{ "data": { "a": { "slug": "azure-openai", "name": "Azure OpenAI in Foundry Models", "vendor": "Microsoft", "vendorUrl": "https://azure.microsoft.com", "kind": "model", "category": "inference", "summary": "Microsoft Azure's hosted API for OpenAI's GPT models and other Foundry Models sold by Azure, such as DeepSeek, Llama, Mistral and Grok, called through an OpenAI-compatible v1 endpoint on each customer's Azure resource.", "url": "https://www.anchorterminal.com/tools/azure-openai", "markdownUrl": "https://www.anchorterminal.com/tools/azure-openai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/azure-openai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/azure-openai.json", "dns": "azure-openai.grade.anchorterminal.com", "repo": "https://github.com/Azure/azure-rest-api-specs", "license": "Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "packages": [ { "registry": "pypi", "name": "openai" }, { "registry": "npm", "name": "openai" }, { "registry": "npm", "name": "@azure/openai" } ], "auth": "mixed", "authNotes": "`api-key` header with one of the resource's two regenerable keys, or a Microsoft Entra ID bearer token (scope https://ai.azure.com/.default) with the Cognitive Services OpenAI User role. Key authentication can be disabled per resource (`disableLocalAuth`). Each model must be deployed in the resource before it can be called, by deployment name.", "pricing": "usage", "pricingNotes": "Pay per token on an Azure subscription. Global Standard GPT-6 and GPT-6.1 prices match OpenAI's list (GPT-6.1 Sol $2/$10, GPT-6.1 Terra $1/$5 per million tokens), Data Zone costs 10% more, long-context requests cost 2x input and 1.5x output, cached input 0.1x, cache writes 1.25x, Flex half price and priority processing 2x (GPT-6 Sol). Batch is 50% off Global Standard. Provisioned throughput is billed per PTU hour. Prices from the Azure Retail Prices API, because the pricing page fills its table with JavaScript (checked 2026-11-06).", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Foundry docs, the v1 OpenAPI spec or the pricing page. Access needs an Azure subscription with a payment method (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 454039, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://learn.microsoft.com/en-us/azure/foundry/openai/", "rateLimitsUrl": "https://learn.microsoft.com/en-us/azure/foundry/openai/quotas-limits", "openapi": "https://github.com/Azure/azure-rest-api-specs/blob/main/specification/ai/data-plane/OpenAI.v1/azure-v1-v1-generated.json", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "card-required", "openapi", "openai-compatible", "closed-source", "enterprise", "eu", "python", "typescript", "status-page", "bug-bounty" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 80, "grade": "A", "agentReady": true, "rank": 14, "ranked": true, "rankOf": 1486, "categoryRank": 4, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 86, "payments": 20, "reliability": 80, "schema": 87, "security": 95, "transparency": 83 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.", "bestFor": "Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.", "strengths": [ "Public OpenAPI 3.2 spec for the v1 API, 71 paths covering Responses, Chat Completions, batches, files and vector stores", "Microsoft Entra ID tokens with Azure roles, and key authentication can be switched off per resource", "GA models carry a retirement date 18 months after launch, set at release and readable through the Models API", "Data Zone deployments keep processing inside the EU or US, and Azure OpenAI is in FedRAMP High scope", "GPT-6 Astra, Sol and Luna at $10/$50, $2/$10 and $0.10/$0.50 per million tokens on Global Standard" ], "weaknesses": [ "Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026", "An Azure subscription with a card, a resource and a model deployment come before the first call", "No llms.txt, and no Azure OpenAI what's new page was found; the Foundry one still covered August on 6 November", "No retention period stated for prompts kept for human abuse review, and Responses data is kept 30 days by default", "The retirement schedule lists two `gpt-realtime-mini` versions twice with different retirement dates, and no GPT-6 retirement dates yet" ], "agentNotes": [ "Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1/ and pass the deployment name as `model`, not the model id", "Prefer Entra ID with the Cognitive Services OpenAI User role over the resource key, which opens the whole data plane", "On 429 wait for `retry-after-ms`, then back off with jitter. The OpenAI SDK retries twice by default", "Set `store: false` on Responses calls you don't need kept. Stored responses persist 30 days by default", "Keep remote MCP approvals on for servers you don't control. They're required by default" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 80 } ], "editorialScores": { "ergonomics": 98, "maintenance": 86, "payments": 20, "reliability": 80, "schema": 87, "security": 95, "transparency": 77 }, "provenanceScore": 88, "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 openai azure-identity # or: npm i openai @azure/identity", "http": "curl \"https://$AZURE_OPENAI_RESOURCE.openai.azure.com/openai/v1/responses\" \\\n -H \"api-key: $AZURE_OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"\u003cyour-deployment-name\u003e\",\"input\":\"hello\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/azure-openai" }, "sameCompany": [ "azure-foundry-fine-tuning", "azure-ai-content-safety", "azure-speech-to-text", "azure-text-to-speech", "microsoft-agent-framework", "microsoft-execution-containers", "microsoft-entra-agent-id", "azure-key-vault", "bing-grounding", "azure-document-intelligence", "azure-devops-mcp", "microsoft-learn-mcp", "tokeneconomizer", "playwright-mcp", "azure-mcp", "azure-maps", "azure-translator", "microsoft-graph-calendar", "azure-blob-storage", "onedrive-sharepoint", "microsoft-teams", "dynamics-365-sales", "copilot-studio-mcp", "power-automate", "foundry-local", "microsoft-decision-1", "microsoft-advertising-api", "microsoft-excel-graph", "outlook-mail-graph", "microsoft-graph-people", "mage-vl-cowork" ], "area": "models", "unitPrices": [ { "item": "GPT-6.1 Sol input, Global Standard", "unit": "1m-tokens", "usd": 2, "note": "short context, Retail Prices API meter effective 2026-10-01" }, { "item": "GPT-6.1 Sol output, Global Standard", "unit": "1m-tokens", "usd": 10, "note": "short context" }, { "item": "GPT-6.1 Terra input, Global Standard", "unit": "1m-tokens", "usd": 1, "note": "short context" }, { "item": "GPT-6.1 Terra output, Global Standard", "unit": "1m-tokens", "usd": 5, "note": "short context" }, { "item": "GPT-6 Astra input, Global Standard", "unit": "1m-tokens", "usd": 10, "note": "short context" }, { "item": "GPT-6 Astra output, Global Standard", "unit": "1m-tokens", "usd": 50, "note": "short context" } ], "provenance": { "legalEntity": "Microsoft Corporation", "domain": "microsoft.com", "domainRegistered": "1991-05-02", "domainNote": "Endpoints are on openai.azure.com and cognitiveservices.azure.com. microsoft.com publishes a security.txt, but it passed its Expires date on 2026-09-23 and was still expired on 2026-11-06.", "endpointOnVendorDomain": true, "terms": "https://www.microsoft.com/licensing/terms/product/ForOnlineServices/all", "privacy": "https://privacy.microsoft.com/en-us/privacystatement", "statusPage": "https://azure.status.microsoft/en-us/status", "changelog": "https://learn.microsoft.com/en-us/azure/foundry/whats-new-foundry", "securityTxt": "expired", "checked": "2026-11-06", "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/azure-openai.json", "live": { "slug": "azure-openai", "probe": { "target": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "method": "get", "lastAt": "2026-11-07T18:20:12.145420458Z", "lastOk": false, "lastStatus": 0, "lastMs": 0, "lastNote": "DNS lookup failed", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 200, "samples30d": 200, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 0 }, { "date": "2026-11-07", "probes": 187, "ok": 0 } ], "outages": [ { "start": "2026-11-06T22:45:05.888142257Z", "end": "0001-01-01T00:00:00Z", "note": "DNS lookup failed" } ] }, "versions": [ { "registry": "npm", "name": "@azure/openai", "version": "2.0.0", "seenAt": "2026-11-06T23:09:28.988578937Z" }, { "registry": "npm", "name": "openai", "version": "7.53.0", "seenAt": "2026-11-06T23:09:28.954764315Z" }, { "registry": "pypi", "name": "openai", "version": "3.46.0", "released": "2026-11-05", "seenAt": "2026-11-06T23:09:28.844701934Z" } ], "githubStars": 3113, "npmWeekly": 56543040, "pypiWeekly": 91156146, "securityTxt": { "url": "https://microsoft.com/.well-known/security.txt", "state": "valid", "expires": "2027-10-27T16:00:00.000Z", "checkedAt": "2026-11-07T16:02:01.196666056Z" }, "pages": [ { "url": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirement-schedule", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:13:01.613678733Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "51438ff38076" } ], "updatedAt": "2026-11-07T18:20:12.145420458Z" } }, "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments \u0026 pricing.", "b": { "slug": "gemini-api", "name": "Gemini Developer API", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "inference", "summary": "Google's API for Gemini models, including content generation and agent interactions.", "url": "https://www.anchorterminal.com/tools/gemini-api", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-api.json", "dns": "gemini-api.grade.anchorterminal.com", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta", "packages": [ { "registry": "pypi", "name": "google-genai", "main": true }, { "registry": "npm", "name": "@google/genai", "main": true } ], "auth": "api-key", "authNotes": "`x-goog-api-key` header with a key from AI Studio. A Google account is needed to make one.", "pricing": "freemium", "pricingNotes": "Free tier on 3.8 Flash, 3.6 Flash and the Flash-Lite models, not on Gemini 4 Argon or 3.1 Pro preview, and free-tier content is used to improve Google products. Gemini 4 Argon (`gemini-4-argon`) costs $2/$10 per million tokens through 2026-11-30, then $4/$20, with implicit caching only. 3.1 Pro preview costs $2/$12 per million tokens, $4/$18 over 200K. 3.8 Flash is $0.75/$3.75 until 2026-12-31, then $1.50/$7.50. Cached input 0.1x. Nano Banana 2.1 (`gemini-nano-banana-2.1`, images) is paid tier only at $1.50 input and $30 per million image output tokens, about $0.034 a 1K image, $0.050 at 2K and $0.113 at 4K, half that in batch. Search grounding 5,000 free a month, then $14 per 1,000. Batch half price (https://ai.google.dev/gemini-api/docs/pricing).", "priceSummary": "from $0.25 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. The free tier needs a Google account and paid use needs billing on a Cloud project.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3992, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://ai.google.dev/gemini-api/docs", "rateLimitsUrl": "https://ai.google.dev/gemini-api/docs/rate-limits", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "free-tier", "no-card", "llms-txt" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 62.2, "grade": "B", "agentReady": false, "rank": 566, "ranked": true, "rankOf": 1486, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -3, "negativeNotes": [ "2026-10-08. Google deprecated the generally available `gemini-3.7-flash` and `gemini-3.5-flash` and routed API requests for them to `gemini-3.8-flash` and `gemini-3.6-flash` the same day, announced in that day's changelog. Callers pinned to those names got a different model without notice, the reading DeepSeek API takes for its 10 September routing, so 3 points (https://ai.google.dev/gemini-api/docs/changelog, https://ai.google.dev/gemini-api/docs/deprecations)." ], "verdict": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.", "bestFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "strengths": [ "Free tier on 3.8 Flash and Flash-Lite with no billing account", "Gemini 4 Argon went GA on 13 October 2026 with a 1,048,576-token shared window and long decode up to 1M tokens", "Backoff guidance with jitter, and the Python