{ "data": { "a": { "slug": "mistral-api", "name": "Mistral AI API", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "model", "category": "inference", "summary": "Mistral's API for its open-weight and proprietary models, with EU and US regional endpoints.", "url": "https://www.anchorterminal.com/tools/mistral-api", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-api.json", "dns": "mistral-api.grade.anchorterminal.com", "repo": "https://github.com/mistralai/client-python", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.mistral.ai/v1", "packages": [ { "registry": "pypi", "name": "mistralai", "main": true }, { "registry": "npm", "name": "@mistralai/mistralai", "main": true } ], "auth": "api-key", "authNotes": "Bearer key. Regional EU and US endpoints are opt-in.", "pricing": "freemium", "pricingNotes": "Free Experiment tier with no card, but a phone number, and its data may be used for training. Cached input 10% of the input price, batch half price, regional endpoints 1.1x. Mistral Large 4 (preview) is $0.70/$2.10 per million tokens, cached input $0.07, on 1 November. Also resells Z.ai GLM 5.3 at $1.40/$4.40, GLM 5.3 Flash at $0.15/$0.50 and DeepSeek V4.1 Flash 1017 (preview) at $0.30/$1.20 (https://mistral.ai/pricing/api/).", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 769, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://docs.mistral.ai", "rateLimitsUrl": "https://docs.mistral.ai/admin/user-management-finops/tier", "llmsTxt": "https://docs.mistral.ai/llms.txt", "openapi": "https://docs.mistral.ai/openapi.yaml", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "eu", "open-weights", "free-tier", "openapi", "llms-txt" ], "lastRelease": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 65.7, "grade": "B", "agentReady": false, "rank": 405, "ranked": true, "rankOf": 1486, "categoryRank": 16, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 91, "maintenance": 80, "payments": 40, "reliability": 50, "schema": 93, "security": 73, "transparency": 80 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -6, "negativeNotes": [ "2026-05-12. The `mistralai` 2.4.6 package on PyPI, one of this listing's two official SDKs, ran a credential-harvesting script on import for about three hours, and `@mistralai/mistralai` 2.2.2 to 2.2.4 on npm were replaced too. Mistral published advisory MAI-2026-002, removed the packages and closed its investigation on 14 May. Fixed and documented, so 3 points, as mistral-voxtral-transcribe carries for the same advisory (https://docs.mistral.ai/resources/security-advisories/MAI-2026-002). -3", "2026-10-20 to 2026-10-27. Minor releases of both official SDKs list \"Breaking changes\" with no advance notice found. Python 3.3.0 (20 October) and TypeScript 3.1.0 change the `type` field in `chat.complete()` and `agents.complete()` and remove `expressiveness` and `phoneme_hints` from audio agent configuration. Python 3.4.0 (23 October) makes `configuration` required on `audio.agents.create()` and renames `models.Format` to `FormatEnum`. Python 3.6.0 and TypeScript 3.4.0 (27 October) remove usage fields from speech responses. One term at the floor, as svix's several breaking minors were read (https://github.com/mistralai/client-python/blob/main/RELEASES.md). -3" ], "verdict": "Public OpenAPI document and an llms.txt with Markdown twins. Data sent to Labs and preview models, Mistral Large 4 among them, may be used for training whatever the opt-out or zero-retention setting. The default model, Medium 3.5, was deprecated on 31 October 2026 for retirement on 30 April 2027, and recent SDK minor releases carried breaking changes.", "bestFor": "Teams that need EU processing, open-weight models they can later run themselves, or an API an agent can read from an OpenAPI file.", "strengths": [ "Public OpenAPI 3.1.0 document at docs.mistral.ai/openapi.yaml and an llms.txt with Markdown twins", "Minimum 6 months' notice before a GA model retires, kept for Medium 3.5 and Large 3, and retired ids return 404", "Workspace-scoped API keys with expiry dates, and service accounts bound to workspace roles", "Three dated security advisories published in the docs in 2026, with affected versions and remediation", "Free Experiment tier with no card, and Batch at half price" ], "weaknesses": [ "Labs and preview models, Large 4 among them, may train on data sent from 2026-09-25, whatever the opt-out or zero-retention setting", "Mistral Medium 3 and 3.1, both GA, retired on 31 August 2026 after 101 days' notice against a published 6-month minimum", "SDK minor releases from 20 to 27 October 2026 removed and changed fields, marked breaking on release day", "The `mistralai` 2.4.6 PyPI release of 12 May 2026 harvested credentials on import for about three hours", "Rate-limit numbers are only in the console, no SLA found, and the status page blocks automated readers" ], "agentNotes": [ "Stay off `labs-*` and preview models, `mistral-large-4` included while it is in preview, for anything confidential", "Plan a move off `mistral-medium-3-5` and `mistral-large-2512` before 30 April 2027. `mistral-medium-latest` still points to Medium 3.5", "Pin each SDK to one version. Minor releases of `mistralai` and `@mistralai/mistralai` have removed fields without notice", "Treat a 404 on a model id as retirement and read the models page for the replacement", "Set `tool_choice` to any to force a tool call, and `strict` on the JSON schema for structured output" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.7 } ], "editorialScores": { "ergonomics": 91, "maintenance": 80, "payments": 40, "reliability": 50, "schema": 93, "security": 73, "transparency": 68 }, "provenanceScore": 92, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install mistralai # or: npm i @mistralai/mistralai", "http": "curl https://api.mistral.