM-5 and Kimi-K2.5 was routed to successor models", "Use `response_format` of `json_object` only where the model page says JSON Mode is supported, and keep `max_tokens` about 10,000 below the context length", "Set the Claude Code environment variables by hand. The automated route pipes a script from an Amazon S3 bucket into bash" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 42.9 } ], "editorialScores": { "ergonomics": 60, "maintenance": 37, "payments": 35, "reliability": 33, "schema": 65, "security": 39, "transparency": 34 }, "provenanceScore": 70, "checked": { "categories": { "ergonomics": "2026-11-05", "maintenance": "2026-11-05", "payments": "2026-11-05", "reliability": "2026-11-05", "schema": "2026-11-05", "security": "2026-11-05", "transparency": "2026-11-05" }, "last": "2026-11-05", "lastMoved": "2026-11-05", "oldest": "2026-11-05", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install --upgrade openai", "http": "curl --request POST \\\n --url https://api.siliconflow.com/v1/chat/completions \\\n --header 'authorization: Bearer $SILICONFLOW_API_KEY' \\\n --header 'content-type: application/json' \\\n --data '{\"model\":\"deepseek-ai/DeepSeek-V4.1-Flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}],\"max_tokens\":128}'", "claudeCode": "export ANTHROPIC_BASE_URL=\"https://api.siliconflow.com/\"\nexport ANTHROPIC_MODEL=\"your-preferred-model\"\nexport ANTHROPIC_API_KEY=\"sk-your-api-key-here\"" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/siliconflow" }, "area": "models", "unitPrices": [ { "item": "Z-Image-Turbo, one image", "unit": "image", "usd": 0.005 }, { "item": "FLUX.1-dev, one image", "unit": "image", "usd": 0.014 }, { "item": "FLUX 1.1 [pro], one image", "unit": "image", "usd": 0.04 }, { "item": "Wan2.2-T2V-A14B, one video", "unit": "call", "usd": 0.29, "note": "Priced per video generated" } ], "provenance": { "legalEntity": "SiliconFlow Labs Pte. Ltd.", "domain": "siliconflow.com", "domainRegistered": "2019-02-14", "endpointOnVendorDomain": true, "terms": "https://docs.siliconflow.com/en/legals/terms-of-service", "privacy": "https://docs.siliconflow.com/en/legals/privacy-policy", "statusPage": "", "changelog": "https://docs.siliconflow.com/en/release-notes/overview", "securityTxt": "none", "checked": "2026-11-05", "notes": [ "The Terms of Use, updated 9 September 2026, name SILICONFLOW LABS PTE. LTD., apply Singapore law and set SIAC arbitration in Singapore. They govern the platform in its web, API and mobile versions, and no separate API agreement was found.", "The Privacy Policy, updated 9 September 2026, names the same entity and cites Singapore's Personal Data Protection Act 2012. It keeps square brackets around several values, such as [Stripe] and [Google and GitHub].", "No status page is linked from the site, the docs index or the pages read.", "https://www.siliconflow.com/.well-known/security.txt answered 404.", "The API answers at api.siliconflow.com, the docs at docs.siliconflow.com and the console at cloud.siliconflow.com. The terms point users in mainland China to siliconflow.cn.", "No SLA, DPA or sub-processor list was found.", "The Terms of Use (updated 9 September 2026) clause 3.4(i) bar any manual or automated software, including spiders, robots, scrapers and crawlers, to scrape, collect or download information from the Platform or Services, which they define to include the web, API and mobile versions (read 5 November 2026).", "The domain registration date was recorded from an RDAP lookup on 9 October 2026, a source this check doesn't use. It wasn't re-read." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/siliconflow.json", "live": { "slug": "siliconflow", "probe": { "target": "https://api.siliconflow.com/v1", "method": "get", "lastAt": "2026-11-07T19:22:33.096265082Z", "lastOk": true, "lastStatus": 404, "lastMs": 359, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 276, "p95ms24h": 377, "samples24h": 238, "samples30d": 7215, "days": [ { "date": "2026-10-09", "probes": 85, "ok": 85 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 252 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "securityTxt": { "url": "https://siliconflow.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:29.327038169Z" }, "llmsTxt": { "url": "https://docs.siliconflow.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:55.35975587Z" }, "domain": { "domain": "siliconflow.com", "registered": "2019-02-14", "source": "https://rdap.verisign.com/com/v1/domain/siliconflow.com", "checkedAt": "2026-11-01T14:18:54.160809284Z" }, "pages": [ { "url": "https://docs.siliconflow.com/en/release-notes/overview", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:08:02.877282346Z", "changedAt": "2026-10-26T03:15:35.683654862Z", "fingerprint": "174b36f1f0c8" }, { "url": "https://www.siliconflow.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-07T14:34:38.076372647Z", "changedAt": "2026-10-22T01:36:57.039692841Z", "fingerprint": "e1314294f4a8" }, { "url": "https://docs.siliconflow.com/en/legals/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:07:58.655668132Z", "changedAt": "2026-10-26T03:15:31.436126122Z", "fingerprint": "6533065904d9" }, { "url": "https://docs.siliconflow.com/en/legals/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:08:00.852123613Z", "changedAt": "2026-10-26T03:15:33.589043532Z", "fingerprint": "9b70022b01e3" } ], "updatedAt": "2026-11-07T19:22:33.096265082Z" } }, "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SiliconFlow's 42.9 (E), and leads in every scored category.", "b": { "slug": "vertex-ai", "name": "Vertex AI (Gemini Enterprise Agent Platform)", "vendor": "Google Cloud", "vendorUrl": "https://cloud.google.com/products/gemini-enterprise-agent-platform", "kind": "model", "category": "inference", "summary": "Google Cloud's enterprise API for Gemini models and Model Garden partner and open models, including Claude, Llama and Qwen, on the platform formerly called Vertex AI, with regional endpoints, provisioned throughput and IAM access.", "url": "https://www.anchorterminal.com/tools/vertex-ai", "markdownUrl": "https://www.anchorterminal.com/tools/vertex-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/vertex-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/vertex-ai.json", "dns": "vertex-ai.grade.anchorterminal.com", "repo": "https://github.com/googleapis/python-genai", "license": "Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://aiplatform.googleapis.com/v1", "packages": [ { "registry": "pypi", "name": "google-genai" }, { "registry": "npm", "name": "@google/genai" }, { "registry": "pypi", "name": "google-cloud-aiplatform" } ], "auth": "mixed", "authNotes": "OAuth 2.0 bearer tokens from application default credentials or service accounts, with IAM roles on a Google Cloud project, which the docs recommend for production. Google Cloud API keys, including express mode keys (Preview) that need no project or location in the URL, are recommended for testing. The OpenAI-compatible endpoint accepts only Google Cloud auth.", "pricing": "usage", "pricingNotes": "Per-token prices per model, with global and higher non-global rates and Standard, Priority and Flex/Batch options. Gemini 4 Argon costs $4/$20 per million, with 50% credits back through 2026-11-30. Gemini 3.8 Flash costs $0.75/$3.75 through 2026-12-31, then $1.50/$7.50. Claude Opus 5.5 $4/$20. Batch and Flex half price on Gemini. Only requests that return 200 are charged. Express mode gives new users 90 days free with no billing details (https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing).", "priceSummary": "from $0.30 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on the pricing page or in the express mode docs (checked 2026-11-06). Access needs a Google account and, after the express trial, a Cloud billing account.