SDK retries transient errors four times", "New keys are bound to a service account and restricted to the Gemini API, and unrestricted standard keys are rejected", "A keyless Discovery document types the v1beta GenerateContent surface, and function calling can be forced to named tools" ], "weaknesses": [ "Free-tier prompts and responses improve Google products and may be read by human reviewers", "The API reads an OAuth access token from the `access_token` query parameter", "No SLA and no zero-retention route found for the Developer API", "Gemini 4 Argon isn't on the free tier and doesn't support explicit context caching", "Shutdown dates are 'earliest possible' with no stated minimum notice, and one preview agent got 15 days" ], "agentNotes": [ "Never send customer data through a free-tier key. Link billing first", "Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL", "Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403", "Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400", "On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.2 } ], "editorialScores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 55 }, "provenanceScore": 89, "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 google-genai # or: npm i @google/genai", "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-3.8-flash:generateContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"contents\":[{\"parts\":[{\"text\":\"hello\"}]}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/gemini-api" }, "sameCompany": [ "vertex-ai", "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" ], "sameModel": [ "gemma-4-26b-a4b-it", "gemma-4-31b-it" ], "area": "models", "unitPrices": [ { "item": "Search grounding over 5,000 a month", "unit": "1k-requests", "usd": 14 }, { "item": "Nano Banana 2.1 image, 1K", "unit": "image", "usd": 0.0336, "note": "1,120 output tokens at $30 per million. Paid tier only, half price in batch" }, { "item": "Nano Banana 2.1 image, 2K", "unit": "image", "usd": 0.0504, "note": "1,680 output tokens" }, { "item": "Nano Banana 2.1 image, 4K", "unit": "image", "usd": 0.113, "note": "3,780 output tokens" } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-11-01", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-api.json", "live": { "slug": "gemini-api", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta", "method": "get", "lastAt": "2026-11-07T18:20:26.848283661Z", "lastOk": true, "lastStatus": 404, "lastMs": 120, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 37, "p95ms24h": 120, "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": 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machine-readable status found", "checkedAt": "2026-11-07T04:47:49.73911967Z" }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.0.0", "released": "2026-11-06", "seenAt": "2026-11-06T23:27:37.766484844Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.0.0", "seenAt": "2026-11-06T23:27:37.49484945Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.0.0", "released": "2026-11-06", "seenAt": "2026-11-06T23:27:37.329000252Z" } ], "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" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:33.34914217Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": 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], "models": { "gemini-3.1-flash-lite": { "openrouterId": "google/gemini-3.1-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.1-pro-preview": { "openrouterId": "google/gemini-3.1-pro-preview", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.5-flash-lite": { "openrouterId": "google/gemini-3.5-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.8-flash": { "openrouterId": "google/gemini-3.8-flash", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-4-argon": { "openrouterId": "google/gemini-4-argon", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 943718, "checkedAt": "2026-11-06T23:49:25.225778715Z" } }, "github": { "repo": "googleapis/python-genai", "checkedAt": "2026-11-02T08:52:22.518653754Z", "stars": 4054, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-31T00:34:25Z", "openIssues": 227, "openPRs": 143, "issuesOpened90d": 78, "issuesClosed90d": 38, "prsMerged90d": 216, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-30T23:58:40Z" }, "replies": { "sampled": 25, "unanswered": 16, "medianDays": 7.5 } }, "updatedAt": "2026-11-07T18:20:26.848283661Z" } }, "edges": [ { "by": 45, "category": "security", "leads": "azure-openai", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Microsoft Entra ID tokens with Azure roles (the Cognitive Services OpenAI User role for calls), managed identities, and key authentication that can be disabled per resource, or two regenerable resource keys in the `api-key` header. No documented route puts the key in a URL query string (30). Prompts, completions, embeddings and training data aren't used to train foundation models without the customer's permission or instruction, per the data privacy page updated 4 August 2026 (20). Flagged prompts and completions may be stored in the customer's geography for human abuse review, with no retention period stated, and approved customers can turn that storage off through modified abuse monitoring. Responses API data is kept 30 days by default unless `store` is false (12 of 15). Azure Monitor resource logs and the subscription activity log, as read in the Foundry fine-tuning dossier (15). MSRC coordinated disclosure policy and Microsoft's bounty programme, both named in microsoft.com's security.txt, and Azure OpenAI is listed in FedRAMP High and DoD IL2 scope in Azure public regions. The security.txt passed its Expires date on 2026-09-23 and was still expired on 6 November, which the Bedrock and S3 dossiers mark down by 2, applied here too (18 of 20, down from 20 on 6 October for consistency).", "b": "Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20)." }, "scores": { "a": 95, "b": 50 } }, { "by": 35, "category": "reliability", "leads": "azure-openai", "name": "Reliability", "reasons": { "a": "Azure status page with post-incident reviews, listing Azure OpenAI Service, Foundry Models and Foundry Agent Service as components (20). One incident in the 90 days to 6 November 2026 names Azure OpenAI, and no review after it does, intermittent request failures, higher latency and HTTP 5xx errors in Sweden Central from 10:03 to 15:58 UTC on 29 September 2026 (tracking 7QL5-Z50), counted as one major outage as in the Foundry fine-tuning dossier. The 23 July West US review and the 30 September gateway incident don't name it (10). Quota tiers from Free to Tier 6 with TPM and RPM per model and deployment type, 1,000 RPM and 1,000,000 TPM for each GPT-6 model on Global Standard at Tier 1 (15). 