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"mistral-medium-3-5\",\"messages\":[{\"role\":\"user\",\"content\":\"bonjour\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/mistral-api" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli" ], "sameModel": [ "mistral-large-4" ], "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": true, "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service", "privacy": "https://legal.mistral.ai/terms/privacy-policy", "statusPage": "https://status.mistral.ai", "changelog": "https://docs.mistral.ai/resources/changelogs", "securityTxt": "valid", "checked": "2026-11-01", "notes": [ "security.txt read on 2026-11-01, Contact security@mistral.ai, Expires 2027-05-05, Policy a HackerOne submission form", "Commercial terms effective 2026-09-25 and DPA effective 2026-07-27, both re-read on 2026-11-01", "status.mistral.ai answered robots.txt and its pages with a Cloudflare bot check on 2026-11-01" ], "score": 92 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-api.json", "live": { "slug": "mistral-api", "probe": { "target": "https://api.mistral.ai/v1", "method": "get", "lastAt": "2026-11-07T18:20:40.179506462Z", "lastOk": true, "lastStatus": 404, "lastMs": 135, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 73, "p95ms24h": 145, "samples24h": 239, "samples30d": 7910, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "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": 253 }, { "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": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "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": 187, "ok": 187 } ] }, "vendorStatus": { "page": "https://status.mistral.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T18:21:25.061909221Z" }, "versions": [ { "registry": "github", "name": "mistralai/client-python", "version": "v3.7.0", "released": "2026-11-01", "seenAt": "2026-11-06T23:46:16.644469996Z" }, { "registry": "npm", "name": "@mistralai/mistralai", "version": "3.5.0", "seenAt": "2026-11-06T23:46:15.214784483Z" }, { "registry": "pypi", "name": "mistralai", "version": "3.7.0", "released": "2026-11-01", "seenAt": "2026-11-06T23:46:15.034450618Z" } ], "githubStars": 773, "npmWeekly": 6411114, "pypiWeekly": 3498549, "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-11-07T16:00:05.953035328Z" }, "llmsTxt": { "url": "https://docs.mistral.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:07.713253236Z" }, "domain": { "domain": "mistral.ai", "registered": "2019-05-15", "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai", "checkedAt": "2026-11-01T14:21:43.304217994Z" }, "pages": [ { "url": "https://docs.mistral.ai/models.md", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:07:06.93917887Z", "changedAt": "2026-11-06T13:29:25.955541081Z", "fingerprint": "47c11be3c457" }, { "url": "https://docs.mistral.ai/resources/changelogs", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:07:14.906275127Z", "changedAt": "2026-10-23T01:45:57.577340637Z", "fingerprint": "a6e40e62ddee" }, { "url": "https://legal.mistral.ai/terms/commercial-terms-of-service", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:12:59.466425194Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "227daf1e9929" }, { "url": "https://raw.githubusercontent.com/mistralai/client-python/main/RELEASES.md", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:21:47.957091397Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "5c6b09386d51" }, { "url": "https://mistral.ai/pricing/api/", "kind": "pricing", "status": 304, "checkedAt": "2026-11-07T14:14:23.157989409Z", "changedAt": "2026-11-05T12:37:30.141983295Z", "fingerprint": "ebe78a915333" }, { "url": "https://legal.mistral.ai/terms/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:13:01.891707132Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e74211012ef6" } ], "models": { "ministral-3b-2512": { "openrouterId": "mistralai/ministral-3b-2512", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": false, "input": [ "image", "text" ], "promptCaching": true, "contextTokens": 131072, "maxOutput": 104857, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "mistral-large-2512": { "openrouterId": "mistralai/mistral-large-2512", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": false, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 262144, "maxOutput": 209715, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "mistral-large-4": { "openrouterId": "mistralai/mistral-large-4-0", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "image", "text" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 262144, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "mistral-medium-3-5": { "openrouterId": "mistralai/mistral-medium-3-5", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": false, "contextTokens": 