{ "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 MiniMax API's 55.3 (C), and leads in 6 of 7 scored categories.", "b": { "slug": "minimax-api", "name": "MiniMax API", "vendor": "MiniMax", "vendorUrl": "https://www.minimax.io", "kind": "model", "category": "inference", "summary": "MiniMax's API for its M-series text models, M3.1 Flash and M3, in the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses formats, with 1M-token context and automatic prompt caching.", "url": "https://www.anchorterminal.com/tools/minimax-api", "markdownUrl": "https://www.anchorterminal.com/tools/minimax-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-api.json", "dns": "minimax-api.grade.anchorterminal.com", "repo": "https://github.com/MiniMax-AI/MiniMax-M3", "license": "Proprietary API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence", "transports": [ "http" ], "remoteUrl": "https://api.minimax.io", "packages": [], "auth": "api-key", "authNotes": "Bearer key from platform.minimax.io, charged to a prepaid balance. M Plan subscribers get a separate Subscription Key that draws on the plan's usage windows, and the two kinds of key can't be swapped. Mainland China accounts use api.minimaxi.com with a separate key. MiniMax says it may disable a key that leaks publicly.", "pricing": "usage", "pricingNotes": "Prepaid balance, pay as you go. MiniMax-M3.1-Flash is $0.15 per million input tokens, $0.60 per million output tokens and $0.015 per million cache-read tokens at any input length. MiniMax-M3 is $0.30 in and $1.20 out up to 512K input tokens and $0.60 and $2.40 above, with cache reads at $0.06 and $0.12, and a Priority tier at 1.5 times that M3.1 Flash doesn't take. Caching is automatic. The web_search server tool (beta) is $0.01 a request. M Plan subscriptions for coding tools are Go $22, Explore $55 and Build $132 a month, and annual billing pays for 10 months (https://platform.minimax.io/docs/pricing/overview, https://platform.minimax.io/docs/m-plan/monthly-offer, checked 19 October 2026).", "priceSummary": "from $0.15 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs, llms.txt or pricing page. Prepaid balance top-up or an M Plan subscription (checked 2026-10-19).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 493, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-19" }, "docsUrl": "https://platform.minimax.io/docs/guides/text-generation", "rateLimitsUrl": "https://platform.minimax.io/docs/guides/rate-limits", "llmsTxt": "https://platform.minimax.io/docs/llms.txt", "openapi": "https://platform.minimax.io/docs/api-reference/text/api/openapi-chat-anthropic.json", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "open-weights", "prepaid", "openapi", "llms-txt" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 55.3, "grade": "C", "agentReady": false, "rank": 872, "ranked": true, "rankOf": 1486, "categoryRank": 26, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 59, "maintenance": 45, "payments": 20, "reliability": 75, "schema": 82, "security": 30, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour.", "bestFor": "Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.", "strengths": [ "M3.1 Flash at $0.15 in, $0.60 out and $0.015 for cache reads per million tokens, flat at any input length", "Anthropic Messages, OpenAI Chat Completions and OpenAI Responses formats, each with an OpenAPI 3.1 file", "1M-token context and up to 524,288 output tokens on M3.1 Flash and M3", "Automatic prompt caching and a token-count endpoint", "MiniMax-M3 and, since 23 October 2026, MiniMax-M3.1-Flash weights on Hugging Face under the MiniMax community licence" ], "weaknesses": [ "The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found", "No free tier, SLA, batch API, JSON Schema output mode or security.txt", "212 'Elevated errors' incidents on the LLM component from 6 August to 3 November 2026, one of 65 minutes and the rest under an hour", "M2.5 and M2.7 retire on 18 November 2026, 30 days after new users lost them, and plan requests to them already go to M3.1 Flash", "The model list calls M2.5 and M2.7 retired while the notice on the same page gives 18 November 2026" ], "agentNotes": [ "Use the Anthropic-format base URL `https://api.minimax.io/anthropic` for tool calls. The OpenAI-format schema has no `tool_choice` or `response_format`", "Set `output_config.effort` (or `reasoning_effort`) below `max` for routine calls. M3.1 Flash defaults to max, can't turn thinking off, and counts thinking against `max_tokens`", "Don't rely on `stop_sequences` or `top_k`. The Anthropic-format endpoint ignores them", "Back off on 429, 529 and the retry-later codes (1002, 2064) yourself. No Retry-After header is documented", "Use a pay-as-you-go key for production. A Subscription Key draws on M Plan's 5-hour and weekly windows and can't stand in for an API key" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.3 } ], "editorialScores": { "ergonomics": 59, "maintenance": 45, "payments": 20, "reliability": 75, "schema": 82, "security": 30, "transparency": 47 }, "provenanceScore": 82, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-10-28", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://api.minimax.io/anthropic/v1/messages \\\n -H \"Authorization: Bearer $MINIMAX_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"MiniMax-M3.1-Flash\",\"output_config\":{\"effort\":\"low\"},\"max_tokens\":4096,\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/minimax-api" }, "sameCompany": [ "minimax-video", "minimax-stt", "minimax-speech", "minimax-voice-cloning", "minimax-code", "minimax-m2-5", "minimax-m2-7", "minimax-m3-1-flash" ], "area": "models", "unitPrices": [ { "item": "web_search server tool (beta)", "unit": "1k-requests", "usd": 10 }, { "item": "M Plan Go", "unit": "month", "usd": 22, "note": "M3.1 Flash, image and audio, no video. Annual billing pays for 10 months" }, { "item": "M Plan Explore", "unit": "month", "usd": 55, "note": "3 times Go's usage, adds H3 video" }, { "item": "M Plan Build", "unit": "month", "usd": 132, "note": "7.5 times Go's usage, adds H3 video" } ], "provenance": { "legalEntity": "Nanonoble Pte. Ltd.", "domain": "minimax.io", "domainRegistered": "2021-03-27", "endpointOnVendorDomain": true, "terms": "https://platform.minimax.io/protocol/terms-of-service", "privacy": "https://platform.minimax.io/protocol/privacy-policy", "statusPage": "https://status.minimax.io", "changelog": "https://platform.minimax.io/docs/release-notes/models", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "Nanonoble Pte. Ltd., 152 Beach Road, #14-02 Gateway East, Singapore, is named in the terms (effective 30 March 2026) and the privacy policy (updated 30 March 2026), read on 19 October and again on 4 November 2026 from the document text the pages carry for the browser. The policy reader recorded both pages as unreadable on 8 October, and that reading is kept.", "RDAP for minimax.io gives a registration date of 2021-03-27 and a transfer on 2025-01-31, as recorded on the MiniMax Video API listing.", "platform.minimax.io answers 404 for /.well-known/security.txt (4 November 2026). api.minimax.io answered 404 and www.minimax.io redirected it to the home page on 19 October.", "The API release notes page has no entry after 28 October 2025. Model releases are dated on a separate page, whose newest entry on 4 November 2026 is 19 October 2026." ], "score": 82 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-api.json", "live": { "slug": "minimax-api", "probe": { "target": "https://api.minimax.io", "method": "get", "lastAt": "2026-11-07T18:20:39.895110071Z", "lastOk": true, "lastStatus": 404, "lastMs": 412, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 272, "p95ms24h": 504, "samples24h": 239, "samples30d": 4706, "days": [ { "date": "2026-10-19", "probes": 93, "ok": 93 }, { "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.minimax.io", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T18:21:22.801924019Z" }, "githubStars": 500, "securityTxt": { "url": "https://minimax.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:57.120551174Z" }, "llmsTxt": { "url": "https://platform.minimax.io/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:33:02.70243541Z" }, "domain": { "domain": "minimax.io", "checkedAt": "2026-11-01T14:11:19.440143188Z" }, "pages": [ { "url": "https://platform.minimax.io/docs/faq/models", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:16:45.419468064Z", "changedAt": "2026-11-06T13:38:25.889680746Z", "fingerprint": "8abebc4199ab" }, { "url": "https://platform.minimax.io/docs/release-notes/models", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:16:51.927185731Z", "changedAt": "2026-11-05T12:39:38.489181558Z", "fingerprint": "3b7661f72594" }, { "url": "https://platform.minimax.io/docs/pricing/overview", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:16:47.780533353Z", "changedAt": "2026-11-06T13:38:28.215329328Z", "fingerprint": "cfd940115e2f" }, { "url": "https://platform.minimax.io/protocol/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:16:53.708283926Z", "changedAt": "2026-10-31T06:18:23.585835081Z", "fingerprint": "1ae59257a3a9" }, { "url": "https://platform.minimax.io/protocol/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:16:57.419839523Z", "changedAt": "2026-10-31T06:18:25.544630345Z", "fingerprint": "f98c6c8b7950" } ], "github": { "repo": "minimax-ai/minimax-m3", "checkedAt": "2026-11-02T08:26:01.986494652Z", "stars": 498, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-26T07:05:04Z", "openIssues": 29, "openPRs": 0, "issuesOpened90d": 8, "issuesClosed90d": 0, "prsMerged90d": 1, "replies": { "sampled": 25, "unanswered": 23, "medianDays": 8.8 } }, "updatedAt": "2026-11-07T18:21:22.801924019Z" } }, "edges": [ { "by": 65, "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 (credential, training, retention, operator visibility, programme). One bearer key per account on the pay-as-you-go side and a separate Subscription Key for M Plan, with no scopes found. MiniMax says it may disable keys that leak publicly (20 of 30). The terms, effective 30 March 2026 and unchanged on 4 November, say MiniMax may use input and generated content to provide, maintain, develop and improve its services, and no opt-out was found. The privacy policy says only that personal data isn't used for training to profile or target consumers (0 of 20). Data is kept as long as necessary for its purpose, with no period stated and no zero-retention route (5 of 15). A usage page and balance alerts, no audit log found (5 of 15). No security.txt on platform.minimax.io (404 again on 4 November) or api.minimax.io (per the 19 October check), and no bug bounty or certification found (0 of 20). Total 30." }, "scores": { "a": 95, "b": 30 } }, { "by": 41, "category": "maintenance", "leads": "azure-openai", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. GPT-6.1 Terra released on Azure on 26 October 2026 and a `gpt-chat-latest` preview of 7 October was added, after GPT-6.1 Sol on 29 September (30). GA models get a retirement date 18 months after launch, set at release and readable through the Models API, with at least 60 days' email and Service Health notice, and previews at least 30 days, so a GA retirement is known more than six months ahead (12 of 12). Many shutdown dates in the 90 days to 6 November, most of them previews or partner models, among them DeepSeek-R1 (13 August), MAI-Image-2 and 2e (15 August), model-router previews (30 August), MAI-Transcribe-1 (15 September), MAI-Image-2.5 previews (1 October), the 2025-03-20 audio models and sora-2 (15 October), a Cohere Command A Plus preview (16 October) and gpt-image-1 (23 October), each in the schedule with a replacement where one exists (4 of 8). The Foundry what's new page, still on August when read on 6 November, plus Microsoft Q\u0026A and paid support; no Azure OpenAI what's new was found (10 of 15). The SDKs are OpenAI's repositories, replies not sampled (5 of 10). Current official SDKs, `openai` 3.46.0 on PyPI (5 November) and 7.53.0 on npm, plus Microsoft's `@azure/openai` 2.0.0 companion (15). Supported runtimes stated, Python 3.9 or later and Node.js 20 or later, though `openai` 3.46.0 requires Python 3.10 (10).", "b": "Model reading. MiniMax-M3.1-Flash shipped on 19 October 2026, dated on the model release page, which has no newer entry on 4 November (16 days, 30). No stated notice policy. The M2.5 and M2.7 families closed to new users on 19 October and retire on 18 November, 30 days later, and from 19 October M Plan and Token Plan requests to them are served by M3.1 Flash under the old names. We couldn't find when that notice was first posted, and the page carries the same notice on 4 November (3 of 12). Churn is high. M2.5 (February 2026) and M2.7 (March 2026) retire within nine months of release, M2.1 and M2 are already retired, and the model list still calls M2.5 and M2.7 retired on 4 November while the notice on the same page gives 18 November (2 of 8). A dated release page, support email addresses on the contact and model pages, a Discord server and GitHub issues on the model repositories. We didn't test whether they answer (10 of 15). No official SDK (0 of 15, 0 of 10). Total 45." }, "scores": { "a": 86, "b": 45 } }, { "by": 39, "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). Tool use on all three formats, with `tool_choice` on the Anthropic format only (17 of 20). No JSON Schema output mode documented on any format (0 of 15). Caching is automatic, with cache reads at a tenth of the input price on M3.1 Flash and a token-count endpoint for sizing a request (15). 1,000,000-token context and up to 524,288 output tokens (15). No batch API found (0). No official SDK. The docs point to the Anthropic and OpenAI SDKs and the AI SDK (0). Typed error bodies with a request id on the Anthropic format and a code table, though `stop_sequences` is ignored without an error (12 of 15). M3.1 Flash can't turn thinking off and defaults to the deepest `effort`, which costs output tokens unless the caller lowers it. Total 59." }, "scores": { "a": 98, "b": 59 } }, { "by": 18, "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 under Singapore law and SIAC arbitration. MiniMax-M3's weights, and since 23 October 2026 MiniMax-M3.1-Flash's, are on Hugging Face under the MiniMax community licence, which isn't an OSI licence and doesn't change the hosted service (15). The terms and privacy policy, both still dated 30 March 2026 on 4 November, agree on the contracting entity, that inputs and outputs are processed and may be used to improve services, and that data sits in a United States data centre, but give no retention period and no DPA was read (10 of 30). A dated retirement notice for M2.5 and M2.7 and a retired-models table, undercut by the table contradicting the notice and API release notes a year stale (12 of 20). Data location stated as the United States and an EU representative named, with third parties named only by category (10 of 20). MiniMax publishes an EU AI Act training-data summary for M3. Total 47." }, "scores": { "a": 83, "b": 65 } }, { "by": 5, "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": "Atlassian Statuspage at status.minimax.io with Large Language Models, Text-to-Speech and Video Generation components (20). Its history pages, re-read on 4 November, now cover the whole 90 days from 6 August. They list 212 incidents on the LLM component, every one titled 'Elevated errors on Large Language Models (LLM)' and labelled major, and none states a full outage. The longest ran 65 minutes (01:15 to 02:20 UTC on 20 October, 'We are currently investigating this issue' and then resolved), and the rest closed within the hour. This run reads elevated errors with no fuller scope as partial, so minor only, frequent as they are (20). The rate-limits page publishes 200 requests and 10,000,000 