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 20917051, "asOf": "2026-11-06" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/start", "rateLimitsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/standard-paygo", "openapi": "https://aiplatform.googleapis.com/$discovery/rest?version=v1", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "open-weights", "enterprise", "usage-priced", "free-tier", "no-card", "python", "typescript", "status-page" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 88.7, "grade": "AA", "agentReady": true, "rank": 2, "ranked": true, "rankOf": 1510, "categoryRank": 1, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 97, "maintenance": 91, "payments": 40, "reliability": 99, "schema": 94, "security": 97, "transparency": 90 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA.", "bestFor": "Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.", "strengths": [ "No incident naming Vertex or Gemini on status.cloud.google.com between 8 July and 6 October 2026", "Public discovery document for the v1 API (revision 20260930) with 1,128 methods and 1,493 schemas", "JSON output follows a supplied `response_schema` when `response_mime_type` is `application/json`", "Express mode gives new users 90 days free with an API key and no billing details", "Only requests that return 200 are charged", "Gemini 4 Argon, GA since 13 October 2026, has a 1,048,576-token unified limit at $4/$20 per million tokens" ], "weaknesses": [ "The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them", "Gemini 3.6 Flash, released 21 July 2026, retires on 19 November 2026, and 3.7 Flash on 28 January 2027", "Zero retention needs an exception request, and flagged prompts may be kept up to 90 days", "The Interactions API stores prompts and responses unless `store` is set to false", "No llms.txt on the docs site, and the docs mix the Vertex AI and Agent Platform names", "The model versions table still listed Gemini 2.5 Pro, Flash and Flash-Lite as available after their 20 October 2026 retirement date" ], "agentNotes": [ "Call the `global` location on aiplatform.googleapis.com unless data residency requires a Region. The 429 guide says it spreads load and reduces errors", "Back off exponentially on 429 `Resource exhausted`. On Standard PayGo it means shared capacity is busy, not a fixed quota", "Send `response_schema` with `response_mime_type: application/json`. JSON mode alone doesn't guarantee valid output", "Set `store` to false on Interactions API calls. It defaults to true and keeps prompts and responses", "Pin `gemini-4-argon` or `gemini-3.8-flash`, both in the 12-month tier, rather than 3.7 or 3.6 Flash, which retire on 28 January 2027 and 19 November 2026" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "AA", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 88.7 } ], "editorialScores": { "ergonomics": 97, "maintenance": 91, "payments": 40, "reliability": 99, "schema": 94, "security": 97, "transparency": 83 }, "provenanceScore": 97, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install google-genai # or: npm i @google/genai", "http": "curl -X POST \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n \"https://aiplatform.googleapis.com/v1/projects/${GOOGLE_CLOUD_PROJECT}/locations/global/publishers/google/models/gemini-3.8-flash:generateContent\" \\\n -d '{\"contents\":{\"role\":\"user\",\"parts\":{\"text\":\"Explain how AI works in a few words\"}}}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/vertex-ai" }, "sameCompany": [ "gemini-api", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "google-imagen", "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", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api" ], "area": "models", "unitPrices": [ { "item": "Grounding with Google Search over 5,000 a month (Gemini 3)", "unit": "1k-requests", "usd": 14 } ], "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://cloud.google.com/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://status.cloud.google.com", "changelog": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/release-notes", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "The product runs under the Google Cloud Platform terms, whose contracting Google entity depends on the billing country, as for vertex-ai-tuning.", "www.google.com/.well-known/security.txt expires on 2030-04-01 (read 2026-11-06).", "docs.cloud.google.com has no llms.txt (404 on 6 October 2026), but each docs page answers as Markdown when `.md.txt` is added to its URL." ], "score": 97 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/vertex-ai.json", "live": { "slug": "vertex-ai", "probe": { "target": "https://aiplatform.googleapis.com/v1", "method": "get", "lastAt": "2026-11-07T19:22:43.041024218Z", "lastOk": true, "lastStatus": 404, "lastMs": 664, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 118, "p95ms24h": 752, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.0.0", "released": "2026-11-06", "seenAt": "2026-11-07T00:15:33.519504561Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.0.0", "seenAt": "2026-11-07T00:15:31.639429892Z" }, { "registry": "pypi", "name": "google-cloud-aiplatform", "version": "2.7.0", "released": "2026-10-28", "seenAt": "2026-11-07T00:15:32.521225598Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.0.0", "released": "2026-11-06", "seenAt": "2026-11-07T00:15:31.485691571Z" } ], "githubStars": 4057, "npmWeekly": 31989360, "pypiWeekly": 38431674, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-07T16:01:22.068349931Z" }, "pages": [ { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:01:16.257808105Z", "changedAt": "2026-11-07T14:01:16.257808105Z", "fingerprint": "a4fe253bd23f" } ], "updatedAt": "2026-11-07T19:22:43.041024218Z" } }, "edges": [ { "by": 66, "category": "reliability", "leads": "vertex-ai", "name": "Reliability", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Hosted reading. No status page is linked from the site, the docs index or any page read (0). With no page there is no incident record to read (5 of 30). Rate limits are published with numbers by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5, on a page served only in Chinese, with per-model figures left to the console (12 of 15). The 429 body is documented and names the limit reached, and the docs say to wait and retry. No `Retry-After` header, backoff figures or idempotency guidance were found (6 of 15). No SLA found. Clause 4.2 of the terms disclaims any uptime or availability commitment (0). The serverless API is generally available, and `/systemone` is marked alpha (10). 