262144, "maxOutput": 209715, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "mistral-small-2603": { "openrouterId": "mistralai/mistral-small-2603", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "image", "text" ], "promptCaching": true, "contextTokens": 262144, "maxOutput": 209715, "checkedAt": "2026-11-06T23:49:25.225778715Z" } }, "github": { "repo": "mistralai/client-python", "checkedAt": "2026-11-02T09:07:39.340819862Z", "stars": 774, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-01T20:09:32Z", "openIssues": 39, "openPRs": 3, "issuesOpened90d": 20, "issuesClosed90d": 2, "prsMerged90d": 23, "ci": { "branch": "main", "runs": 50, "success": 37, "failure": 13, "other": 0, "latest": "failure", "latestAt": "2026-11-01T20:33:28Z" }, "advisories": [ { "ghsa": "GHSA-wx9m-wx4f-4cmg", "severity": "critical", "published": "2026-05-12T12:57:44Z" } ], "replies": { "sampled": 25, "unanswered": 23, "medianDays": 21.9 } }, "updatedAt": "2026-11-07T18:21:25.061909221Z" } }, "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against Mistral AI API's 65.7 (B), and leads in 6 of 7 scored categories.", "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": 1486, "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": "2026-11-07T18:21:03.779025658Z", "lastOk": true, "lastStatus": 404, "lastMs": 763, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 118, "p95ms24h": 752, "samples24h": 200, "samples30d": 200, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "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": "2026-11-07T18:21:03.779025658Z" } }, "edges": [ { "by": 49, "category": "reliability", "leads": "vertex-ai", "name": "Reliability", "reasons": { "a": "Status page at status.mistral.ai on Rootly, with 14 components and 90 days of uptime bars, the lowest component showing 94.36% on 1 October (20). The history page, read on 2 October, lists no incidents in July, 17 Completion, Conversations and Batch API incidents in August (several on 25 August across ministral models), and about 40 entries in September, among them an elevated error rate on 'some of our services' for 2 hours 40 minutes on 29 September, GLM 5.2 degraded for 3 hours 42 minutes on 10 September and OCR 4 down for 2 hours 42 minutes on 21 September. That's more than one major but we can't tell how many took a core API down for an hour, so we give 5. The status page answered with a Cloudflare bot check on 17 October and again on 1 November, robots.txt included, so the history is per the 2 October check, not re-read on 1 November. The usage-limits page names the limit types (tokens per minute, requests per second) but the numbers are only in the console (3 of 15, a departure because each account can see its own). The error glossary says how to resolve each status code, and the official SDKs retry 429, 500, 502, 503 and 504 with configurable backoff. No Retry-After header confirmed (12 of 15). A Priority Tier for queueing exists, no SLA found (0). Chat completions on GA models is GA (10). 20+5+3+12+0+10 = 50.", "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": 50, "b": 99 } }, { "by": 24, "category": "security", "leads": "vertex-ai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Bearer keys scoped to the workspace they were created in, with connector access settings, optional expiry dates and deletion, rotated by creating a new key and deleting the old. Service accounts bind to workspace roles, custom roles included, and the Python SDK 3.0.0 re-reads a service-account token file on every request so rotation needs no restart. Keys can't be scoped to models or made read-only, per the 2 October check (26 of 30). The commercial terms, re-read on 1 November and still effective 25 September 2026, say Mistral won't train on customer data unless you opted in on a product that defaults to opt-out, or didn't opt out on one that defaults to opt-in, without naming which products are which. Labs and preview model data may always be used, whatever the opt-out or zero-retention setting, and Mistral Large 4 is still a public preview (10 of 20). 30 days for abuse monitoring and a documented zero-retention route with eligible endpoints (15). Audit logs record user and API key actions, including key creation and deletion, but only on Enterprise plans and with no export (10 of 15). security.txt re-read on 1 November, with a security@mistral.ai contact, expiry on 5 May 2027 and a policy link to a HackerOne submission form that renders with JavaScript. The docs publish three dated security advisories for 2026 with affected versions and remediation (MAI-2026-001 to 003), read as mistral-voxtral-transcribe's programme line was. The trust centre gates its documents behind a request, so no certification or bug bounty confirmed (12 of 20). 