tokens a minute for M3.1 Flash and M3 (15). The Anthropic-format OpenAPI file documents 429 for rate limits and 529 as an overload that can be retried, and the error-code page says to retry later on 1002, 2064 and other codes, with no Retry-After header or backoff schedule found (10). No SLA found (0). M3.1 Flash and M3 are generally available (10). Total 75." }, "scores": { "a": 80, "b": 75 } }, { "by": 5, "category": "schema", "leads": "azure-openai", "name": "Schema \u0026 documentation", "reasons": { "a": "OpenAPI 3.2 spec for the v1 data plane in Azure/azure-rest-api-specs (`azure-v1-v1-generated.json`, 71 paths and 852 schemas), linked from the v1 API page (25). No llms.txt at learn.microsoft.com (404). Learn pages offer Copy Markdown and the Markdown sources are public in MicrosoftDocs/azure-ai-docs, last merged 5 October (5 of 10). The models page lists each model's capabilities, context and API support, but the model choice guide, dated 19 May 2026, doesn't cover GPT-6 (15 of 20). Structured outputs with `strict: true` on schemas and function tools, though not together with parallel function calls (15). Examples in Python, C#, JavaScript, Go, Java and REST, and an error table on the quotas page with a fix for 429 and other codes (15). The v1 path replaces monthly `api-version` values, with preview capabilities opted into by header, a dated API version changelog and dated model versions. On 6 November the Foundry what's new still covered August, though updated on 5 November, and no Azure OpenAI what's new was found at learn.microsoft.com/en-us/azure/foundry/openai/whats-new, which answered 404 (12 of 15).", "b": "OpenAPI 3.1 files for the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses endpoints, each listing MiniMax-M3.1-Flash and MiniMax-M3 (25). llms.txt indexes the docs, and each page is served as Markdown (10). Field descriptions say which model each setting applies to, such as `service_tier` not being supported on M3.1 Flash and `thinking: disabled` returning 400 there, and a compatibility table marks `top_k`, `stop_sequences`, `mcp_servers` and `container` as ignored (15 of 20). Enums for model, `effort`, `service_tier` and thinking type, with required fields marked. The OpenAI-format schema has no `tool_choice` or `response_format` (11 of 15). The Anthropic-format file documents typed errors for 400, 401, 403, 404, 413, 429, 500 and 529 with example bodies, and a page lists 45 error codes with a fix for each (the 19 October note counted 23; 2069, model no longer available, is among them on 4 November), but the OpenAI and Responses files document only 200 (11 of 15). A dated model release page with the 19 October entry as its newest on 4 November, while the API release notes still stop at 28 October 2025 (10 of 15). Total 82." }, "scores": { "a": 87, "b": 82 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Microsoft", "b": "MiniMax", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://api.minimax.io", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "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": "Proprietary API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-10-19", "name": "Last release" }, { "a": "no date given", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "454k npm/wk", "b": "493 stars", "name": "Popularity" }, { "a": "none", "b": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against MiniMax API's 55.3 (C), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Azure OpenAI in Foundry Models or MiniMax API?" }, { "answer": "Azure OpenAI in Foundry Models takes an API key or an OAuth sign-in. MiniMax API needs an API key.", "question": "Do Azure OpenAI in Foundry Models and MiniMax 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 MiniMax API at https://api.minimax.io.", "question": "Can an agent call Azure OpenAI in Foundry Models and MiniMax API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 75", "Schema \u0026 documentation, 87 against 82", "Agent ergonomics, 98 against 59", "Security \u0026 auth, 95 against 30", "Maintenance \u0026 community, 86 against 45", "Transparency \u0026 trust, 83 against 65" ], "also": [ "Agent-ready, a grade of BB or better" ], "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": null, "also": null, "goodFor": "Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.", "slug": "minimax-api", "watchFor": "The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found" } ], "history": { "azure-openai": null, "minimax-api": [ { "causes": [ "method" ], "date": "2026-10-28", "delta": 0.1, "from": 55.2, "fromGrade": "C", "to": 55.3, "toGrade": "C" } ] }, "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-minimax-api.json", "title": "Amazon Bedrock vs MiniMax API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-minimax-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-minimax-api.json", "title": "Claude API vs MiniMax API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-azure-openai.json", "title": "Antseed vs Azure OpenAI in Foundry Models", "url": "https://www.anchorterminal.com/compare/antseed-vs-azure-openai" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-minimax-api.json", "title": "Antseed vs MiniMax API", "url": "https://www.anchorterminal.com/compare/antseed-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-blockrun-ai.json", "title": "Azure