{ "data": { "a": { "slug": "hy4", "name": "Tencent Hy4", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "model", "category": "inference", "summary": "Tencent Hy4 is Tencent's flagship language model, released on 4 November 2026 with image understanding and computer use, served on Tencent Cloud TokenHub as hy4 with a 1M-token context, at ¥6 input and ¥18 output per million tokens.", "url": "https://www.anchorterminal.com/tools/hy4", "markdownUrl": "https://www.anchorterminal.com/tools/hy4.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy4.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy4.json", "dns": "hy4.grade.anchorterminal.com", "license": "Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet", "transports": [ "http" ], "packages": [], "auth": "api-key", "authNotes": "A TokenHub bearer key (or `x-api-key` on the Anthropic-style route) from a Tencent Cloud account with real-name verification, per the tencent-tokenhub listing. Keys can be limited by model, service, IP range and token quota.", "pricing": "usage", "pricingNotes": "¥6 input, ¥0.3 cached input and ¥18 output per million tokens on TokenHub, billed at 50% until 31 December 2026 (Beijing time), per the price page updated 9 November 2026. Tencent's release gives about US$0.90, US$0.045 and US$2.70. No free allowance named for hy4 on the price page.", "priceSummary": "Pay per use", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in Tencent's release or TokenHub's price page, 11 November 2026.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-11" }, "docsUrl": "https://cloud.tencent.com/document/product/1823/130051", "llmsTxt": "https://cloud.tencent.com/document/product/1823/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "reasoning", "long-context", "tool-calling", "usage-priced", "openai-compatible", "anthropic-compatible", "chinese", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 64, "grade": "B", "agentReady": false, "rank": 595, "ranked": true, "rankOf": 1524, "categoryRank": 25, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 73, "payments": 45, "reliability": 46, "schema": 73, "security": 71, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-11" }, "negative": 0, "verdict": "Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.", "bestFor": "Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.", "strengths": [ "1M-token context with 960K input and 64K output, per TokenHub's model list", "Image understanding, function calling, structured output, thinking and caching are marked for hy4", "Prices published: ¥6 input, ¥0.3 cached, ¥18 output per million tokens, billed at 50% until 31 December 2026", "OpenAI- and Anthropic-compatible endpoints on TokenHub, so existing SDKs work with a changed base URL", "Tencent says it's the same price as Hy4 preview despite the upgrade" ], "weaknesses": [ "Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November", "Computer use, which Tencent's release names, isn't marked in TokenHub's model list", "TokenHub needs real-name verification, and the international site answers with a bot check", "No public status page for TokenHub", "Every benchmark in the release is Tencent's own or a partner's, several of them internal" ], "agentNotes": [ "Use model id hy4 on TokenHub's OpenAI- or Anthropic-compatible endpoints; hy4-preview is a separate, older model", "Keep input under 960K tokens and expect at most 64K output", "Reuse prompt prefixes: cached input is ¥0.3 against ¥6", "Check the price page for the end of the 50% discount on 31 December 2026 before budgeting", "Read rate limits from the console or DescribeModelQuota; the public docs don't give them for chat models" ], "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": 64 } ], "editorialScores": { "ergonomics": 86, "maintenance": 73, "payments": 45, "reliability": 46, "schema": 73, "security": 71, "transparency": 55 }, "provenanceScore": 49, "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 https://tokenhub.tencentmaas.com/v1/chat/completions \\\n -H \"Authorization: Bearer $TOKENHUB_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"hy4\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/hy4" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "octop", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "sameModel": [ "tencent-tokenhub" ], "area": "models", "unitPrices": [ { "item": "Hy4 input", "unit": "1m-tokens", "usd": 0.9, "note": "¥6 list, about US$0.90 per Tencent; billed at 50% until 31 December 2026" }, { "item": "Hy4 cached input", "unit": "1m-tokens", "usd": 0.045, "note": "¥0.3 list" }, { "item": "Hy4 output", "unit": "1m-tokens", "usd": 2.7, "note": "¥18 list" } ], "provenance": { "legalEntity": "Tencent Cloud Computing (Beijing) Co., Ltd. (腾讯云计算（北京）有限责任公司)", "domain": "tencentmaas.com", "domainRegistered": "2026-03-13", "endpointOnVendorDomain": false, "terms": "https://cloud.tencent.com/document/product/301/129852", "privacy": "https://privacy.qq.com/document/preview/91d5fd3c98ce4468ae462ac59233bba2", "statusPage": "", "changelog": "https://cloud.tencent.com/document/product/1823/130675", "securityTxt": "none", "checked": "2026-11-11", "notes": [ "The TokenHub Terms of Service (effective 14 September 2026), the SLA (effective 23 April 2026) and the special note on third-party deployed models (effective 14 September 2026) name 腾讯云计算（北京）有限责任公司. The docs footer also names 深圳市腾讯计算机系统有限公司 as ICP licence holder.", "The TokenHub privacy policy is linked from the docs to privacy.qq.com, which renders only with JavaScript, so it was not read. privacy.qq.com has no robots.txt (404).", "No public status page was found. status.cloud.tencent.com did not answer, and the Tencent Cloud health dashboard documentation answered with a bot check.", "security.txt answered 404 on cloud.tencent.com, www.tencent.com and tokenhub.tencentmaas.com. Tencent's bug bounty policy at en.security.tencent.com lists Tencent Cloud in scope.", "RDAP at Verisign gives tencentmaas.com a registration date of 13 March 2026 with no registrant. The API answers at tokenhub.tencentmaas.com and tokenhub-intl.tencentmaas.com, named in Tencent Cloud's API usage page.", "cloud.tencent.com allows these docs in robots.txt. Its pages answered some requests with a JavaScript bot check, which was not passed. The pages were read when a later plain request returned them. www.tencentcloud.com answered robots.txt with a bot check, so the international site was not read." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy4.json", "live": { "slug": "hy4", "securityTxt": { "url": "https://tencentmaas.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-13T16:16:35.489580858Z" }, "llmsTxt": { "url": "https://cloud.tencent.com/document/product/1823/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:54:34.939672287Z" }, "pages": [ { "url": "https://cloud.tencent.com/document/product/1823/130675", "kind": "changelog", "status": 200, "checkedAt": "2026-11-13T18:11:34.856577424Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" }, { "url": "https://privacy.qq.com/document/preview/91d5fd3c98ce4468ae462ac59233bba2", "kind": "privacy", "status": 304, "checkedAt": "2026-11-13T18:28:00.062507799Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" }, { "url": "https://cloud.tencent.com/document/product/301/129852", "kind": "terms", "status": 200, "checkedAt": "2026-11-13T18:11:36.909670614Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" } ], "updatedAt": "2026-11-13T18:28:00.062507799Z" } }, "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 91.2 (AA) on agent readiness against Tencent Hy4's 64 (B), 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": 91.2, "grade": "AA", "agentReady": true, "rank": 2, "ranked": true, "rankOf": 1524, "categoryRank": 1, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 97, "maintenance": 91, "payments": 60, "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": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 91.2 } ], "editorialScores": { "ergonomics": 97, "maintenance": 91, "payments": 60, "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", "lastMoved": "2026-11-13", "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-4-carbon", "gemini-4-hydrogen", "gemini-api", "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", "gemini-decision-1", "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" ], "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": "2026-11-13T23:59:07.457397837Z", "lastOk": true, "lastStatus": 404, "lastMs": 116, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 121, "p95ms24h": 690, "samples24h": 239, "samples30d": 1713, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 243, "ok": 243 }, { "date": "2026-11-10", "probes": 244, "ok": 244 }, { "date": "2026-11-11", "probes": 244, "ok": 244 }, { "date": "2026-11-12", "probes": 245, "ok": 245 }, { "date": "2026-11-13", "probes": 238, "ok": 238 } ], "since": "2026-11-06T00:00:00Z" }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.3.0", "released": "2026-11-12", "seenAt": "2026-11-13T09:25:41.025445079Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.3.0", "seenAt": "2026-11-13T09:25:39.130861365Z" }, { "registry": "pypi", "name": "google-cloud-aiplatform", "version": "2.9.0", "released": "2026-11-12", "seenAt": "2026-11-13T09:25:39.381652986Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.3.0", "released": "2026-11-12", "seenAt": "2026-11-13T09:25:39.005459972Z" } ], "githubStars": 4066, "npmWeekly": 30303311, "pypiWeekly": 38621828, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-13T16:15:44.621683591Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-11-08T15:01:59.545325134Z" }, "pages": [ { "url": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-13T18:11:18.206567094Z", "changedAt": "2026-11-13T18:11:18.206567094Z", "fingerprint": "d95c1f03351f" } ], "github": { "repo": "googleapis/python-genai", "checkedAt": "2026-11-09T09:47:04.856295504Z", "stars": 4058, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-08T20:32:42Z", "openIssues": 224, "openPRs": 