429 responses carry `retry-after-ms`, every response carries `x-ratelimit-*` headers, and the quota guide gives exponential backoff with jitter and SDK retry settings (15). The models page says models sold by Azure are covered by Azure service-level agreements and links the Online Services SLA. The consolidated SLA document wouldn't download on 6 October and wasn't retried on 6 November, so the clause rests on Microsoft's docs, read the same way as the 5 October Azure Speech check (10). The v1 API is GA and the GPT-6 models carry no preview label (10).", "b": "Status page at aistudio.google.com/status (robots.txt allows it), but on 1 November it still returned a script shell titled Google AI Studio with no components or history, so 10 of 20, and 5 of 30 for the incident line with no readable record, as the Gemini Live and Gemini Embedding listings read the same page. Google Cloud's status dashboard covers Vertex AI products, not this endpoint. Per-model RPM and TPM live in AI Studio. The public rate-limits page, re-read on 1 November, publishes tier rules, spend-based limits of $10, $50 and $200 per 10 minutes for Tiers 1 to 3, and batch queue limits (5 of 15). The troubleshooting page, re-read on 1 November, gives exponential backoff with jitter for 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE, says to retry 429, 408 and 5xx and not 400, 402 or 403, and says the Python SDK retries transient errors up to four times (15). No SLA found for the Developer API (0). The flagship `gemini-4-argon` went GA on 13 October 2026 and the default 3.8 Flash is GA, and the Interactions API the guides lead with has been Generally Available since June 2026. The listing's example and the SDKs default to the `v1beta` path, which Google labels beta, but the API versions page, read on 1 November, says the stable `v1` path carries the Interactions API, generateContent, function calling and structured output and supports every model, so the surface is available as GA, the reading Gemini Embedding takes on the same endpoint. Gemini Live keeps a deduction because the Live API runs on `v1beta` only (10). Before 13 October the only Pro model was `gemini-3.1-pro-preview` (5 of 10)." }, "scores": { "a": 80, "b": 45 } }, { "by": 20, "category": "payments", "leads": "gemini-api", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-1M-token prices are public in the Azure Retail Prices API without a login, while the pricing page fills its table with JavaScript and showed GPT-6 prices as pending on 6 October (20). The quota tiers include a Free tier, but an Azure subscription needs a payment method (0). A person signs up for Azure in a browser and creates a resource and a deployment before the first call (0).", "b": "No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0)." }, "scores": { "a": 20, "b": 40 } }, { "by": 11, "category": "transparency", "leads": "azure-openai", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under the Microsoft Product Terms, with the v1 spec under MIT and the OpenAI SDKs under Apache-2.0 (15). The data privacy page agrees with the product terms on no training and states where data is processed and stored by deployment type, but gives no retention period for the human-review store and sits beside a 30-day default for Responses data (24 of 30). A published lifecycle policy and a retirement schedule with dates and replacements, but on 6 November the schedule listed `gpt-realtime-mini` 2025-10-06 and 2025-12-15 twice each with different retirement dates, and no GPT-6 model yet (18 of 20, down from 20). Data locations stated for Standard, Data Zone and Global deployments, and a public sub-processor list per the 5 October Azure Speech check, not re-read (20).", "b": "Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20)." }, "scores": { "a": 83, "b": 72 } }, { "by": 5, "category": "ergonomics", "leads": "azure-openai", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and a forced call by naming the function in `tool_choice`, 18 of 20 because `gpt-6.1-sol` calls tools only through the Responses API (18). Strict structured outputs (15). Prompt caching from 1,024 tokens, cached input at 0.1x on GPT-6 ($1 per million on Astra) and cache writes at 1.25x from GPT-5.6 on, with explicit breakpoints (15). 1,050,000-token context on GPT-6 and GPT-6.1, input capped at 922,000 (15). Batch at 50% off Global Standard with a 24-hour target (10). The docs use OpenAI's own SDKs for Python, JavaScript, C#, Go and Java against the v1 endpoint (10). 429 and quota errors documented with retry headers (15). Calls name a deployment rather than a model, so each model must be deployed in the resource first, which the model checklist doesn't score.", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed)." }, "scores": { "a": 98, "b": 93 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Microsoft", "b": "Google", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://generativelanguage.googleapis.com/v1beta", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-10-30", "name": "Last release" }, { "a": "no date given", "b": "2026-04-28", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "yes", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "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": "454k npm/wk", "b": "4k stars", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Azure OpenAI in Foundry Models or Gemini Developer