26+10+15+10+12 = 73.", "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": 73, "b": 97 } }, { "by": 11, "category": "maintenance", "leads": "vertex-ai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. DeepSeek V4.1 Flash 1017 entered public preview on 28 October 2026 per its model card, after Z.ai GLM 5.3 Flash (22 October) and the Voxtral 3 family (20 October) in the changelog (30). Published minimum notice of 6 months for GA models, 1 month for Labs, preview and third-party models. Medium 3.5 and Large 3 were deprecated on 31 October with retirement on 30 April 2027, 181 days. But Mistral Medium 3 (`mistral-medium-2505`) and Medium 3.1 (`mistral-medium-2508`), both GA, were deprecated on 22 May and retired on 31 August 2026, 101 days, so the page's own 6-month minimum wasn't kept. 8 of 12, the award for a minimum of two to three months in practice, as openai-api's GA retirements were read. Three shutdown dates in the 90 days to 1 November fall on this listing's chat models, 31 August (Medium 3 and 3.1), 30 September (Leanstral 1.5, Labs, dated at release) and 31 October (Z.ai GLM 5.2, third-party, 32 days). Two of the three kept the published notice, so 4 of 8, as openai-api was read. The 17 October count missed 31 August. OCR 4.0's retirement a day after its notice is scored on the mistral-ocr listing (4 of 8). Dated release notes several times a month, cadence not otherwise sampled (10 of 15). SDK repo replies not sampled (5 of 10). `mistralai` 3.6.0 and `@mistralai/mistralai` 3.4.0 on 27 October per each repository's RELEASES.md (git tags stop at 3.2.0 and 3.0.0 of 9 October), with a v2 to v3 migration guide (15 of 15). Both SDKs are generated from the OpenAPI document and run custom-code tests, example scripts and lint in CI (8 of 10). 30+8+4+10+5+15+8 = 80.", "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": 80, "b": 91 } }, { "by": 10, "category": "transparency", "leads": "vertex-ai", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service with clear terms, SDKs Apache-2.0, and several models published as open weights. Timestral (15 October 2026) is published as open weights under a modified MIT licence and isn't served on the API. Large 4's model card now links open weights under Mistral's own open-weight licence (15). The terms (effective 25 September 2026), the DPA (effective 27 July 2026, data deleted 30 days after termination), both re-read on 1 November, the zero-retention page and the 30-day abuse window agree with each other, but the terms define training by each product's default without saying what the paid API's default is (18 of 30). Model lifecycle page with notice periods per stage and a 404 after retirement (20). The DPA points to a subprocessor list at trust.mistral.ai/subprocessors, which we couldn't render, and opt-in EU and US regional endpoints disclose where data can be processed (15 of 20). 15+18+20+15 = 68.", "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": 80, "b": 90 } }, { "by": 6, "category": "ergonomics", "leads": "vertex-ai", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors), per the 2 and 12 October checks. Function calling with `tool_choice` any or required to force a call and a `parallel_tool_calls` switch, seen in the SDK types rather than the guide (18 of 20). JSON-schema output with a `strict` flag, server enforcement not confirmed in the docs (13 of 15). Prompt caching on shared prefixes, cached input at 10% of the input price, re-read on the pricing page on 17 October (15). 256,000-token context on Medium 3.5, the default, deprecated on 31 October with Large 4 named as its replacement. Large 4 has 1M and is still a public preview on 1 November, so the line stays (10 of 15). A stateless Responses API (`/v1/responses`) arrived in the 23 October SDKs. Batch at half price (10). Official SDKs for Python and TypeScript (10). Error glossary (15). 18+13+15+10+10+10+15 = 91.", "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": 91, "b": 97 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Mistral AI", "b": "Google Cloud", "name": "Vendor" }, { "a": "https://api.mistral.ai/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": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDKs)", "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": "2026-10-28", "b": "2026-11-05", "name": "Last release" }, { "a": "2026-09-25", "b": "2026-09-02", "name": "Terms last updated" }, { "a": "2026-09-03", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "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": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "769 stars", "b": "20.9M PyPI/wk", "name": "Popularity" }, { "a": "4/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against Mistral AI API's 65.7 (B), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Mistral AI API or Vertex AI (Gemini Enterprise Agent Platform)?" }, { "answer": "Mistral AI API needs an API key. Vertex AI (Gemini Enterprise Agent Platform) takes an API key or an OAuth sign-in.", "question": "Do Mistral AI API and Vertex AI (Gemini Enterprise Agent Platform) need an API key?" }, { "answer": "Yes. Mistral AI API has a hosted endpoint at https://api.mistral.ai/v1 and Vertex AI (Gemini Enterprise Agent Platform) at https://aiplatform.googleapis.com/v1.", "question": "Can an agent call Mistral AI API and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams that need EU processing, open-weight models they can later run themselves, or an API an agent can read from an OpenAPI file.", "slug": "mistral-api", "watchFor": "Labs and preview models, Large 4 among them, may train on data sent from 2026-09-25, whatever the opt-out or zero-retention setting" }, { "aheadOn": [ "Reliability, 99 against 50", "Agent ergonomics, 97 against 91", "Security \u0026 auth, 97 against 73", "Maintenance \u0026 community, 91 against 80", "Transparency \u0026 trust, 90 against 80" ], "also": [ "Agent-ready, a grade of BB or better", "Free to start without a card", "No incidents deducted, where Mistral AI API loses 6 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": { "mistral-api": [ { "causes": [ "correction", "event" ], "date": "2026-11-01", "delta": -5.5, "from": 71.2, "fromGrade": "BB", "to": 65.7, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.1, "from": 71.3, "fromGrade": "BB", "to": 71.2, "toGrade": "BB" } ], "vertex-ai": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-mistral-api.json", "title": "Amazon Bedrock vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-mistral-api" }, { "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-mistral-api.json", "title": "Claude API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-mistral-api" }, { "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-mistral-api.json", "title": "Antseed vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/antseed-vs-mistral-api" }, { "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-mistral-api.json", "title": "Azure OpenAI in Foundry Models vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-mistral-api" }, { "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-mistral-api.json", "title": "BlockRun.AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-mistral-api" }, { "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-mistral-api.json", "title": "Cerebras Inference vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-mistral-api" }, { "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-mistral-api.json", "title": "Cloudflare AI Gateway vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-mistral-api" }, { "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-mistral-api.json", "title": "Cloudflare Workers AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-mistral-api" }, { "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-mistral-api.json", "title": "Cohere Chat API (Command models) vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-mistral-api" }, { "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-mistral-api.json", "title": "DeepInfra vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-mistral-api" }, { "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-mistral-api.json", "title": "DeepSeek API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-mistral-api" }, { "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-mistral-api.json", "title": "Eden AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api" }, { "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-mistral-api.json", "title": "Glimpse vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-mistral-api" }, { "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-mistral-api.json", "title": "Fireworks AI Inference vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-mistral-api" }, { "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-mistral-api.json", "title": "Gemini Developer API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api" }, { "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-mistral-api.json", "title": "GroqCloud vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/groq-vs-mistral-api" }, { "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-mistral-api.json", "title": "Hugging Face Inference Providers vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-mistral-api" }, { "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-mistral-api.json", "title": "Inkling 2 Small vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-api" }, { "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-mistral-api.json", "title": "Jais 3 vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-mistral-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-mistral-api.json", "title": "Meta Model API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api" }, { "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-mistral-api.json", "title": "MiniMax API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-mistral-api" }, { "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-muse-spark-open.json", "title": "Mistral AI API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-nemotron-3-5-super-vl.json", "title": "Mistral AI API vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-novita-ai.json", "title": "Mistral AI API vs Novita AI", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-openai-api.json", "title": "Mistral AI API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-openrouter.json", "title": "Mistral AI API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-prism-inference.json", "title": "Mistral AI API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-ramp-router.json", "title": "Mistral AI API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-reflection-beam.json", "title": "Mistral AI API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-routerplus.json", "title": "Mistral AI API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-sambanova.json", "title": "Mistral AI API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-sambanova" }, { "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-simplismart.json", "title": "Mistral AI API