170, "issuesOpened90d": 83, "issuesClosed90d": 49, "prsMerged90d": 219, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-11-06T02:02:46Z" }, "replies": { "sampled": 25, "unanswered": 9, "medianDays": 2.6 } }, "updatedAt": "2026-11-13T23:59:07.457397837Z" }, "streak": { "days": 8, "from": "2026-11-06", "sinceWatching": true, "last": { "kind": "shutdown", "date": "2026-10-20", "what": "`gemini-2.5-pro`, `gemini-2.5-flash` and `gemini-2.5-flash-lite` retire", "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/model-versions" }, "next": { "kind": "shutdown", "date": "2026-11-19", "what": "`gemini-3.6-flash` retires", "source": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/model-versions" }, "best": 8 } }, "edges": [ { "by": 53, "category": "reliability", "leads": "vertex-ai", "name": "Reliability", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No public status page (0) and so no incident record to read (5 of 30). Per-model rate limits only in the console and DescribeModelQuota (8 of 15). Six 429 codes, Retry-After and backoff advice (13 of 15). The TokenHub SLA promises 99.5% a month, and Hy4 is Tencent's own model, so the third-party exclusion doesn't apply (10). Released as hy4 on 4 November, a separate id from hy4-preview, with no preview label (10). 0 + 5 + 8 + 13 + 10 + 10 = 46.", "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": 46, "b": 99 } }, { "by": 38, "category": "transparency", "leads": "vertex-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service under the TokenHub terms, but Tencent's release headline calls Hy4 open source while the weights and system card are only promised 'in the coming days', with no licence stated, and tencent/Hy4 didn't answer on Hugging Face on 11 November (14 of 30). Terms and FAQ agree inputs aren't used for training; the privacy policy renders only with JavaScript and wasn't read (15 of 30). Dated retirement notices and shutdown fields (13 of 20). Processing generally in mainland China for the China site, Singapore for the international one (13 of 20). Every benchmark in the release is Tencent's own or a partner's. 14 + 15 + 13 + 13 = 55.", "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": 26, "category": "security", "leads": "vertex-ai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Bearer keys limited by model, service, IP range and token quota, long-lived with no rotation (26 of 30). The terms say inputs aren't used to train Tencent Cloud's own models, and Hy4 is first party, so no third party's terms apply (14 of 20). Retention only as long as the service needs, no periods (7 of 15). Per-call audit logs and optional full logs to the customer's CLS (14 of 15). No security.txt; Tencent's TSRC bounty covers Tencent Cloud; no certification checked (10 of 20). 26 + 14 + 7 + 14 + 10 = 71.", "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": 71, "b": 97 } }, { "by": 21, "category": "schema", "leads": "vertex-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No OpenAPI file for inference; field pages for the OpenAI and Anthropic formats as TokenHub implements them (12 of 25). llms.txt with Markdown copies (10). The model list, updated 9 November, gives hy4's context, input and output limits and marks thinking, structured output, function calling, caching and image understanding; computer use, which Tencent's release names, isn't marked (13 of 20). Typed field pages (10 of 15). Examples in five languages and an error page of 30 codes (14 of 15). Dated release notes, with hy4 added on 4 November (14 of 15). 12 + 10 + 13 + 10 + 14 + 14 = 73.", "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": 73, "b": 94 } }, { "by": 18, "category": "maintenance", "leads": "vertex-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Added on 4 November 2026 per the release notes; the model list and price page were updated on 9 November (30). Retirement notices of 21 to 33 days, no minimum written (7 of 12). Heavy retirement churn on the platform (3 of 8). Dated release notes and announcements, round-the-clock support (13 of 15). CLI replies not sampled (3 of 10). Control-plane SDKs and CLI, no inference SDK of its own (12 of 15). CLI runtime stated, CI not checked (5 of 10). 30 + 7 + 3 + 13 + 3 + 12 + 5 = 73. The model is a week old, so these lines are TokenHub's habits more than Hy4's record.", "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": 73, "b": 91 } }, { "by": 15, "category": "payments", "leads": "vertex-ai", "name": "Payments \u0026 pricing", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The price page, updated 9 November, lists ¥6 input, ¥0.3 cached and ¥18 output per million tokens, with Hy4 billed at 50% until 31 December 2026 (20). TokenHub's campaign of 1M free tokens per language model, read on 1 November, isn't confirmed for hy4 on the price page, so half (10 of 20, a judgement call). A person signs up with real-name verification (0). 0 + 20 + 10 + 0 = 30. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 30 is 45.", "b": "No x402, MPP or L402 (0). Per-token prices per model and option published without a login (20). Express mode gives new Google Cloud users 90 days free within quotas with no billing details, though it's in Preview and needs a @gmail.com account (20). A person signs up in the console to get the express key (0). Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 40 is 60." }, "scores": { "a": 45, "b": 60 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Tencent (Hy team)", "b": "Google Cloud", "name": "Vendor" }, { "a": "no (local only)", "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": "Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet", "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": "2026-11-04", "b": "2026-11-05", "name": "Last release" }, { "a": "couldn't be read", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "yes", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "20.9M PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 91.2 (AA) on agent readiness against Tencent Hy4's 64 (B), and leads in every scored category.", "question": "Which is better for AI agents, Tencent Hy4 or Vertex AI (Gemini Enterprise Agent Platform)?" }, { "answer": "Tencent Hy4 needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.", "question": "Do Tencent Hy4 and Vertex AI (Gemini Enterprise Agent Platform) need an API key?" }, { "answer": "No hosted endpoint is listed for Tencent Hy4. Vertex AI (Gemini Enterprise Agent Platform) has a hosted endpoint at https://aiplatform.googleapis.com/v1.", "question": "Can an agent call Tencent Hy4 and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.", "slug": "hy4", "watchFor": "Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November" }, { "aheadOn": [ "Reliability, 99 against 46", "Schema \u0026 documentation, 94 against 73", "Agent ergonomics, 97 against 86", "Security \u0026 auth, 97 against 71", "Payments \u0026 pricing, 60 against 45", "Maintenance \u0026 community, 91 against 73", "Transparency \u0026 trust, 90 against 52" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install", "Free to start without a card" ], "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": { "hy4": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 1.8, "from": 62.2, "fromGrade": "B", "to": 64, "toGrade": "B" } ], "vertex-ai": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 2.5, "from": 88.7, "fromGrade": "AA", "to": 91.2, "toGrade": "AA" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hy4.json", "title": "Amazon Bedrock vs Tencent Hy4", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hy4" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-vertex-ai.json", "title": 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Ember", "url": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-muse-spark-open.json", "title": "Tencent Hy4 vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/hy4-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-nemotron-3-5-super-vl.json", "title": "Tencent Hy4 vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/hy4-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-novita-ai.json", "title": "Tencent Hy4 vs Novita AI", "url": "https://www.anchorterminal.com/compare/hy4-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-openai-api.json", "title": "Tencent Hy4 vs OpenAI API", "url": "https://www.anchorterminal.com/compare/hy4-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-openrouter.json", "title": "Tencent Hy4 vs OpenRouter", "url": "https://www.anchorterminal.com/compare/hy4-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-prism-inference.json", "title": "Tencent Hy4 vs Prism Inference", "url": "https://www.anchorterminal.com/compare/hy4-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-ramp-router.json", "title": "Tencent Hy4 vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/hy4-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-reflection-beam.json", "title": "Tencent Hy4 vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/hy4-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-routerplus.json", "title": "Tencent Hy4 vs RouterPlus", "url": "https://www.anchorterminal.com/compare/hy4-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-sambanova.json", "title": "Tencent Hy4 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/hy4-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-siliconflow.json", "title": "Tencent Hy4 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/hy4-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-simplismart.json", "title": "Tencent Hy4 vs Simplismart", "url": "https://www.anchorterminal.com/compare/hy4-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-stepfun-api.json", "title": "Tencent Hy4 vs StepFun API", "url": "https://www.anchorterminal.com/compare/hy4-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-swe-2-flash.json", "title": "Tencent Hy4 vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/hy4-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-tencent-tokenhub.json", "title": "Tencent