API?" }, { "answer": "Azure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Gemini Developer API needs an API key.", "question": "Do Azure OpenAI in Foundry Models and Gemini Developer API need an API key?" }, { "answer": "Yes. Azure OpenAI in Foundry Models has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Gemini Developer API at https://generativelanguage.googleapis.com/v1beta.", "question": "Can an agent call Azure OpenAI in Foundry Models and Gemini Developer API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 45", "Agent ergonomics, 98 against 93", "Security \u0026 auth, 95 against 50", "Transparency \u0026 trust, 83 against 72" ], "also": [ "Agent-ready, a grade of BB or better", "No incidents deducted, where Gemini Developer API loses 3 points for them" ], "goodFor": "Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.", "slug": "azure-openai", "watchFor": "Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026" }, { "aheadOn": [ "Payments \u0026 pricing, 40 against 20" ], "also": [ "Free to start without a card" ], "goodFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "slug": "gemini-api", "watchFor": "Free-tier prompts and responses improve Google products and may be read by human reviewers" } ], "history": { "azure-openai": null, "gemini-api": [ { "causes": [ "product", "evidence", "correction" ], "date": "2026-11-01", "delta": -3.3, "from": 65.5, "fromGrade": "B", "to": 62.2, "toGrade": "B" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 65.6, "fromGrade": "B", "to": 65.5, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-28", "delta": 0.1, "from": 65.5, "fromGrade": "B", "to": 65.6, "toGrade": "B" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai.json", "title": "Amazon Bedrock vs Azure OpenAI in Foundry Models", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api.json", "title": "Amazon Bedrock vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-azure-openai.json", "title": "Claude API vs Azure OpenAI in Foundry Models", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-azure-openai" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api.json", "title": "Claude API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-azure-openai.json", "title": "Antseed vs Azure OpenAI in Foundry Models", "url": 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"https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.json", "title": "Azure OpenAI in Foundry Models vs Eden AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-elorian-glimpse.json", "title": "Azure OpenAI in Foundry Models vs Glimpse", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-elorian-glimpse" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-fireworks-inference.json", "title": "Azure OpenAI in Foundry Models vs Fireworks AI Inference", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-fireworks-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-groq.json", "title": "Azure OpenAI in Foundry Models vs GroqCloud", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers.json", "title": 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"https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api" }, { "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-muse-spark-open.json", "title": "Azure OpenAI in Foundry Models vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-nemotron-3-5-super-vl.json", "title": "Azure OpenAI in Foundry Models vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-novita-ai.json", "title": "Azure OpenAI in Foundry Models vs Novita AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-openai-api.json", "title": "Azure OpenAI in Foundry Models vs OpenAI API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-openrouter.json", "title": "Azure OpenAI in Foundry Models vs OpenRouter", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference.json", "title": "Azure OpenAI in Foundry Models vs Prism Inference", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-ramp-router.json", "title": "Azure OpenAI in Foundry Models vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-reflection-beam.json", "title": "Azure OpenAI in Foundry Models vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-routerplus.json", "title": "Azure OpenAI in Foundry Models vs RouterPlus", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-sambanova.json", "title": "Azure OpenAI in Foundry Models vs SambaCloud", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.json", "title": "Azure OpenAI in Foundry Models vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.json", "title": "Azure OpenAI in Foundry Models vs Simplismart", "url": 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"https://www.anchorterminal.com/compare/azure-openai-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-vercel-ai-gateway.json", "title": "Azure OpenAI in Foundry Models vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-vercel-ai-gateway" }, { "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/azure-openai-vs-xai-api.json", "title": "Azure OpenAI in Foundry Models vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.json", "title": "BlockRun.AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-api.json", "title": "Cerebras Inference vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api.json", "title": "Cloudflare AI Gateway vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api.json", "title": "Cloudflare Workers AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api.json", "title": "Cohere Chat API (Command models) vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.json", "title": "DeepInfra vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api.json", "title": "DeepSeek API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.json", "title": "Eden AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-api.json", "title": "Glimpse vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-api.json", "title": "Fireworks AI Inference vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-groq.json", "title": "Gemini Developer