0+5+12+6+0+10 = 33.", "b": "Google Cloud status dashboard with per-product history (20). Its incident feed, read on 6 November, has no incident naming Vertex Gemini API, Gemini or the Agent Platform in the 90 days. The regional incidents of 20 August, 1 September and 20 October (europe-west2) and the Cloud Storage incident of 8 and 9 October didn't list it among affected products (30). Standard PayGo publishes usage tiers by 30-day spend with baseline throughput, 2,000,000 to 50,000,000 tokens a minute for Flash models and 500,000 to 10,000,000 for Pro, plus quota tables for other methods (15). A 429 page says to use the global endpoint and truncated exponential backoff, and a retry-strategy page (15). SLA of 99.5 per cent a month on generateContent and streamGenerateContent, 95 per cent for short-term availability models, and a latency SLO on Provisioned Throughput. The 429 page says pay-as-you-go 429s don't count toward the SLA error rate (10). Gemini 4 Argon reached GA on 13 October and Gemini 3.8 Flash moved to the 12-month tier, and the v1 API, 3.5 Flash-Lite and Claude Opus 5.5 are GA, but express mode was Preview at the 6 October check (9 of 10, up from 8). Up from 98 on 6 October." }, "scores": { "a": 33, "b": 99 } }, { "by": 58, "category": "security", "leads": "vertex-ai", "name": "Security \u0026 auth", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Bearer keys created in the console. No scopes, expiry, per-key limits or rotation are documented, and deletion of a key was not confirmed because the console was not read (15 of 30). The terms say Interaction Data is used only to supply the service and is not used or disclosed without authorisation. No statement on model training was found (12 of 20). The terms say there is no obligation to store Interaction Data. No retention period or zero-retention option is stated, and clause 5.11 allows communications to be recorded and passed to the authorities (6 of 15). The release note of 26 March 2026 describes usage and cost breakdowns and invoice export, and the pricing FAQ says monthly spending limits can be set. No audit log was found (6 of 15). No security.txt (404), disclosure policy, bug bounty or named certification was found on the pages read. The product introduction says the service complies with industry standards and names none (0 of 20). 15+12+6+6+0 = 39.", "b": "Model reading. OAuth 2.0 with IAM roles and service accounts, which the docs recommend for production, with API keys for testing. No key in a query string found in the docs we read (30). The training restriction in the Service Specific Terms says Google won't use customer data to train or fine-tune any model without permission, GA and pre-GA alike (20). Default in-memory caching for 24 hours that can be disabled, flagged prompts logged up to 90 days unless an abuse-monitoring exception is granted or the customer has a Cloud Master Agreement, the Interactions API storing by default, and Claude Fable 5 and Mythos 5 requiring 30-day retention shared with Anthropic (12 of 15, a judgement call matching the Fable exception on the Anthropic and Bedrock listings). Cloud Audit Logs, with Data Access logs off until enabled, and optional request-response logging to BigQuery (15). google.com security.txt valid to 2030-04-01, the Google VRP, and Generative AI on the platform in ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 scope per the 1 October services-in-scope check for vertex-ai-tuning, not re-read on 6 October (20)." }, "scores": { "a": 39, "b": 97 } }, { "by": 54, "category": "maintenance", "leads": "vertex-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Model reading, per the 9 October check, not re-read on 5 November. Hy4-preview is listed as released on 14 September 2026, 52 days before this check, and no later model or API change was recorded; blog post dates record no API change (20). No notice period is stated. Six removal notices from 31 December 2025 to 11 June 2026 are dated the day of removal, and two in March 2026 gave six and seven days (2 of 12). The release notes record about 60 model ids removed in ten notices over the last twelve months (2 of 8). The release notes stop at 11 June 2026 while nine chat models were added afterwards. Support is by email and Discord (6 of 15). Replies in the support channels were not examined (2 of 10). No official SDK. The `langchain-siliconflow` repository in the vendor's GitHub organisation was last changed on 12 December 2025 (version 0.2.3), confirmed from a clone on 5 November (3 of 15). The API description and two guides are out of date (2 of 10). 20+2+2+6+2+3+2 = 37.", "b": "Model reading. Gemini Omni 1.2 Flash GA on 5 November, Grok 4.7 GA on 27 October, Gemini 4 Argon GA on 13 October and Claude Haiku 5.5 on 7 October 2026 (30). Stable models stay at least 12 months, and since 6 October Gemini 4 Argon and Gemini 3.8 Flash are in that tier, while 3.7 and 3.6 Flash remain short-term with at least 45 days' notice once a date is set (10 of 12, up from 8). Gemini 2.5 Pro, Flash and Flash-Lite were due to retire on 20 October, three models on one date after more than 12 months, with replacements named. We didn't check partner model retirements (6 of 8, down from 8). Dated release notes on most working days, newest 5 November, and Cloud Customer Care (15). python-genai replies not sampled this run (5 of 10). Current SDKs, google-genai 3.0.0 on 6 November and `@google/genai` 3.0.0, and google-cloud-aiplatform 2.7.0 on 28 October 2026 (15). Supported runtimes stated, Python 3.10 or later and Node 20 or later, per the 6 October check (10). Unchanged at 91." }, "scores": { "a": 37, "b": 91 } }, { "by": 38, "category": "transparency", "leads": "vertex-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Closed service. The Terms of Use name the entity, Singapore law and SIAC arbitration. They also grant a licence \"for in Singapore\" and forbid use for any commercial purposes, which sits oddly with a paid API (10 of 30). The terms and the privacy policy agree that Interaction Data is processed on the customer's instructions with no obligation to store it. No retention period or DPA was found, and the privacy policy keeps drafting brackets around several values (12 of 30). Dated removal notices in the release notes, with no written policy or notice period (8 of 20). Stripe is named as payment processor and other providers only by type. Data may be handled within or outside Singapore, with no locations given (4 of 20). 10+12+8+4 = 34.", "b": "Closed service under the Google Cloud terms, with the Gen AI SDKs under Apache-2.0 and a public discovery document (18, as for Bedrock's open service models). The zero data retention and abuse monitoring pages agree with the Cloud terms and give periods for each case, 24 hours in memory, up to 90 days for flagged prompts, 30 days for Advanced AI models, 3 days for Search grounding logs and 30 days for Maps grounding, and say which can't be switched off (27 of 30). A dated model versions table with release and retirement dates and a stated 12-month or 45-day policy, but on 6 November it still listed Gemini 2.5 Pro, Flash and Flash-Lite as available with a 20 October 2026 retirement date and left them out of the retired table (18 of 20, down from 20). The Cloud subprocessor list with locations, per the 1 October vertex-ai-tuning check, regional and multi-region endpoints, and per-model ML processing locations on the partner model cards (20)." }, "scores": { "a": 52, "b": 90 } }, { "by": 37, "category": "ergonomics", "leads": "vertex-ai", "name": "Agent ergonomics", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-style `tools` with up to 128 functions, and each model page says whether Tools are supported. No `tool_choice` field is in the chat schema and the guide is out of date (12 of 20). `response_format` of `json_object`. The DeepSeek-V4.1-Flash page says Structured Outputs are not supported (7 of 15). Cached input prices are published per model. No caching guide was found (8 of 15). Context listed as 1049K tokens on eleven chat models on the home page, with output up to 393K (14 of 15). `/files` and `/batches` are in the OpenAPI file for chat completions only, with no guide in the English index, no batch price and a window description that contradicts itself (5 of 10). No official SDK. The OpenAI and Anthropic clients work with a changed base URL (6 of 10). Errors carry an HTTP status, a numeric code and a message. No list of codes was found (8 of 15). 