Hy4 vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/hy4-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-together-inference.json", "title": "Tencent Hy4 vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/hy4-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-vercel-ai-gateway.json", "title": "Tencent Hy4 vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/hy4-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-xai-api.json", "title": "Tencent Hy4 vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/hy4-vs-xai-api" }, { "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-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-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-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-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "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-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-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-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-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-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-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-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-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-vertex-ai.json", "title": "SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai" }, { "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-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": 53, "edge": "vertex-ai", "hy4": 46, "key": "reliability", "name": "Reliability", "vertex-ai": 99, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 21, "edge": "vertex-ai", "hy4": 73, "key": "schema", "name": "Schema \u0026 documentation", "vertex-ai": 94, "weight": 13 }, { "by": 11, "edge": "vertex-ai", "hy4": 86, "key": "ergonomics", "name": "Agent ergonomics", "vertex-ai": 97, "weight": 13 }, { "by": 26, "edge": "vertex-ai", "hy4": 71, "key": "security", "name": "Security \u0026 auth", "vertex-ai": 97, "weight": 14 }, { "by": 15, "edge": "vertex-ai", "hy4": 45, "key": "payments", "name": "Payments \u0026 pricing", "vertex-ai": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 18, "edge": "vertex-ai", "hy4": 73, "key": "maintenance", "name": "Maintenance \u0026 community", "vertex-ai": 91, "weight": 7 }, { "by": 38, "edge": "vertex-ai", "hy4": 52, "key": "transparency", "name": "Transparency \u0026 trust", "vertex-ai": 90, "weight": 7 } ], "summary": "Vertex AI (Gemini Enterprise Agent Platform) scores 91.2 (AA) on agent readiness against Tencent Hy4's 64 (B), and leads in every scored category. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "hy4": "Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.", "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/hy4-vs-vertex-ai", "json": "https://www.anchorterminal.com/compare/hy4-vs-vertex-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy4-vs-vertex-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy4-vs-vertex-ai.min.md" }, "markdown": "Vertex AI (Gemini Enterprise Agent Platform) scores 91.2 (AA) on agent readiness against Tencent Hy4's 64 (B), and leads in every scored category. Both do llm inference.\n\n- Tencent Hy4: grade B, 64/100, rank #595 of 1524. Markdown https://www.anchorterminal.com/tools/hy4.md · JSON https://www.anchorterminal.com/api/v1/tools/hy4.json\n- Vertex AI (Gemini Enterprise Agent Platform): grade AA, 91.2/100, rank #2 of 1524. 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 1044 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Tencent Hy4 (B)\n\nGood for: Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.\n\nWatch for: Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November\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 46\n- Schema \u0026 documentation, 94 against 73\n- Agent ergonomics, 97 against 86\n- Security \u0026 auth, 97 against 71\n- Payments \u0026 pricing, 60 against 45\n- Maintenance \u0026 community, 91 against 73\n- Transparency \u0026 trust, 90 against 52\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n- Free to start without a card\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) +53\n\n- Tencent Hy4 (46): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No public status page (0) and so no incident record to read (5 of 30). Per-model rate limits only in the console and DescribeModelQuota (8 of 15). Six 429 codes, Retry-After and backoff advice (13 of 15). The TokenHub SLA promises 99.5% a month, and Hy4 is Tencent's own model, so the third-party exclusion doesn't apply (10). Released as hy4 on 4 November, a separate id from hy4-preview, with no preview label (10). 0 + 5 + 8 + 13 + 10 + 10 = 46.