vs Simplismart", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-stepfun-api.json", "title": "Mistral AI API vs StepFun API", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-swe-2-flash.json", "title": "Mistral AI API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-tencent-tokenhub.json", "title": "Mistral AI API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-together-inference.json", "title": "Mistral AI API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-vercel-ai-gateway.json", "title": "Mistral AI API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-xai-api.json", "title": "Mistral AI API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-xai-api" }, { "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": 49, "edge": "vertex-ai", "key": "reliability", "mistral-api": 50, "name": "Reliability", "vertex-ai": 99, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 1, "edge": "vertex-ai", "key": "schema", "mistral-api": 93, "name": "Schema \u0026 documentation", "vertex-ai": 94, "weight": 13 }, { "by": 6, "edge": "vertex-ai", "key": "ergonomics", "mistral-api": 91, "name": "Agent ergonomics", "vertex-ai": 97, "weight": 13 }, { "by": 24, "edge": "vertex-ai", "key": "security", "mistral-api": 73, "name": "Security \u0026 auth", "vertex-ai": 97, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "mistral-api": 40, "name": "Payments \u0026 pricing", "vertex-ai": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 11, "edge": "vertex-ai", "key": "maintenance", "mistral-api": 80, "name": "Maintenance \u0026 community", "vertex-ai": 91, "weight": 7 }, { "by": 10, "edge": "vertex-ai", "key": "transparency", "mistral-api": 80, "name": "Transparency \u0026 trust", "vertex-ai": 90, "weight": 7 } ], "summary": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against Mistral AI API's 65.7 (B), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": [ { "a": "$0.98", "b": "$22.03", "cheaper": "mistral-api", "task": "Research a product and write a sourced report", "what": "mistral-large-4 on Mistral AI API, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.31", "b": "$5.09", "cheaper": "mistral-api", "task": "Build a small four-page website", "what": "mistral-large-4 on Mistral AI API, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" }, { "a": "$0.08", "b": "$1.09", "cheaper": "mistral-api", "task": "Find and fix a bug in a large codebase", "what": "mistral-large-4 on Mistral AI API, gemini-3.1-pro-preview on Vertex AI (Gemini Enterprise Agent Platform)" } ], "tutorials": [], "verdicts": { "mistral-api": "Public OpenAPI document and an llms.txt with Markdown twins. Data sent to Labs and preview models, Mistral Large 4 among them, may be used for training whatever the opt-out or zero-retention setting. The default model, Medium 3.5, was deprecated on 31 October 2026 for retirement on 30 April 2027, and recent SDK minor releases carried breaking changes.", "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/mistral-api-vs-vertex-ai", "json": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.min.md" }, "markdown": "Vertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against Mistral AI API's 65.7 (B), and leads in 6 of 7 scored categories. Both do llm inference.\n\n- Mistral AI API: grade B, 65.7/100, rank #405 of 1486. Markdown https://www.anchorterminal.com/tools/mistral-api.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-api.json\n- Vertex AI (Gemini Enterprise Agent Platform): grade AA, 88.7/100, rank #2 of 1486. 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### Mistral AI API (B)\n\nGood for: Teams that need EU processing, open-weight models they can later run themselves, or an API an agent can read from an OpenAPI file.\n\nWatch for: Labs and preview models, Large 4 among them, may train on data sent from 2026-09-25, whatever the opt-out or zero-retention setting\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 50\n- Agent ergonomics, 97 against 91\n- Security \u0026 auth, 97 against 73\n- Maintenance \u0026 community, 91 against 80\n- Transparency \u0026 trust, 90 against 80\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 Mistral AI API loses 6 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) +49\n\n- Mistral AI API (50): Status page at status.mistral.ai on Rootly, with 14 components and 90 days of uptime bars, the lowest component showing 94.36% on 1 October (20). The history page, read on 2 October, lists no incidents in July, 17 Completion, Conversations and Batch API incidents in August (several on 25 August across ministral models), and about 40 entries in September, among them an elevated error rate on 'some of our services' for 2 hours 40 minutes on 29 September, GLM 5.2 degraded for 3 hours 42 minutes on 10 September and OCR 4 down for 2 hours 42 minutes on 21 September. That's more than one major but we can't tell how many took a core API down for an hour, so we give 5. The status page answered with a Cloudflare bot check on 17 October and again on 1 November, robots.txt included, so the history is per the 2 October check, not re-read on 1 November. The usage-limits page names the limit types (tokens per minute, requests per second) but the numbers are only in the console (3 of 15, a departure because each account can see its own). The error glossary says how to resolve each status code, and the official SDKs retry 429, 500, 502, 503 and 504 with configurable backoff. No Retry-After header confirmed (12 of 15). A Priority Tier for queueing exists, no SLA found (0). Chat completions on GA models is GA (10). 