OpenAI in Foundry Models vs BlockRun.AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-blockrun-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-cerebras-inference.json", "title": "Azure OpenAI in Foundry Models vs Cerebras Inference", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-cerebras-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-ai-gateway.json", "title": "Azure OpenAI in Foundry Models vs Cloudflare AI Gateway", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-workers-ai.json", "title": "Azure OpenAI in Foundry Models vs Cloudflare Workers AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-workers-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-cohere-chat.json", "title": "Azure OpenAI in Foundry Models vs Cohere Chat API (Command models)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-cohere-chat" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-deepinfra.json", "title": "Azure OpenAI in Foundry Models vs DeepInfra", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-deepinfra" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-deepseek-api.json", "title": "Azure OpenAI in Foundry Models vs DeepSeek API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-deepseek-api" }, { "json": "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-gemini-api.json", "title": "Azure OpenAI in Foundry Models vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api" }, { "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": "Azure OpenAI in Foundry Models vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers" }, { "json": 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vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-tencent-tokenhub.json", "title": "Azure OpenAI in Foundry Models vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-together-inference.json", "title": "Azure OpenAI in Foundry Models vs Together AI Inference", "url": "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)", 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"title": "Hugging Face Inference Providers vs MiniMax API", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api.json", "title": "Inkling 2 Small vs MiniMax API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api.json", "title": "Jais 3 vs MiniMax API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-api.json", "title": "Meta Model API vs MiniMax API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-api" }, { "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-muse-spark-open.json", "title": "MiniMax API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-nemotron-3-5-super-vl.json", "title": "MiniMax API vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-novita-ai.json", "title": "MiniMax API vs Novita AI", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api.json", "title": "MiniMax API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-openrouter.json", "title": "MiniMax API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-prism-inference.json", "title": "MiniMax API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-ramp-router.json", "title": "MiniMax API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-reflection-beam.json", "title": "MiniMax API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-routerplus.json", "title": "MiniMax API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-sambanova.json", "title": "MiniMax API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow.json", "title": "MiniMax API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart.json", "title": "MiniMax API vs Simplismart", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-stepfun-api.json", "title": "MiniMax API vs StepFun API", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-swe-2-flash.json", "title": "MiniMax API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-tencent-tokenhub.json", "title": "MiniMax API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-together-inference.json", "title": "MiniMax API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-vercel-ai-gateway.json", "title": "MiniMax API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-vercel-ai-gateway" }, { "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/minimax-api-vs-xai-api.json", "title": "MiniMax API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-xai-api" } ], "panel": null, "scores": [ { "azure-openai": 80, "by": 5, "edge": "azure-openai", "key": "reliability", "minimax-api": 75, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-openai": 87, "by": 5, "edge": "azure-openai", "key": "schema", "minimax-api": 82, "name": "Schema \u0026 documentation", "weight": 13 }, { "azure-openai": 98, "by": 39, "edge": "azure-openai", "key": "ergonomics", "minimax-api": 59, "name": "Agent ergonomics", "weight": 13 }, { "azure-openai": 95, "by": 65, "edge": "azure-openai", "key": "security", "minimax-api": 30, "name": "Security \u0026 auth", "weight": 14 }, { "azure-openai": 20, "by": 0, "edge": "", "key": "payments", "minimax-api": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-openai": 86, "by": 41, "edge": "azure-openai", "key": "maintenance", "minimax-api": 45, "name": "Maintenance \u0026 community", "weight": 