\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### Transparency \u0026 trust: Vertex AI (Gemini Enterprise Agent Platform) +38\n\n- Tencent Hy4 (52): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service under the TokenHub terms, but Tencent's release headline calls Hy4 open source while the weights and system card are only promised 'in the coming days', with no licence stated, and tencent/Hy4 didn't answer on Hugging Face on 11 November (14 of 30). Terms and FAQ agree inputs aren't used for training; the privacy policy renders only with JavaScript and wasn't read (15 of 30). Dated retirement notices and shutdown fields (13 of 20). Processing generally in mainland China for the China site, Singapore for the international one (13 of 20). Every benchmark in the release is Tencent's own or a partner's. 14 + 15 + 13 + 13 = 55.\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### Security \u0026 auth: Vertex AI (Gemini Enterprise Agent Platform) +26\n\n- Tencent Hy4 (71): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Bearer keys limited by model, service, IP range and token quota, long-lived with no rotation (26 of 30). The terms say inputs aren't used to train Tencent Cloud's own models, and Hy4 is first party, so no third party's terms apply (14 of 20). Retention only as long as the service needs, no periods (7 of 15). Per-call audit logs and optional full logs to the customer's CLS (14 of 15). No security.txt; Tencent's TSRC bounty covers Tencent Cloud; no certification checked (10 of 20). 26 + 14 + 7 + 14 + 10 = 71.\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### Schema \u0026 documentation: Vertex AI (Gemini Enterprise Agent Platform) +21\n\n- Tencent Hy4 (73): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No OpenAPI file for inference; field pages for the OpenAI and Anthropic formats as TokenHub implements them (12 of 25). llms.txt with Markdown copies (10). The model list, updated 9 November, gives hy4's context, input and output limits and marks thinking, structured output, function calling, caching and image understanding; computer use, which Tencent's release names, isn't marked (13 of 20). Typed field pages (10 of 15). Examples in five languages and an error page of 30 codes (14 of 15). Dated release notes, with hy4 added on 4 November (14 of 15). 12 + 10 + 13 + 10 + 14 + 14 = 73.\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### Maintenance \u0026 community: Vertex AI (Gemini Enterprise Agent Platform) +18\n\n- Tencent Hy4 (73): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Added on 4 November 2026 per the release notes; the model list and price page were updated on 9 November (30). Retirement notices of 21 to 33 days, no minimum written (7 of 12). Heavy retirement churn on the platform (3 of 8). Dated release notes and announcements, round-the-clock support (13 of 15). CLI replies not sampled (3 of 10). Control-plane SDKs and CLI, no inference SDK of its own (12 of 15). CLI runtime stated, CI not checked (5 of 10). 30 + 7 + 3 + 13 + 3 + 12 + 5 = 73. The model is a week old, so these lines are TokenHub's habits more than Hy4's record.\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### Payments \u0026 pricing: Vertex AI (Gemini Enterprise Agent Platform) +15\n\n- Tencent Hy4 (45): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The price page, updated 9 November, lists ¥6 input, ¥0.3 cached and ¥18 output per million tokens, with Hy4 billed at 50% until 31 December 2026 (20). TokenHub's campaign of 1M free tokens per language model, read on 1 November, isn't confirmed for hy4 on the price page, so half (10 of 20, a judgement call). A person signs up with real-name verification (0). 0 + 20 + 10 + 0 = 30. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 30 is 45.\n- Vertex AI (Gemini Enterprise Agent Platform) (60): No x402, MPP or L402 (0). Per-token prices per model and option published without a login (20). Express mode gives new Google Cloud users 90 days free within quotas with no billing details, though it's in Preview and needs a @gmail.com account (20). A person signs up in the console to get the express key (0). Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 40 is 60.\n\n## Score by category\n\n| Category | Weight | Tencent Hy4 | Vertex AI (Gemini Enterprise Agent Platform) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 46 | 99 | Vertex AI (Gemini Enterprise Agent Platform) +53 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 94 | Vertex AI (Gemini Enterprise Agent Platform) +21 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +11 |\n| Security \u0026 auth | 14% (17.5 this run) | 71 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +26 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 45 | 60 | Vertex AI (Gemini Enterprise Agent Platform) +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 73 | 91 | Vertex AI (Gemini Enterprise Agent Platform) +18 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 52 | 90 | Vertex AI (Gemini Enterprise Agent Platform) +38 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64 · B** | **91.2 · AA** | |\n\n## Facts side by side\n\n| Fact | Tencent Hy4 | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Tencent (Hy team) | Google Cloud |\n| Hosted endpoint | no (local