12+7+8+14+5+6+8 = 60.", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `FunctionCallingConfig` mode ANY and `allowedFunctionNames` to force a named tool (20). Schema-guaranteed JSON output (15). Implicit and explicit context caching, cached input at 0.1x, $0.075 per million on 3.8 Flash through 31 December (15). 1,048,576-token context on 3.8 Flash, a unified 1,048,576-token input and output limit on Gemini 4 Argon, and 1,000,000 on Claude Opus 5.5 (15). Flex/Batch at half price on Gemini (10). Gen AI SDKs for Python and JavaScript among others, and the OpenAI libraries work with Google Cloud auth (10). 429 handling documented per quota framework, with standard Google status codes elsewhere (12 of 15)." }, "scores": { "a": 60, "b": 97 } }, { "by": 29, "category": "schema", "leads": "vertex-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Public OpenAPI 3.0 file with 22 operations and 102 schemas, linked from `llms.txt` under the Chinese path only. Its chat model enum of 71 ids includes removed models and omits every model added since July 2026 (19 of 25). `llms.txt` and a Markdown copy of each docs page (10). Guides for function calling, JSON mode, reasoning, prefix and FIM completion. The function calling guide lists only models the release notes show as removed, and its examples use DeepSeek-V2.5 (10 of 20). Typed parameters with 104 enums and required fields. `response_format` is typed as a bare string with an example (10 of 15). Curl and Python examples, an error page with seven HTTP codes and one body code, and 400, 401, 404, 429, 503 and 504 responses in the file (9 of 15). The file's version reads 1.0.0. The release notes are dated, stop at 11 June 2026 and since March 2026 record only model removals (7 of 15). 19+10+10+10+9+7 = 65.", "b": "Model reading. Keyless discovery document for the Agent Platform API v1, revision 20260930, with 1,128 methods and 1,493 schemas (25). No llms.txt, but every docs page answers as Markdown at `.md.txt`, counted as for vertex-ai-tuning (10). Model cards give limits, capabilities and retirement dates, and the 3.8 Flash card says when to use 3.7 Flash instead, but the docs mix the Vertex AI and Agent Platform names, and the OpenAI page tells new users to call the Gemini Developer API instead (16 of 20). Structured output with `response_schema` and `response_mime_type: application/json` always follows the schema per the docs, and complex schemas fail with a 400 (15). Examples on every page and a 429 guide, but we found no full error code list for generateContent (13 of 15). Versioned v1 and v1beta1 APIs and release notes with entries on most working days, newest 5 November 2026 (15)." }, "scores": { "a": 65, "b": 94 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "SiliconFlow Labs Pte. Ltd.", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://api.siliconflow.com/v1", "b": "https://aiplatform.googleapis.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "OAuth or key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the SiliconFlow Terms of Use", "b": "Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-09-14", "b": "2026-11-05", "name": "Last release" }, { "a": "no date given", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "no date given", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "20.9M PyPI/wk", "name": "Popularity" }, { "a": "2.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SiliconFlow's 42.9 (E), and leads in every scored category.", "question": "Which is better for AI agents, SiliconFlow or Vertex AI (Gemini Enterprise Agent Platform)?" }, { "answer": "SiliconFlow needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.", "question": "Do SiliconFlow and Vertex AI (Gemini Enterprise Agent Platform) need an API key?" }, { "answer": "Yes. SiliconFlow has a hosted endpoint at https://api.siliconflow.com/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.", "question": "Can an agent call SiliconFlow and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.", "slug": "siliconflow", "watchFor": "No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment" }, { "aheadOn": [ "Reliability, 99 against 33", "Schema \u0026 documentation, 94 against 65", "Agent ergonomics, 97 against 60", "Security \u0026 auth, 97 against 39", "Payments \u0026 pricing, 40 against 35", "Maintenance \u0026 community, 91 against 37", "Transparency \u0026 trust, 90 against 52" ], "also": [ "Agent-ready, a grade of BB or better", "Free to start without a card", "No incidents deducted, where SiliconFlow loses 3 points for them" ], "goodFor": "Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.", "slug": "vertex-ai", "watchFor": "The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them" } ], "history": { "siliconflow": [ { "causes": [ "correction", "time" ], "date": "2026-11-05", "delta": -3.9, "from": 46.8, "fromGrade": "D", "to": 42.9, "toGrade": "E" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.2, "from": 47, "fromGrade": "D", "to": 46.8, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.3, "from": 46.7, "fromGrade": "D", "to": 47, "toGrade": "D" } ], "vertex-ai": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-siliconflow.json", "title": "Amazon Bedrock vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai.json", "title": "Amazon Bedrock vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-siliconflow.json", "title": "Claude API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai.json", "title": "Claude API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-siliconflow.json", "title": "Antseed vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/antseed-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-vertex-ai.json", "title": "Antseed vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/antseed-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.json", "title": "Azure OpenAI in Foundry Models vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai.json", "title": "Azure OpenAI in Foundry Models vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow.json", "title": "BlockRun.AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai.json", "title": "BlockRun.AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-siliconflow.json", "title": "Cerebras Inference vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai.json", "title": "Cerebras Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-siliconflow.json", "title": "Cloudflare AI Gateway vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai.json", "title": "Cloudflare AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-siliconflow.json", "title": "Cloudflare Workers AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai.json", "title": "Cloudflare Workers AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-siliconflow.json", "title": "Cohere Chat API (Command models) vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai.json", "title": "Cohere Chat API (Command models) vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-siliconflow.json", "title": "DeepInfra vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.json", "title": "DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-siliconflow.json", "title": "DeepSeek API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai.json", "title": "DeepSeek API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.json", "title": "Eden AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.json", "title": "Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-siliconflow.json", "title": "Glimpse vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai.json", "title": "Glimpse vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-siliconflow.json", "title": "Fireworks AI Inference vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai.json", "title": "Fireworks AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.json", "title": "Gemini Developer API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.json", "title": "Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-siliconflow.json", "title": "GroqCloud vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/groq-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai.json", "title": "GroqCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/groq-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-siliconflow.json", "title": "Hugging Face Inference Providers vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai.json", "title": "Hugging Face Inference Providers vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-siliconflow.json", "title": "Inkling 2 Small vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vertex-ai.json", "title": "Inkling 2 Small vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-siliconflow.json", "title": "Jais 3 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/jais-3-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.json", "title": "Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.json", "title": "Meta Model API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.json", "title": "Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow.json", "title": "MiniMax API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai.json", "title": "MiniMax API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-siliconflow.json", "title": "Mistral AI API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.json", "title": "Mistral AI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.json", "title": "Muse Spark Open vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.json", "title": "Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-siliconflow.json", "title": "Novita AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai.json", "title": "Novita AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-siliconflow.json", "title": "OpenAI API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/openai-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.json", "title": "OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-siliconflow.json", "title": "OpenRouter vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/openrouter-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai.json", "title": "OpenRouter vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow.json", "title": "Prism Inference vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai.json", "title": "Prism Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-siliconflow.json", "title": "Router.com (Ramp Router) vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai.json", "title": "Router.com (Ramp Router) vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-siliconflow.json", "title": "Beam 1.0 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai.json", "title": "Beam 1.0 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-siliconflow.json", "title": "RouterPlus vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/routerplus-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai.json", "title": "RouterPlus vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-siliconflow.json", "title": "SambaCloud vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/sambanova-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.json", "title": "SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.json", "title": "SiliconFlow vs Simplismart", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api.json", "title": "SiliconFlow vs StepFun API", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-swe-2-flash.json", "title": "SiliconFlow vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-tencent-tokenhub.json", "title": "SiliconFlow vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-together-inference.json", "title": "SiliconFlow vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-vercel-ai-gateway.json", "title": "SiliconFlow vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-xai-api.json", "title": "SiliconFlow vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.json", "title": "Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai.json", "title": "StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai.json", "title": "SWE-2 Flash vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai.json", "title": "Tencent Cloud TokenHub vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai.json", "title": "Together AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai.json", "title": "Vercel AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api.json", "title": "Vertex AI (Gemini Enterprise Agent Platform) vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow.json", "title": "Nemotron 3.5 Super VL vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai.json", "title": "Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai" } ], "panel": null, "scores": [ { "by": 66, "edge": "vertex-ai", "key": "reliability", "name": "Reliability", "siliconflow": 33, "vertex-ai": 99, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 29, "edge": "vertex-ai", "key": "schema", "name": "Schema \u0026 documentation", "siliconflow": 65, "vertex-ai": 94, "weight": 13 }, { "by": 37, "edge": "vertex-ai", "key": "ergonomics", "name": "Agent ergonomics", "siliconflow": 60, "vertex-ai": 97, "weight": 13 }, { "by": 58, "edge": "vertex-ai", "key": "security", "name": "Security \u0026 auth", "siliconflow": 39, "vertex-ai": 97, "weight": 14 }, { "by": 5, "edge": "vertex-ai", "key": "payments", "name": "Payments \u0026 pricing", "siliconflow": 35, "vertex-ai": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 54, "edge": "vertex-ai", "key": "maintenance", "name": "Maintenance \u0026 community", "siliconflow": 37, "vertex-ai": 91, "weight": 7 }, { "by": 38, "edge": "vertex-ai", "key": "transparency", "name": "Transparency \u0026 trust", "siliconflow": 52, "vertex-ai": 90, "weight": 7 } ], "summary": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SiliconFlow's 42.9 (E), and leads in every scored category. Both do llm inference.", "tasks": [ { "a": "$0.09", "b": "$22.03", "cheaper": "siliconflow", "task": "Research a product and write a sourced report", "what": "deepseek-ai/DeepSeek-V4.1-Flash on SiliconFlow, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.05", "b": "$5.09", "cheaper": "siliconflow", "task": "Build a small four-page website", "what": "deepseek-ai/DeepSeek-V4.1-Flash on SiliconFlow, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.01", "b": "$1.09", "cheaper": "siliconflow", "task": "Find and fix a bug in a large codebase", "what": "deepseek-ai/DeepSeek-V4.1-Flash on SiliconFlow, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" } ], "tutorials": [], "verdicts": { "siliconflow": "Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.", "vertex-ai": "Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai", "json": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.min.md" }, "markdown": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SiliconFlow's 42.9 (E), and leads in every scored category. Both do llm inference.