20+5+3+12+0+10 = 50.\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) +24\n\n- Mistral AI API (73): Model reading. Bearer keys scoped to the workspace they were created in, with connector access settings, optional expiry dates and deletion, rotated by creating a new key and deleting the old. Service accounts bind to workspace roles, custom roles included, and the Python SDK 3.0.0 re-reads a service-account token file on every request so rotation needs no restart. Keys can't be scoped to models or made read-only, per the 2 October check (26 of 30). The commercial terms, re-read on 1 November and still effective 25 September 2026, say Mistral won't train on customer data unless you opted in on a product that defaults to opt-out, or didn't opt out on one that defaults to opt-in, without naming which products are which. Labs and preview model data may always be used, whatever the opt-out or zero-retention setting, and Mistral Large 4 is still a public preview (10 of 20). 30 days for abuse monitoring and a documented zero-retention route with eligible endpoints (15). Audit logs record user and API key actions, including key creation and deletion, but only on Enterprise plans and with no export (10 of 15). security.txt re-read on 1 November, with a security@mistral.ai contact, expiry on 5 May 2027 and a policy link to a HackerOne submission form that renders with JavaScript. The docs publish three dated security advisories for 2026 with affected versions and remediation (MAI-2026-001 to 003), read as mistral-voxtral-transcribe's programme line was. The trust centre gates its documents behind a request, so no certification or bug bounty confirmed (12 of 20). 26+10+15+10+12 = 73.\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) +11\n\n- Mistral AI API (80): Model reading. DeepSeek V4.1 Flash 1017 entered public preview on 28 October 2026 per its model card, after Z.ai GLM 5.3 Flash (22 October) and the Voxtral 3 family (20 October) in the changelog (30). Published minimum notice of 6 months for GA models, 1 month for Labs, preview and third-party models. Medium 3.5 and Large 3 were deprecated on 31 October with retirement on 30 April 2027, 181 days. But Mistral Medium 3 (`mistral-medium-2505`) and Medium 3.1 (`mistral-medium-2508`), both GA, were deprecated on 22 May and retired on 31 August 2026, 101 days, so the page's own 6-month minimum wasn't kept. 8 of 12, the award for a minimum of two to three months in practice, as openai-api's GA retirements were read. Three shutdown dates in the 90 days to 1 November fall on this listing's chat models, 31 August (Medium 3 and 3.1), 30 September (Leanstral 1.5, Labs, dated at release) and 31 October (Z.ai GLM 5.2, third-party, 32 days). Two of the three kept the published notice, so 4 of 8, as openai-api was read. The 17 October count missed 31 August. OCR 4.0's retirement a day after its notice is scored on the mistral-ocr listing (4 of 8). Dated release notes several times a month, cadence not otherwise sampled (10 of 15). SDK repo replies not sampled (5 of 10). `mistralai` 3.6.0 and `@mistralai/mistralai` 3.4.0 on 27 October per each repository's RELEASES.md (git tags stop at 3.2.0 and 3.0.0 of 9 October), with a v2 to v3 migration guide (15 of 15). Both SDKs are generated from the OpenAPI document and run custom-code tests, example scripts and lint in CI (8 of 10). 30+8+4+10+5+15+8 = 80.\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) +10\n\n- Mistral AI API (80): Closed service with clear terms, SDKs Apache-2.0, and several models published as open weights. Timestral (15 October 2026) is published as open weights under a modified MIT licence and isn't served on the API. Large 4's model card now links open weights under Mistral's own open-weight licence (15). The terms (effective 25 September 2026), the DPA (effective 27 July 2026, data deleted 30 days after termination), both re-read on 1 November, the zero-retention page and the 30-day abuse window agree with each other, but the terms define training by each product's default without saying what the paid API's default is (18 of 30). Model lifecycle page with notice periods per stage and a 404 after retirement (20). The DPA points to a subprocessor list at trust.mistral.ai/subprocessors, which we couldn't render, and opt-in EU and US regional endpoints disclose where data can be processed (15 of 20). 15+18+20+15 = 68.\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) +6\n\n- Mistral AI API (91): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors), per the 2 and 12 October checks. Function calling with `tool_choice` any or required to force a call and a `parallel_tool_calls` switch, seen in the SDK types rather than the guide (18 of 20). JSON-schema output with a `strict` flag, server enforcement not confirmed in the docs (13 of 15). Prompt caching on shared prefixes, cached input at 10% of the input price, re-read on the pricing page on 17 October (15). 