only) | `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 | Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet | 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 | 2026-11-04 | 2026-11-05 |\n| Terms last updated | couldn't be read | 2026-09-02 |\n| Privacy policy last updated | couldn't be read | 2026-10-01 |\n| Customer content may train models | couldn't be read | yes |\n| Terms restrict automated access | couldn't be read | not found in the text |\n| Terms restrict benchmarking | couldn't be read | not found in the text |\n| Terms or service can change without notice | couldn't be read | not found in the text |\n| Arbitration or class-action waiver | couldn't be read | not found in the text |\n| Popularity | none | 20.9M PyPI/wk |\n\n## How the scores have moved\n\n### Tencent Hy4\n\n- +1.8 on 13 November 2026: checklist\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\n- +2.5 on 13 November 2026: checklist\n\n## Verdicts\n\n**Tencent Hy4.** Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.\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### Tencent Hy4\n\n1. Use model id hy4 on TokenHub's OpenAI- or Anthropic-compatible endpoints; hy4-preview is a separate, older model\n2. Keep input under 960K tokens and expect at most 64K output\n3. Reuse prompt prefixes: cached input is ¥0.3 against ¥6\n4. Check the price page for the end of the 50% discount on 31 December 2026 before budgeting\n5. Read rate limits from the console or DescribeModelQuota; the public docs don't give them for chat models\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, Tencent Hy4 or Vertex AI (Gemini Enterprise Agent Platform)?\n\nVertex AI (Gemini Enterprise Agent Platform) scores 91.2 (AA) on agent readiness against Tencent Hy4's 64 (B), and leads in every scored category.\n\n### Do Tencent Hy4 and Vertex AI (Gemini Enterprise Agent Platform) need an API key?\n\nTencent Hy4 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 Tencent Hy4 and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?\n\nNo hosted endpoint is listed for Tencent Hy4. Vertex AI (Gemini Enterprise Agent Platform) has a hosted endpoint at https://aiplatform.googleapis.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy4-vs-vertex-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy4-vs-vertex-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy4\", \"b\": \"vertex-ai\"}`. From a terminal: `anchor compare hy4 vertex-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy4.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n\n## Other comparisons with Tencent Hy4 or Vertex AI (Gemini Enterprise Agent Platform)\n\n- [Amazon Bedrock vs Tencent Hy4](https://www.anchorterminal.com/compare/amazon-bedrock-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/anthropic-api-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/antseed-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/azure-openai-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/blockrun-ai-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/cerebras-inference-vs-hy4.md)\n- [Cerebras Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cerebras-inference-vs-vertex-ai.md)\n- [Claude Fable 5.2 vs Tencent Hy4](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hy4.md)\n- [Claude Fable 5.2 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vertex-ai.md)\n- [Claude Mythos 5.2 vs Tencent Hy4](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-hy4.md)\n- [Claude Mythos 5.2 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-vertex-ai.md)\n- [Cloudflare AI Gateway vs Tencent Hy4](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-hy4.md)\n- [Cloudflare AI Gateway vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-vertex-ai.md)\n- [Cloudflare Workers AI vs Tencent Hy4](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-hy4.md)\n- [Cloudflare Workers AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-vertex-ai.md)\n- [Cohere Chat API (Command models) vs Tencent Hy4](https://www.anchorterminal.com/compare/cohere-chat-vs-hy4.md)\n- [Cohere Chat API (Command models) vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/cohere-chat-vs-vertex-ai.md)\n- [DeepInfra vs Tencent Hy4](https://www.anchorterminal.com/compare/deepinfra-vs-hy4.md)\n- [DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.md)\n- [DeepSeek API vs Tencent Hy4](https://www.anchorterminal.com/compare/deepseek-api-vs-hy4.md)\n- [DeepSeek API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/deepseek-api-vs-vertex-ai.md)\n- [Eden AI vs Tencent Hy4](https://www.anchorterminal.com/compare/eden-ai-vs-hy4.md)\n- [Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.md)\n- [Glimpse vs Tencent Hy4](https://www.anchorterminal.com/compare/elorian-glimpse-vs-hy4.md)\n- [Glimpse vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/elorian-glimpse-vs-vertex-ai.md)\n- [Fireworks AI Inference vs Tencent Hy4](https://www.anchorterminal.com/compare/fireworks-inference-vs-hy4.md)\n- [Fireworks AI Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/fireworks-inference-vs-vertex-ai.md)\n- [Gemini 