\n\n- SiliconFlow: grade E, 42.9/100, rank #1266 of 1510. Markdown https://www.anchorterminal.com/tools/siliconflow.md · JSON https://www.anchorterminal.com/api/v1/tools/siliconflow.json\n- Vertex AI (Gemini Enterprise Agent Platform): grade AA, 88.7/100, rank #2 of 1510. Markdown https://www.anchorterminal.com/tools/vertex-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### SiliconFlow (E)\n\nGood for: Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.\n\nWatch for: No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment\n\n### Vertex AI (Gemini Enterprise Agent Platform) (AA)\n\nGood for: Google Cloud customers who need Gemini with residency, VPC-SC, provisioned throughput and an SLA, or Claude under a Google bill.\n\nAhead on:\n- Reliability, 99 against 33\n- Schema \u0026 documentation, 94 against 65\n- Agent ergonomics, 97 against 60\n- Security \u0026 auth, 97 against 39\n- Payments \u0026 pricing, 40 against 35\n- Maintenance \u0026 community, 91 against 37\n- Transparency \u0026 trust, 90 against 52\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Free to start without a card\n- No incidents deducted, where SiliconFlow loses 3 points for them\n\nWatch for: The SLA drops from 99.5 to 95 per cent for short-term availability models, Gemini 3.7 and 3.6 Flash among them\n\n\n## Why the edge\n\n### Reliability: Vertex AI (Gemini Enterprise Agent Platform) +66\n\n- SiliconFlow (33): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Hosted reading. No status page is linked from the site, the docs index or any page read (0). With no page there is no incident record to read (5 of 30). Rate limits are published with numbers by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5, on a page served only in Chinese, with per-model figures left to the console (12 of 15). The 429 body is documented and names the limit reached, and the docs say to wait and retry. No `Retry-After` header, backoff figures or idempotency guidance were found (6 of 15). No SLA found. Clause 4.2 of the terms disclaims any uptime or availability commitment (0). The serverless API is generally available, and `/systemone` is marked alpha (10). 0+5+12+6+0+10 = 33.\n- Vertex AI (Gemini Enterprise Agent Platform) (99): Google Cloud status dashboard with per-product history (20). Its incident feed, read on 6 November, has no incident naming Vertex Gemini API, Gemini or the Agent Platform in the 90 days. The regional incidents of 20 August, 1 September and 20 October (europe-west2) and the Cloud Storage incident of 8 and 9 October didn't list it among affected products (30). Standard PayGo publishes usage tiers by 30-day spend with baseline throughput, 2,000,000 to 50,000,000 tokens a minute for Flash models and 500,000 to 10,000,000 for Pro, plus quota tables for other methods (15). A 429 page says to use the global endpoint and truncated exponential backoff, and a retry-strategy page (15). SLA of 99.5 per cent a month on generateContent and streamGenerateContent, 95 per cent for short-term availability models, and a latency SLO on Provisioned Throughput. The 429 page says pay-as-you-go 429s don't count toward the SLA error rate (10). Gemini 4 Argon reached GA on 13 October and Gemini 3.8 Flash moved to the 12-month tier, and the v1 API, 3.5 Flash-Lite and Claude Opus 5.5 are GA, but express mode was Preview at the 6 October check (9 of 10, up from 8). Up from 98 on 6 October.\n\n### Security \u0026 auth: Vertex AI (Gemini Enterprise Agent Platform) +58\n\n- SiliconFlow (39): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Bearer keys created in the console. No scopes, expiry, per-key limits or rotation are documented, and deletion of a key was not confirmed because the console was not read (15 of 30). The terms say Interaction Data is used only to supply the service and is not used or disclosed without authorisation. No statement on model training was found (12 of 20). The terms say there is no obligation to store Interaction Data. No retention period or zero-retention option is stated, and clause 5.11 allows communications to be recorded and passed to the authorities (6 of 15). The release note of 26 March 2026 describes usage and cost breakdowns and invoice export, and the pricing FAQ says monthly spending limits can be set. No audit log was found (6 of 15). No security.txt (404), disclosure policy, bug bounty or named certification was found on the pages read. The product introduction says the service complies with industry standards and names none (0 of 20). 15+12+6+6+0 = 39.\n- Vertex AI (Gemini Enterprise Agent Platform) (97): Model reading. OAuth 2.0 with IAM roles and service accounts, which the docs recommend for production, with API keys for testing. No key in a query string found in the docs we read (30). The training restriction in the Service Specific Terms says Google won't use customer data to train or fine-tune any model without permission, GA and pre-GA alike (20). Default in-memory caching for 24 hours that can be disabled, flagged prompts logged up to 90 days unless an abuse-monitoring exception is granted or the customer has a Cloud Master Agreement, the Interactions API storing by default, and Claude Fable 5 and Mythos 5 requiring 30-day retention shared with Anthropic (12 of 15, a judgement call matching the Fable exception on the Anthropic and Bedrock listings). Cloud Audit Logs, with Data Access logs off until enabled, and optional request-response logging to BigQuery (15). google.com security.txt valid to 2030-04-01, the Google VRP, and Generative AI on the platform in ISO 27001, 27017 and 27018 and SOC 1, 2 and 3 scope per the 1 October services-in-scope check for vertex-ai-tuning, not re-read on 6 October (20).\n\n### Maintenance \u0026 community: Vertex AI (Gemini Enterprise Agent Platform) +54\n\n- SiliconFlow (37): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Model reading, per the 9 October check, not re-read on 5 November. Hy4-preview is listed as released on 14 September 2026, 52 days before this check, and no later model or API change was recorded; blog post dates record no API change (20). No notice period is stated. Six removal notices from 31 December 2025 to 11 June 2026 are dated the day of removal, and two in March 2026 gave six and seven days (2 of 12). The release notes record about 60 model ids removed in ten notices over the last twelve months (2 of 8). The release notes stop at 11 June 2026 while nine chat models were added afterwards. Support is by email and Discord (6 of 15). Replies in the support channels were not examined (2 of 10). No official SDK. The `langchain-siliconflow` repository in the vendor's GitHub organisation was last changed on 12 December 2025 (version 0.2.3), confirmed from a clone on 5 November (3 of 15). The API description and two guides are out of date (2 of 10). 