API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-hugging-face-inference-providers.json", "title": "Gemini Developer API vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-inkling-2-small.json", "title": "Gemini Developer API vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-jais-3.json", "title": "Gemini Developer API vs Jais 3", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.json", "title": "Gemini Developer API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api.json", "title": "Gemini Developer API vs MiniMax API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api" }, { "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-muse-spark-open.json", "title": "Gemini Developer API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-nemotron-3-5-super-vl.json", "title": "Gemini Developer API vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai.json", "title": "Gemini Developer API vs Novita AI", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api.json", "title": "Gemini Developer API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter.json", "title": "Gemini Developer API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.json", "title": "Gemini Developer API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-ramp-router.json", "title": "Gemini Developer API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-reflection-beam.json", "title": "Gemini Developer API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-routerplus.json", "title": "Gemini Developer API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.json", "title": "Gemini Developer API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.json", "title": "Gemini Developer API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart.json", "title": "Gemini Developer API vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.json", "title": "Gemini Developer API vs StepFun API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash.json", "title": "Gemini Developer API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub.json", "title": "Gemini Developer API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference.json", "title": "Gemini Developer API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-vercel-ai-gateway.json", "title": "Gemini Developer API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-vercel-ai-gateway" }, { "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/gemini-api-vs-xai-api.json", "title": "Gemini Developer API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-xai-api" } ], "panel": null, "scores": [ { "azure-openai": 80, "by": 35, "edge": "azure-openai", "gemini-api": 45, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-openai": 87, "by": 2, "edge": "azure-openai", "gemini-api": 85, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "azure-openai": 98, "by": 5, "edge": "azure-openai", "gemini-api": 93, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "azure-openai": 95, "by": 45, "edge": "azure-openai", "gemini-api": 50, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "azure-openai": 20, "by": 20, "edge": "gemini-api", "gemini-api": 40, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-openai": 86, "by": 3, "edge": "azure-openai", "gemini-api": 83, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "azure-openai": 83, "by": 11, "edge": "azure-openai", "gemini-api": 72, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments \u0026 pricing. Both do llm inference.", "tasks": [ { "a": "$110", "b": "$1.93", "cheaper": "gemini-api", "task": "Research a product and write a sourced report", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, gemini-4-argon on Gemini Developer API" }, { "a": "$25.07", "b": "$0.84", "cheaper": "gemini-api", "task": "Build a small four-page website", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, gemini-4-argon on Gemini Developer API" }, { "a": "$5.41", "b": "$0.18", "cheaper": "gemini-api", "task": "Find and fix a bug in a large codebase", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, gemini-4-argon on Gemini Developer API" } ], "tutorials": [], "verdicts": { "azure-openai": "An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.", "gemini-api": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api", "json": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.min.md" }, "markdown": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments \u0026 pricing. Both do llm inference.\n\n- Azure OpenAI in Foundry Models: grade A, 80/100, rank #14 of 1486. Markdown https://www.anchorterminal.com/tools/azure-openai.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-openai.json\n- Gemini Developer API: grade B, 62.2/100, rank #566 of 1486. Markdown https://www.anchorterminal.com/tools/gemini-api.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-api.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### Azure OpenAI in Foundry Models (A)\n\nGood for: Teams on Azure that need OpenAI models with Entra ID, private networking, EU or US data zones or FedRAMP scope, and one resource that also serves DeepSeek, Llama, Mistral and Grok models.\n\nAhead on:\n- Reliability, 80 against 45\n- Agent ergonomics, 98 against 93\n- Security \u0026 auth, 95 against 50\n- Transparency \u0026 trust, 83 against 72\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- No incidents deducted, where Gemini Developer API loses 3 points for them\n\nWatch for: Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026\n\n### Gemini Developer API (B)\n\nGood for: Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.