4 Carbon vs Tencent Hy4](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-hy4.md)\n- [Gemini 4 Carbon vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vertex-ai.md)\n- [Gemini 4 Hydrogen vs Tencent Hy4](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hy4.md)\n- [Gemini 4 Hydrogen vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vertex-ai.md)\n- [Gemini Developer API vs Tencent Hy4](https://www.anchorterminal.com/compare/gemini-api-vs-hy4.md)\n- [Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.md)\n- [GroqCloud vs Tencent Hy4](https://www.anchorterminal.com/compare/groq-vs-hy4.md)\n- [GroqCloud vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/groq-vs-vertex-ai.md)\n- [Hugging Face Inference Providers vs Tencent Hy4](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-hy4.md)\n- [Hugging Face Inference Providers vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vertex-ai.md)\n- [Tencent Hy4 vs Jais 3](https://www.anchorterminal.com/compare/hy4-vs-jais-3.md)\n- [Tencent Hy4 vs Meta Model API](https://www.anchorterminal.com/compare/hy4-vs-meta-model-api.md)\n- [Tencent Hy4 vs MiniMax API](https://www.anchorterminal.com/compare/hy4-vs-minimax-api.md)\n- [Tencent Hy4 vs Mistral AI API](https://www.anchorterminal.com/compare/hy4-vs-mistral-api.md)\n- [Tencent Hy4 vs Muse Ember](https://www.anchorterminal.com/compare/hy4-vs-muse-ember.md)\n- [Tencent Hy4 vs Muse Spark Open](https://www.anchorterminal.com/compare/hy4-vs-muse-spark-open.md)\n- [Tencent Hy4 vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/hy4-vs-nemotron-3-5-super-vl.md)\n- [Tencent Hy4 vs Novita AI](https://www.anchorterminal.com/compare/hy4-vs-novita-ai.md)\n- [Tencent Hy4 vs OpenAI API](https://www.anchorterminal.com/compare/hy4-vs-openai-api.md)\n- [Tencent Hy4 vs OpenRouter](https://www.anchorterminal.com/compare/hy4-vs-openrouter.md)\n- [Tencent Hy4 vs Prism Inference](https://www.anchorterminal.com/compare/hy4-vs-prism-inference.md)\n- [Tencent Hy4 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/hy4-vs-ramp-router.md)\n- [Tencent Hy4 vs Beam 1.0](https://www.anchorterminal.com/compare/hy4-vs-reflection-beam.md)\n- [Tencent Hy4 vs RouterPlus](https://www.anchorterminal.com/compare/hy4-vs-routerplus.md)\n- [Tencent Hy4 vs SambaCloud](https://www.anchorterminal.com/compare/hy4-vs-sambanova.md)\n- [Tencent Hy4 vs SiliconFlow](https://www.anchorterminal.com/compare/hy4-vs-siliconflow.md)\n- [Tencent Hy4 vs Simplismart](https://www.anchorterminal.com/compare/hy4-vs-simplismart.md)\n- [Tencent Hy4 vs StepFun API](https://www.anchorterminal.com/compare/hy4-vs-stepfun-api.md)\n- [Tencent Hy4 vs SWE-2 Flash](https://www.anchorterminal.com/compare/hy4-vs-swe-2-flash.md)\n- [Tencent Hy4 vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/hy4-vs-tencent-tokenhub.md)\n- [Tencent Hy4 vs Together AI Inference](https://www.anchorterminal.com/compare/hy4-vs-together-inference.md)\n- [Tencent Hy4 vs Vercel AI Gateway](https://www.anchorterminal.com/compare/hy4-vs-vercel-ai-gateway.md)\n- [Tencent Hy4 vs xAI API (Grok)](https://www.anchorterminal.com/compare/hy4-vs-xai-api.md)\n- [Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.md)\n- [Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.md)\n- [MiniMax API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai.md)\n- [Mistral AI API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.md)\n- [Novita AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/novita-ai-vs-vertex-ai.md)\n- [OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.md)\n- [OpenRouter vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/openrouter-vs-vertex-ai.md)\n- [Prism Inference vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/prism-inference-vs-vertex-ai.md)\n- [Router.com (Ramp Router) vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/ramp-router-vs-vertex-ai.md)\n- [Beam 1.0 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai.md)\n- [RouterPlus vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/routerplus-vs-vertex-ai.md)\n- [SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.md)\n- [SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.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 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": "2026-11-14", "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": "Tencent Hy4 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "" } ], "description": "Vertex AI scores 91.2 (AA) to Tencent Hy4's 64 (B) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Tencent Hy4 B 64", "Vertex AI (Gemini Enterprise Agent Platform) AA 91.2", "scores" ], "h1": "Tencent Hy4 vs Vertex AI (Gemini Enterprise Agent Platform)", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/hy4-vs-vertex-ai", "published": "2026-10-01", "section": "tools", "title": "Tencent Hy4 vs Vertex AI for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/hy4-vs-vertex-ai" }, "tokens": { "markdown": 7450, "slim": 1180 }, "version": 1 }