7 }, { "azure-openai": 83, "by": 18, "edge": "azure-openai", "key": "transparency", "minimax-api": 65, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against MiniMax API's 55.3 (C), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": [ { "a": "$110", "b": "$0.22", "cheaper": "minimax-api", "task": "Research a product and write a sourced report", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, MiniMax-M3.1-Flash on MiniMax API" }, { "a": "$25.07", "b": "$0.07", "cheaper": "minimax-api", "task": "Build a small four-page website", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, MiniMax-M3.1-Flash on MiniMax API" }, { "a": "$5.41", "b": "$0.02", "cheaper": "minimax-api", "task": "Find and fix a bug in a large codebase", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, MiniMax-M3.1-Flash on MiniMax 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.", "minimax-api": "M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api", "json": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.min.md" }, "markdown": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against MiniMax API's 55.3 (C), and leads in 6 of 7 scored categories. 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- MiniMax API: grade C, 55.3/100, rank #872 of 1486. Markdown https://www.anchorterminal.com/tools/minimax-api.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-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 75\n- Schema \u0026 documentation, 87 against 82\n- Agent ergonomics, 98 against 59\n- Security \u0026 auth, 95 against 30\n- Maintenance \u0026 community, 86 against 45\n- Transparency \u0026 trust, 83 against 65\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: Intermittent failures and 5xx errors in Sweden Central for about six hours on 29 September 2026\n\n### MiniMax API (C)\n\nGood for: Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.\n\nWatch for: The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found\n\n\n## Why the edge\n\n### Security \u0026 auth: Azure OpenAI in Foundry Models +65\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- MiniMax API (30): Model reading (credential, training, retention, operator visibility, programme). One bearer key per account on the pay-as-you-go side and a separate Subscription Key for M Plan, with no scopes found. MiniMax says it may disable keys that leak publicly (20 of 30). The terms, effective 30 March 2026 and unchanged on 4 November, say MiniMax may use input and generated content to provide, maintain, develop and improve its services, and no opt-out was found. The privacy policy says only that personal data isn't used for training to profile or target consumers (0 of 20). Data is kept as long as necessary for its purpose, with no period stated and no zero-retention route (5 of 15). A usage page and balance alerts, no audit log found (5 of 15). No security.txt on platform.minimax.io (404 again on 4 November) or api.minimax.io (per the 19 October check), and no bug bounty or certification found (0 of 20). Total 30.\n\n### Maintenance \u0026 community: Azure OpenAI in Foundry Models +41\n\n- Azure OpenAI in Foundry Models (86): Model reading. GPT-6.1 Terra released on Azure on 26 October 2026 and a `gpt-chat-latest` preview of 7 October was added, after GPT-6.1 Sol on 29 September (30). GA models get a retirement date 18 months after launch, set at release and readable through the Models API, with at least 60 days' email and Service Health notice, and previews at least 30 days, so a GA retirement is known more than six months ahead (12 of 12). Many shutdown dates in the 90 days to 6 November, most of them previews or partner models, among them DeepSeek-R1 (13 August), MAI-Image-2 and 2e (15 August), model-router previews (30 August), MAI-Transcribe-1 (15 September), MAI-Image-2.5 previews (1 October), the 2025-03-20 audio models and sora-2 (15 October), a Cohere Command A Plus preview (16 October) and gpt-image-1 (23 October), each in the schedule with a replacement where one exists (4 of 8). The Foundry what's new page, still on August when read on 6 November, plus Microsoft Q\u0026A and paid support; no Azure OpenAI what's new was found (10 of 15). The SDKs are OpenAI's repositories, replies not sampled (5 of 10). Current official SDKs, `openai` 3.46.0 on PyPI (5 November) and 7.53.0 on npm, plus Microsoft's `@azure/openai` 2.0.0 companion (15). Supported runtimes stated, Python 3.9 or later and Node.js 20 or later, though `openai` 3.46.0 requires Python 3.10 (10).\n- MiniMax API (45): Model reading. MiniMax-M3.1-Flash shipped on 19 October 2026, dated on the model release page, which has no newer entry on 4 November (16 days, 30). No stated notice policy. The M2.5 and M2.7 families closed to new users on 19 October and retire on 18 November, 30 days later, and from 19 October M Plan and Token Plan requests to them are served by M3.1 Flash under the old names. We couldn't find when that notice was first posted, and the page carries the same notice on 4 November (3 of 12). Churn is high. M2.5 (February 2026) and M2.7 (March 2026) retire within nine months of release, M2.1 and M2 are already retired, and the model list still calls M2.5 and M2.7 retired on 4 November while the notice on the same page gives 18 November (2 of 8). A dated release page, support email addresses on the contact and model pages, a Discord server and GitHub issues on the model repositories. We didn't test whether they answer (10 of 15). No official SDK (0 of 15, 0 of 10). Total 45.