256,000-token context on Medium 3.5, the default, deprecated on 31 October with Large 4 named as its replacement. Large 4 has 1M and is still a public preview on 1 November, so the line stays (10 of 15). A stateless Responses API (`/v1/responses`) arrived in the 23 October SDKs. Batch at half price (10). Official SDKs for Python and TypeScript (10). Error glossary (15). 18+13+15+10+10+10+15 = 91.\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## Score by category\n\n| Category | Weight | Mistral AI API | Vertex AI (Gemini Enterprise Agent Platform) | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 50 | 99 | Vertex AI (Gemini Enterprise Agent Platform) +49 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 93 | 94 | Vertex AI (Gemini Enterprise Agent Platform) +1 |\n| Agent ergonomics | 13% (16.2 this run) | 91 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +6 |\n| Security \u0026 auth | 14% (17.5 this run) | 73 | 97 | Vertex AI (Gemini Enterprise Agent Platform) +24 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 40 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 91 | Vertex AI (Gemini Enterprise Agent Platform) +11 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 80 | 90 | Vertex AI (Gemini Enterprise Agent Platform) +10 |\n| Negative events | ≤15 | -6 | 0 | |\n| **Total** | | **65.7 · B** | **88.7 · AA** | |\n\n## Facts side by side\n\n| Fact | Mistral AI API | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Mistral AI | Google Cloud |\n| Hosted endpoint | `https://api.mistral.ai/v1` | `https://aiplatform.googleapis.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | OAuth or key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | 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 | 2026-10-28 | 2026-11-05 |\n| Terms last updated | 2026-09-25 | 2026-09-02 |\n| Privacy policy last updated | 2026-09-03 | 2026-10-01 |\n| Customer content may train models | yes, with an opt-out | yes |\n| Terms restrict automated access | not found in the text | 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 | not found in the text | not found in the text |\n| Popularity | 769 stars | 20.9M PyPI/wk |\n| Agent reviews | 4/5 (2) | none |\n\n## What the same task costs\n\n| Task | Mistral AI API | Vertex AI (Gemini Enterprise Agent Platform) |\n| --- | --- | --- |\n| Research a product and write a sourced report | $0.98 | $22.03 |\n| Build a small four-page website | $0.31 | $5.09 |\n| Find and fix a bug in a large codebase | $0.08 | $1.09 |\n\n## How the scores have moved\n\n### Mistral AI API\n\n- -5.5 on 1 November 2026 (BB to B): correction, negative event\n- -0.1 on 8 October 2026: checklist\n\n### Vertex AI (Gemini Enterprise Agent Platform)\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Mistral AI API.** Public OpenAPI document and an llms.txt with Markdown twins. Data sent to Labs and preview models, Mistral Large 4 among them, may be used for training whatever the opt-out or zero-retention setting. The default model, Medium 3.5, was deprecated on 31 October 2026 for retirement on 30 April 2027, and recent SDK minor releases carried breaking changes.\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### Mistral AI API\n\n1. Stay off `labs-*` and preview models, `mistral-large-4` included while it is in preview, for anything confidential\n2. Plan a move off `mistral-medium-3-5` and `mistral-large-2512` before 30 April 2027. `mistral-medium-latest` still points to Medium 3.5\n3. Pin each SDK to one version. Minor releases of `mistralai` and `@mistralai/mistralai` have removed fields without notice\n4. Treat a 404 on a model id as retirement and read the models page for the replacement\n5. Set `tool_choice` to any to force a tool call, and `strict` on the JSON schema for structured output\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, Mistral AI API or Vertex AI (Gemini Enterprise Agent Platform)?\n\nVertex AI (Gemini Enterprise Agent Platform) scores 88.7 (AA) on agent readiness against Mistral AI API's 65.7 (B), and leads in 6 of 7 scored categories.\n\n### Do Mistral AI API and Vertex AI (Gemini Enterprise Agent Platform) need an API key?\n\nMistral AI API 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 Mistral AI API and Vertex AI (Gemini Enterprise Agent Platform) without installing anything?\n\nYes. Mistral AI API has a hosted endpoint at https://api.mistral.ai/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/mistral-api-vs-vertex-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-api-vs-vertex-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"mistral-api\", \"b\": \"vertex-ai\"}`. From a terminal: `anchor compare mistral-api vertex-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-api.json and https://www.anchorterminal.com/api/v1/tools/vertex-ai.json\n\n## Other comparisons with Mistral AI API or Vertex AI (Gemini Enterprise Agent Platform)\n\n- [Amazon Bedrock vs Mistral AI API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-mistral-api.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 Mistral AI API](https://www.anchorterminal.com/compare/anthropic-api-vs-mistral-api.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 Mistral AI API](https://www.anchorterminal.com/compare/antseed-vs-mistral-api.md)\n- [Antseed vs Vertex AI (Gemini Enterprise Agent 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