\n\nAhead on:\n- Payments \u0026 pricing, 40 against 20\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Free-tier prompts and responses improve Google products and may be read by human reviewers\n\n\n## Why the edge\n\n### Security \u0026 auth: Azure OpenAI in Foundry Models +45\n\n- Azure OpenAI in Foundry Models (95): Model reading. Microsoft Entra ID tokens with Azure roles (the Cognitive Services OpenAI User role for calls), managed identities, and key authentication that can be disabled per resource, or two regenerable resource keys in the `api-key` header. No documented route puts the key in a URL query string (30). Prompts, completions, embeddings and training data aren't used to train foundation models without the customer's permission or instruction, per the data privacy page updated 4 August 2026 (20). Flagged prompts and completions may be stored in the customer's geography for human abuse review, with no retention period stated, and approved customers can turn that storage off through modified abuse monitoring. Responses API data is kept 30 days by default unless `store` is false (12 of 15). Azure Monitor resource logs and the subscription activity log, as read in the Foundry fine-tuning dossier (15). MSRC coordinated disclosure policy and Microsoft's bounty programme, both named in microsoft.com's security.txt, and Azure OpenAI is listed in FedRAMP High and DoD IL2 scope in Azure public regions. The security.txt passed its Expires date on 2026-09-23 and was still expired on 6 November, which the Bedrock and S3 dossiers mark down by 2, applied here too (18 of 20, down from 20 on 6 October for consistency).\n- Gemini Developer API (50): Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20).\n\n### Reliability: Azure OpenAI in Foundry Models +35\n\n- Azure OpenAI in Foundry Models (80): Azure status page with post-incident reviews, listing Azure OpenAI Service, Foundry Models and Foundry Agent Service as components (20). One incident in the 90 days to 6 November 2026 names Azure OpenAI, and no review after it does, intermittent request failures, higher latency and HTTP 5xx errors in Sweden Central from 10:03 to 15:58 UTC on 29 September 2026 (tracking 7QL5-Z50), counted as one major outage as in the Foundry fine-tuning dossier. The 23 July West US review and the 30 September gateway incident don't name it (10). Quota tiers from Free to Tier 6 with TPM and RPM per model and deployment type, 1,000 RPM and 1,000,000 TPM for each GPT-6 model on Global Standard at Tier 1 (15). 429 responses carry `retry-after-ms`, every response carries `x-ratelimit-*` headers, and the quota guide gives exponential backoff with jitter and SDK retry settings (15). The models page says models sold by Azure are covered by Azure service-level agreements and links the Online Services SLA. The consolidated SLA document wouldn't download on 6 October and wasn't retried on 6 November, so the clause rests on Microsoft's docs, read the same way as the 5 October Azure Speech check (10). The v1 API is GA and the GPT-6 models carry no preview label (10).\n- Gemini Developer API (45): Status page at aistudio.google.com/status (robots.txt allows it), but on 1 November it still returned a script shell titled Google AI Studio with no components or history, so 10 of 20, and 5 of 30 for the incident line with no readable record, as the Gemini Live and Gemini Embedding listings read the same page. Google Cloud's status dashboard covers Vertex AI products, not this endpoint. Per-model RPM and TPM live in AI Studio. The public rate-limits page, re-read on 1 November, publishes tier rules, spend-based limits of $10, $50 and $200 per 10 minutes for Tiers 1 to 3, and batch queue limits (5 of 15). The troubleshooting page, re-read on 1 November, gives exponential backoff with jitter for 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE, says to retry 429, 408 and 5xx and not 400, 402 or 403, and says the Python SDK retries transient errors up to four times (15). No SLA found for the Developer API (0). The flagship `gemini-4-argon` went GA on 13 October 2026 and the default 3.8 Flash is GA, and the Interactions API the guides lead with has been Generally Available since June 2026. The listing's example and the SDKs default to the `v1beta` path, which Google labels beta, but the API versions page, read on 1 November, says the stable `v1` path carries the Interactions API, generateContent, function calling and structured output and supports every model, so the surface is available as GA, the reading Gemini Embedding takes on the same endpoint. Gemini Live keeps a deduction because the Live API runs on `v1beta` only (10). Before 13 October the only Pro model was `gemini-3.1-pro-preview` (5 of 10).\n\n### Payments \u0026 pricing: Gemini Developer API +20\n\n- Azure OpenAI in Foundry Models (20): No x402, MPP or L402 (0). Per-1M-token prices are public in the Azure Retail Prices API without a login, while the pricing page fills its table with JavaScript and showed GPT-6 prices as pending on 6 October (20). The quota tiers include a Free tier, but an Azure subscription needs a payment method (0). A person signs up for Azure in a browser and creates a resource and a deployment before the first call (0).\n- Gemini Developer API (40): No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0).\n\n### Transparency \u0026 trust: Azure OpenAI in Foundry Models +11\n\n- Azure OpenAI in Foundry Models (83): Closed service under the Microsoft Product Terms, with the v1 spec under MIT and the OpenAI SDKs under Apache-2.0 (15). The data privacy page agrees with the product terms on no training and states where data is processed and stored by deployment type, but gives no retention period for the human-review store and sits beside a 30-day default for Responses data (24 of 30). A published lifecycle policy and a retirement schedule with dates and replacements, but on 6 November the schedule listed `gpt-realtime-mini` 2025-10-06 and 2025-12-15 twice each with different retirement dates, and no GPT-6 model yet (18 of 20, down from 20). Data locations stated for Standard, Data Zone and Global deployments, and a public sub-processor list per the 5 October Azure Speech check, not re-read (20).\n- Gemini Developer API (72): Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20).\n\n### Agent ergonomics: Azure OpenAI in Foundry Models +5\n\n- Azure OpenAI in Foundry Models (98): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and a forced call by naming the function in `tool_choice`, 18 of 20 because `gpt-6.1-sol` calls tools only through the Responses API (18). Strict structured outputs (15). Prompt caching from 1,024 tokens, cached input at 0.1x on GPT-6 ($1 per million on Astra) and cache writes at 1.25x from GPT-5.6 on, with explicit breakpoints (15). 