\n\n### Agent ergonomics: Azure OpenAI in Foundry Models +39\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- MiniMax API (59): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use on all three formats, with `tool_choice` on the Anthropic format only (17 of 20). No JSON Schema output mode documented on any format (0 of 15). Caching is automatic, with cache reads at a tenth of the input price on M3.1 Flash and a token-count endpoint for sizing a request (15). 1,000,000-token context and up to 524,288 output tokens (15). No batch API found (0). No official SDK. The docs point to the Anthropic and OpenAI SDKs and the AI SDK (0). Typed error bodies with a request id on the Anthropic format and a code table, though `stop_sequences` is ignored without an error (12 of 15). M3.1 Flash can't turn thinking off and defaults to the deepest `effort`, which costs output tokens unless the caller lowers it. Total 59.\n\n### Transparency \u0026 trust: Azure OpenAI in Foundry Models +18\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- MiniMax API (65): Closed service with clear terms under Singapore law and SIAC arbitration. MiniMax-M3's weights, and since 23 October 2026 MiniMax-M3.1-Flash's, are on Hugging Face under the MiniMax community licence, which isn't an OSI licence and doesn't change the hosted service (15). The terms and privacy policy, both still dated 30 March 2026 on 4 November, agree on the contracting entity, that inputs and outputs are processed and may be used to improve services, and that data sits in a United States data centre, but give no retention period and no DPA was read (10 of 30). A dated retirement notice for M2.5 and M2.7 and a retired-models table, undercut by the table contradicting the notice and API release notes a year stale (12 of 20). Data location stated as the United States and an EU representative named, with third parties named only by category (10 of 20). MiniMax publishes an EU AI Act training-data summary for M3. Total 47.\n\n### Reliability: Azure OpenAI in Foundry Models +5\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- MiniMax API (75): Atlassian Statuspage at status.minimax.io with Large Language Models, Text-to-Speech and Video Generation components (20). Its history pages, re-read on 4 November, now cover the whole 90 days from 6 August. They list 212 incidents on the LLM component, every one titled 'Elevated errors on Large Language Models (LLM)' and labelled major, and none states a full outage. The longest ran 65 minutes (01:15 to 02:20 UTC on 20 October, 'We are currently investigating this issue' and then resolved), and the rest closed within the hour. This run reads elevated errors with no fuller scope as partial, so minor only, frequent as they are (20). The rate-limits page publishes 200 requests and 10,000,000 tokens a minute for M3.1 Flash and M3 (15). The Anthropic-format OpenAPI file documents 429 for rate limits and 529 as an overload that can be retried, and the error-code page says to retry later on 1002, 2064 and other codes, with no Retry-After header or backoff schedule found (10). No SLA found (0). M3.1 Flash and M3 are generally available (10). Total 75.\n\n### Schema \u0026 documentation: Azure OpenAI in Foundry Models +5\n\n- Azure OpenAI in Foundry Models (87): OpenAPI 3.2 spec for the v1 data plane in Azure/azure-rest-api-specs (`azure-v1-v1-generated.json`, 71 paths and 852 schemas), linked from the v1 API page (25). No llms.txt at learn.microsoft.com (404). Learn pages offer Copy Markdown and the Markdown sources are public in MicrosoftDocs/azure-ai-docs, last merged 5 October (5 of 10). The models page lists each model's capabilities, context and API support, but the model choice guide, dated 19 May 2026, doesn't cover GPT-6 (15 of 20). Structured outputs with `strict: true` on schemas and function tools, though not together with parallel function calls (15). Examples in Python, C#, JavaScript, Go, Java and REST, and an error table on the quotas page with a fix for 429 and other codes (15). The v1 path replaces monthly `api-version` values, with preview capabilities opted into by header, a dated API version changelog and dated model versions. On 6 November the Foundry what's new still covered August, though updated on 5 November, and no Azure OpenAI what's new was found at learn.microsoft.com/en-us/azure/foundry/openai/whats-new, which answered 404 (12 of 15).\n- MiniMax API (82): OpenAPI 3.1 files for the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses endpoints, each listing MiniMax-M3.1-Flash and MiniMax-M3 (25). llms.txt indexes the docs, and each page is served as Markdown (10). Field descriptions say which model each setting applies to, such as `service_tier` not being supported on M3.1 Flash and `thinking: disabled` returning 400 there, and a compatibility table marks `top_k`, `stop_sequences`, `mcp_servers` and `container` as ignored (15 of 20). Enums for model, `effort`, `service_tier` and thinking type, with required fields marked. The OpenAI-format schema has no `tool_choice` or `response_format` (11 of 15). The Anthropic-format file documents typed errors for 400, 401, 403, 404, 413, 429, 500 and 529 with example bodies, and a page lists 45 error codes with a fix for each (the 19 October note counted 23; 2069, model no longer available, is among them on 4 November), but the OpenAI and Responses files document only 200 (11 of 15). A dated model release page with the 19 October entry as its newest on 4 November, while the API release notes still stop at 28 October 2025 (10 of 15). Total 82.