20+2+2+6+2+3+2 = 37.\n- Vertex AI (Gemini Enterprise Agent Platform) (91): Model reading. Gemini Omni 1.2 Flash GA on 5 November, Grok 4.7 GA on 27 October, Gemini 4 Argon GA on 13 October and Claude Haiku 5.5 on 7 October 2026 (30). Stable models stay at least 12 months, and since 6 October Gemini 4 Argon and Gemini 3.8 Flash are in that tier, while 3.7 and 3.6 Flash remain short-term with at least 45 days' notice once a date is set (10 of 12, up from 8). Gemini 2.5 Pro, Flash and Flash-Lite were due to retire on 20 October, three models on one date after more than 12 months, with replacements named. We didn't check partner model retirements (6 of 8, down from 8). Dated release notes on most working days, newest 5 November, and Cloud Customer Care (15). python-genai replies not sampled this run (5 of 10). Current SDKs, google-genai 3.0.0 on 6 November and `@google/genai` 3.0.0, and google-cloud-aiplatform 2.7.0 on 28 October 2026 (15). Supported runtimes stated, Python 3.10 or later and Node 20 or later, per the 6 October check (10). Unchanged at 91.\n\n### Transparency \u0026 trust: Vertex AI (Gemini Enterprise Agent Platform) +38\n\n- SiliconFlow (52): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Closed service. The Terms of Use name the entity, Singapore law and SIAC arbitration. They also grant a licence \"for in Singapore\" and forbid use for any commercial purposes, which sits oddly with a paid API (10 of 30). The terms and the privacy policy agree that Interaction Data is processed on the customer's instructions with no obligation to store it. No retention period or DPA was found, and the privacy policy keeps drafting brackets around several values (12 of 30). Dated removal notices in the release notes, with no written policy or notice period (8 of 20). Stripe is named as payment processor and other providers only by type. Data may be handled within or outside Singapore, with no locations given (4 of 20). 10+12+8+4 = 34.\n- Vertex AI (Gemini Enterprise Agent Platform) (90): Closed service under the Google Cloud terms, with the Gen AI SDKs under Apache-2.0 and a public discovery document (18, as for Bedrock's open service models). The zero data retention and abuse monitoring pages agree with the Cloud terms and give periods for each case, 24 hours in memory, up to 90 days for flagged prompts, 30 days for Advanced AI models, 3 days for Search grounding logs and 30 days for Maps grounding, and say which can't be switched off (27 of 30). A dated model versions table with release and retirement dates and a stated 12-month or 45-day policy, but on 6 November it still listed Gemini 2.5 Pro, Flash and Flash-Lite as available with a 20 October 2026 retirement date and left them out of the retired table (18 of 20, down from 20). The Cloud subprocessor list with locations, per the 1 October vertex-ai-tuning check, regional and multi-region endpoints, and per-model ML processing locations on the partner model cards (20).\n\n### Agent ergonomics: Vertex AI (Gemini Enterprise Agent Platform) +37\n\n- SiliconFlow (60): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-style `tools` with up to 128 functions, and each model page says whether Tools are supported. No `tool_choice` field is in the chat schema and the guide is out of date (12 of 20). `response_format` of `json_object`. The DeepSeek-V4.1-Flash page says Structured Outputs are not supported (7 of 15). Cached input prices are published per model. No caching guide was found (8 of 15). Context listed as 1049K tokens on eleven chat models on the home page, with output up to 393K (14 of 15). `/files` and `/batches` are in the OpenAPI file for chat completions only, with no guide in the English index, no batch price and a window description that contradicts itself (5 of 10). No official SDK. The OpenAI and Anthropic clients work with a changed base URL (6 of 10). Errors carry an HTTP status, a numeric code and a message. No list of codes was found (8 of 15). 12+7+8+14+5+6+8 = 60.\n- Vertex AI (Gemini Enterprise Agent Platform) (97): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `FunctionCallingConfig` mode ANY and `allowedFunctionNames` to force a named tool (20). Schema-guaranteed JSON output (15). Implicit and explicit context caching, cached input at 0.1x, $0.075 per million on 3.8 Flash through 31 December (15). 1,048,576-token context on 3.8 Flash, a unified 1,048,576-token input and output limit on Gemini 4 Argon, and 1,000,000 on Claude Opus 5.5 (15). Flex/Batch at half price on Gemini (10). Gen AI SDKs for Python and JavaScript among others, and the OpenAI libraries work with Google Cloud auth (10). 429 handling documented per quota framework, with standard Google status codes elsewhere (12 of 15).\n\n### Schema \u0026 documentation: Vertex AI (Gemini Enterprise Agent Platform) +29\n\n- SiliconFlow (65): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Public OpenAPI 3.0 file with 22 operations and 102 schemas, linked from `llms.txt` under the Chinese path only. Its chat model enum of 71 ids includes removed models and omits every model added since July 2026 (19 of 25). `llms.txt` and a Markdown copy of each docs page (10). Guides for function calling, JSON mode, reasoning, prefix and FIM completion. The function calling guide lists only models the release notes show as removed, and its examples use DeepSeek-V2.5 (10 of 20). Typed parameters with 104 enums and required fields. `response_format` is typed as a bare string with an example (10 of 15). Curl and Python examples, an error page with seven HTTP codes and one body code, and 400, 401, 404, 429, 503 and 504 responses in the file (9 of 15). The file's version reads 1.0.0. The release notes are dated, stop at 11 June 2026 and since March 2026 record only model removals (7 of 15). 19+10+10+10+9+7 = 65.\n- Vertex AI (Gemini Enterprise Agent Platform) (94): Model reading. Keyless discovery document for the Agent Platform API v1, revision 20260930, with 1,128 methods and 1,493 schemas (25). No llms.txt, but every docs page answers as Markdown at `.md.txt`, counted as for vertex-ai-tuning (10). Model cards give limits, capabilities and retirement dates, and the 3.8 Flash card says when to use 3.7 Flash instead, but the docs mix the Vertex AI and Agent Platform names, and the OpenAI page tells new users to call the Gemini Developer API instead (16 of 20). Structured output with `response_schema` and `response_mime_type: application/json` always follows the schema per the docs, and complex schemas fail with a 400 (15). Examples on every page and a 429 guide, but we found no full error code list for generateContent (13 of 15). Versioned v1 and v1beta1 APIs and release notes with entries on most working days, newest 5 November 2026 (15).