1,050,000-token context on GPT-6 and GPT-6.1, input capped at 922,000 (15). Batch at 50% off Global Standard with a 24-hour target (10). The docs use OpenAI's own SDKs for Python, JavaScript, C#, Go and Java against the v1 endpoint (10). 429 and quota errors documented with retry headers (15). Calls name a deployment rather than a model, so each model must be deployed in the resource first, which the model checklist doesn't score.\n- Gemini Developer API (93): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed).\n\n## Score by category\n\n| Category | Weight | Azure OpenAI in Foundry Models | Gemini Developer API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 45 | Azure OpenAI in Foundry Models +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 85 | Azure OpenAI in Foundry Models +2 |\n| Agent ergonomics | 13% (16.2 this run) | 98 | 93 | Azure OpenAI in Foundry Models +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 50 | Azure OpenAI in Foundry Models +45 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 40 | Gemini Developer API +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 86 | 83 | Azure OpenAI in Foundry Models +3 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 72 | Azure OpenAI in Foundry Models +11 |\n| Negative events | ≤15 | 0 | -3 | |\n| **Total** | | **80 · A** | **62.2 · B** | |\n\n## Facts side by side\n\n| Fact | Azure OpenAI in Foundry Models | Gemini Developer API |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Microsoft | Google |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://generativelanguage.googleapis.com/v1beta` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | Proprietary service under the Microsoft Product Terms. The v1 OpenAPI spec is MIT, and the OpenAI SDKs the docs use are Apache-2.0 | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-26 | 2026-10-30 |\n| Terms last updated | no date given | 2026-04-28 |\n| Privacy policy last updated | 2026-09-01 | 2026-10-01 |\n| Customer content may train models | yes | yes |\n| Terms restrict automated access | yes | yes |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 454k npm/wk | 4k stars |\n| Agent reviews | none | 3.5/5 (2) |\n\n## What the same task costs\n\n| Task | Azure OpenAI in Foundry Models | Gemini Developer API |\n| --- | --- | --- |\n| Research a product and write a sourced report | $110 | $1.93 |\n| Build a small four-page website | $25.07 | $0.84 |\n| Find and fix a bug in a large codebase | $5.41 | $0.18 |\n\n## How the scores have moved\n\n### Azure OpenAI in Foundry Models\n\nNo score change since it was listed.\n\n### Gemini Developer API\n\n- -3.3 on 1 November 2026: product, new evidence, correction\n- -0.1 on 28 October 2026: correction\n- +0.1 on 28 October 2026: checklist\n\n## Verdicts\n\n**Azure OpenAI in Foundry Models.** An OpenAI-compatible v1 API with a public OpenAPI spec, Entra ID roles in place of shared keys and FedRAMP High scope, with GPT-6 and GPT-6.1 at OpenAI's list prices on Global Standard. Access needs an Azure subscription with a card and a deployment per model, and a 29 September 2026 incident caused about six hours of failures in Sweden Central.\n\n**Gemini Developer API.** Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.\n\n## Before you call either\n\n### Azure OpenAI in Foundry Models\n\n1. Point the OpenAI SDK at https://\u003cresource\u003e.openai.azure.com/openai/v1/ and pass the deployment name as `model`, not the model id\n2. Prefer Entra ID with the Cognitive Services OpenAI User role over the resource key, which opens the whole data plane\n3. On 429 wait for `retry-after-ms`, then back off with jitter. The OpenAI SDK retries twice by default\n4. Set `store: false` on Responses calls you don't need kept. Stored responses persist 30 days by default\n5. Keep remote MCP approvals on for servers you don't control. They're required by default\n\n### Gemini Developer API\n\n1. Never send customer data through a free-tier key. Link billing first\n2. Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL\n3. Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403\n4. Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400\n5. On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution\n\n## Questions\n\n### Which is better for AI agents, Azure OpenAI in Foundry Models or Gemini Developer API?\n\nAzure OpenAI in Foundry Models scores 80 (A) on agent readiness against Gemini Developer API's 62.2 (B), and leads in 6 of 7 scored categories. Gemini Developer API leads on payments \u0026 pricing.\n\n### Do Azure OpenAI in Foundry Models and Gemini Developer API need an API key?\n\nAzure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Gemini Developer API needs an API key.\n\n### Can an agent call Azure OpenAI in Foundry Models and Gemini Developer API without installing anything?\n\nYes. Azure OpenAI in Foundry Models has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Gemini Developer API at https://generativelanguage.googleapis.com/v1beta.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-openai\", \"b\": \"gemini-api\"}`. 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(Grok)](https://www.anchorterminal.com/compare/gemini-api-vs-xai-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Azure OpenAI in Foundry Models vs Gemini Developer API", "url": "" } ], "description": "Azure OpenAI in Foundry Models scores 80 (A) to Gemini Developer's 62.2 (B) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Azure OpenAI in Foundry Models A 80", "Gemini Developer API B 62.2", "scores" ], "h1": "Azure OpenAI in Foundry Models vs Gemini Developer API", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/azure-openai-vs-gemini-api", "published": "2026-10-01", "section": "tools", "title": "Azure OpenAI in Foundry Models vs Gemini Developer (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api" }, "tokens": { "markdown": 7150, "slim": 3430 }, "version": 1 }