\n\n## Score by category\n\n| Category | Weight | Azure OpenAI in Foundry Models | MiniMax API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 75 | Azure OpenAI in Foundry Models +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 82 | Azure OpenAI in Foundry Models +5 |\n| Agent ergonomics | 13% (16.2 this run) | 98 | 59 | Azure OpenAI in Foundry Models +39 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 30 | Azure OpenAI in Foundry Models +65 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 86 | 45 | Azure OpenAI in Foundry Models +41 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 65 | Azure OpenAI in Foundry Models +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **80 · A** | **55.3 · C** | |\n\n## Facts side by side\n\n| Fact | Azure OpenAI in Foundry Models | MiniMax API |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Microsoft | MiniMax |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.minimax.io` |\n| Transports | HTTP | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\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 | Proprietary API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-26 | 2026-10-19 |\n| Terms last updated | no date given | couldn't be read |\n| Privacy policy last updated | 2026-09-01 | couldn't be read |\n| Customer content may train models | yes | couldn't be read |\n| Terms restrict automated access | yes | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | not found in the text | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 454k npm/wk | 493 stars |\n| Agent reviews | none | 2.8/5 (4) |\n\n## What the same task costs\n\n| Task | Azure OpenAI in Foundry Models | MiniMax API |\n| --- | --- | --- |\n| Research a product and write a sourced report | $110 | $0.22 |\n| Build a small four-page website | $25.07 | $0.07 |\n| Find and fix a bug in a large codebase | $5.41 | $0.02 |\n\n## How the scores have moved\n\n### Azure OpenAI in Foundry Models\n\nNo score change since it was listed.\n\n### MiniMax API\n\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**MiniMax API.** M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour.\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### MiniMax API\n\n1. Use the Anthropic-format base URL `https://api.minimax.io/anthropic` for tool calls. The OpenAI-format schema has no `tool_choice` or `response_format`\n2. Set `output_config.effort` (or `reasoning_effort`) below `max` for routine calls. M3.1 Flash defaults to max, can't turn thinking off, and counts thinking against `max_tokens`\n3. Don't rely on `stop_sequences` or `top_k`. The Anthropic-format endpoint ignores them\n4. Back off on 429, 529 and the retry-later codes (1002, 2064) yourself. No Retry-After header is documented\n5. Use a pay-as-you-go key for production. A Subscription Key draws on M Plan's 5-hour and weekly windows and can't stand in for an API key\n\n## Questions\n\n### Which is better for AI agents, Azure OpenAI in Foundry Models or MiniMax API?\n\nAzure OpenAI in Foundry Models scores 80 (A) on agent readiness against MiniMax API's 55.3 (C), and leads in 6 of 7 scored categories.\n\n### Do Azure OpenAI in Foundry Models and MiniMax API need an API key?\n\nAzure OpenAI in Foundry Models takes an API key or an OAuth sign-in. MiniMax API needs an API key.\n\n### Can an agent call Azure OpenAI in Foundry Models and MiniMax 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 MiniMax API at https://api.minimax.io.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-openai\", \"b\": \"minimax-api\"}`. From a terminal: `anchor compare azure-openai minimax-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-openai.json and https://www.anchorterminal.com/api/v1/tools/minimax-api.json\n\n## Other comparisons with Azure OpenAI in Foundry Models or MiniMax API\n\n- [Amazon Bedrock vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/amazon-bedrock-vs-azure-openai.md)\n- [Amazon Bedrock vs MiniMax API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-minimax-api.md)\n- [Claude API vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/anthropic-api-vs-azure-openai.md)\n- [Claude API vs MiniMax API](https://www.anchorterminal.com/compare/anthropic-api-vs-minimax-api.md)\n- [Antseed vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/antseed-vs-azure-openai.md)\n- [Antseed vs MiniMax API](https://www.anchorterminal.com/compare/antseed-vs-minimax-api.md)\n- [Azure OpenAI in Foundry Models vs 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(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 MiniMax API", "url": "" } ], "description": "Azure OpenAI in Foundry Models scores 80 (A) to MiniMax's 55.3 (C) for llm inference. 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