\n\n## Score by category\n\n| Category | Weight | SiliconFlow | Vertex AI (Gemini Enterprise Agent Platform) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 33 | 99 | Vertex AI (Gemini Enterprise Agent Platform) +66 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 65 | 94 | Vertex AI (Gemini Enterprise Agent Platform) +29 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +37 |\n| Security \u0026 auth | 14% (17.5 this run) | 39 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +58 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 40 | Vertex AI (Gemini Enterprise Agent Platform) +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 37 | 91 | Vertex AI (Gemini Enterprise Agent Platform) +54 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 52 | 90 | Vertex AI (Gemini Enterprise Agent Platform) +38 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **42.9 · E** | **88.7 · AA** | |\n\n## Facts side by side\n\n| Fact | SiliconFlow | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | SiliconFlow Labs Pte. Ltd. | Google Cloud |\n| Hosted endpoint | `https://api.siliconflow.com/v1` | `https://aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the SiliconFlow Terms of Use | Proprietary service under the Google Cloud Platform terms. Partner models carry their providers' terms. The Google Gen AI SDKs are Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-09-14 | 2026-11-05 |\n| Terms last updated | no date given | 2026-09-02 |\n| Privacy policy last updated | no date given | 2026-10-01 |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | yes | not found in the text |\n| Popularity | none | 20.9M PyPI/wk |\n| Agent reviews | 2.5/5 (4) | none |\n\n## What the same task costs\n\n| Task | SiliconFlow | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Research a product and write a sourced report | $0.09 | $22.03 |\n| Build a small four-page website | $0.05 | $5.09 |\n| Find and fix a bug in a large codebase | $0.01 | $1.09 |\n\n## How the scores have moved\n\n### SiliconFlow\n\n- -3.9 on 5 November 2026 (D to E): correction, time\n- -0.2 on 28 October 2026: correction\n- +0.3 on 27 October 2026: checklist\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\nNo score change since it was listed.\n\n## Verdicts\n\n**SiliconFlow.** Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.\n\n**Vertex AI (Gemini Enterprise Agent Platform).** Gemini, Claude and open models through one Google Cloud API with IAM, regional endpoints, schema-guaranteed JSON and a 99.5 per cent SLA, and no Vertex incident on the Cloud status page in the 90 days to 6 November 2026. Gemini 4 Argon and 3.8 Flash have 12-month availability, but short-term Flash models get a 95 per cent SLA.\n\n## Before you call either\n\n### SiliconFlow\n\n1. Set the OpenAI client's base URL to `https://api.siliconflow.com/v1`, or the Anthropic client's to `https://api.siliconflow.com/`, and send the key as a Bearer token\n2. Take model ids from the model pages or `GET /v1/models`, not from the OpenAPI enum or the function calling guide, which list removed models\n3. Check the `model` field of each response. On 11 June 2026 traffic for GLM-5 and Kimi-K2.5 was routed to successor models\n4. Use `response_format` of `json_object` only where the model page says JSON Mode is supported, and keep `max_tokens` about 10,000 below the context length\n5. Set the Claude Code environment variables by hand. The automated route pipes a script from an Amazon S3 bucket into bash\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\n1. Call the `global` location on aiplatform.googleapis.com unless data residency requires a Region. The 429 guide says it spreads load and reduces errors\n2. Back off exponentially on 429 `Resource exhausted`. On Standard PayGo it means shared capacity is busy, not a fixed quota\n3. Send `response_schema` with `response_mime_type: application/json`. JSON mode alone doesn't guarantee valid output\n4. Set `store` to false on Interactions API calls. It defaults to true and keeps prompts and responses\n5. Pin `gemini-4-argon` or `gemini-3.8-flash`, both in the 12-month tier, rather than 3.7 or 3.6 Flash, which retire on 28 January 2027 and 19 November 2026\n\n## Questions\n\n### Which is better for AI agents, SiliconFlow or Vertex AI (Gemini Enterprise Agent Platform)?\n\nVertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against SiliconFlow's 42.9 (E), and leads in every scored category.\n\n### Do SiliconFlow and Vertex AI (Gemini Enterprise Agent Platform) need an API key?\n\nSiliconFlow needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.\n\n### Can an agent call SiliconFlow and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?\n\nYes. SiliconFlow has a hosted endpoint at https://api.siliconflow.com/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"siliconflow\", \"b\": \"vertex-ai\"}`. From a terminal: `anchor compare siliconflow vertex-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/siliconflow.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n\n## Other comparisons with SiliconFlow or Vertex AI (Gemini Enterprise Agent Platform)\n\n- [Amazon Bedrock vs SiliconFlow](https://www.anchorterminal.com/compare/amazon-bedrock-vs-siliconflow.md)\n- [Amazon Bedrock vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai.md)\n- [Claude API vs SiliconFlow](https://www.anchorterminal.com/compare/anthropic-api-vs-siliconflow.md)\n- [Claude API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/anthropic-api-vs-vertex-ai.md)\n- [Antseed vs SiliconFlow](https://www.anchorterminal.com/compare/antseed-vs-siliconflow.md)\n- [Antseed vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/antseed-vs-vertex-ai.md)\n- [Azure OpenAI in Foundry Models vs SiliconFlow](https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.md)\n- [Azure OpenAI in Foundry Models vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai.md)\n- [BlockRun.AI vs SiliconFlow](https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow.md)\n- [BlockRun.AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/blockrun-ai-vs-vertex-ai.md)\n- [Cerebras Inference vs SiliconFlow](https://www.anchorterminal.com/compare/cerebras-inference-vs-siliconflow.md)\n- [Cerebras Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai.md)\n- [Cloudflare AI Gateway vs SiliconFlow](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-siliconflow.md)\n- [Cloudflare AI Gateway vs 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(Grok)](https://www.anchorterminal.com/compare/siliconflow-vs-xai-api.md)\n- [Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.md)\n- [StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai.md)\n- [SWE-2 Flash vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/swe-2-flash-vs-vertex-ai.md)\n- [Tencent Cloud TokenHub vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/tencent-tokenhub-vs-vertex-ai.md)\n- [Together AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/together-inference-vs-vertex-ai.md)\n- [Vercel AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/vercel-ai-gateway-vs-vertex-ai.md)\n- [Vertex AI (Gemini Enterprise Agent Platform) vs xAI API (Grok)](https://www.anchorterminal.com/compare/vertex-ai-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs SiliconFlow](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow.md)\n- [Nemotron 3.5 Super VL vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-vertex-ai.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": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": [ { 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