{ "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 StepFun API's 44.3 (E), and leads in 6 of 7 scored categories.", "b": { "slug": "stepfun-api", "name": "StepFun API", "vendor": "StepFun", "vendorUrl": "https://platform.stepfun.ai", "kind": "model", "category": "inference", "summary": "The StepFun API is StepFun's paid model API for its Step language, vision and speech models, including the flagship Step 5 Preview, through OpenAI-style Chat Completions and Responses endpoints and an Anthropic-style Messages endpoint.", "url": "https://www.anchorterminal.com/tools/stepfun-api", "markdownUrl": "https://www.anchorterminal.com/tools/stepfun-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/stepfun-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/stepfun-api.json", "dns": "stepfun-api.grade.anchorterminal.com", "license": "Closed service under the StepFun Open Platform Terms of Service (STEPFUN TECHNOLOGY (HK) LIMITED, Hong Kong law).", "transports": [ "http" ], "remoteUrl": "https://api.stepfun.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer API key created in the StepFun Open Platform console after sign-up. Organisations can create one key per project, billed to the organisation's credit and deletable at once. No key scopes were found. Individual accounts must complete identity verification before tiered rate limits apply.", "pricing": "usage", "pricingNotes": "Step 5 Preview is $1.00 per million input tokens on a cache miss, $0.10 on a cache hit and $2.70 per million output tokens, reasoning included (checked 2026-10-12). Prepaid top-ups. No free API allowance was found. A Step Plan subscription covers coding tools separately.", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the international docs index, the pricing page or the Chat Completions reference, checked 12 October 2026. Calls draw on a prepaid balance.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-12" }, "docsUrl": "https://platform.stepfun.ai/docs/en/guides/models/step-5-preview", "llmsTxt": "https://platform.stepfun.ai/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "usage-priced", "closed-source", "llms-txt", "multimodal", "streaming" ], "graded": true, "anchor": { "graded": true, "score": 44.3, "grade": "E", "agentReady": false, "rank": 1206, "ranked": true, "rankOf": 1486, "categoryRank": 38, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 68, "maintenance": 50, "payments": 20, "reliability": 28, "schema": 53, "security": 41, "transparency": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-12" }, "negative": 0, "verdict": "StepFun's API serves Step 5 Preview with a 1M-token context, video input, strict JSON Schema output and published per-token prices, documented in Markdown with an llms.txt. The model is a preview with no release date found, there's no free allowance or SLA, and the terms and privacy policy leave room to train on customer data.", "bestFor": "Long-context agent and coding work over text, screenshots and video at a low per-token price, where a preview model is acceptable.", "strengths": [ "Step 5 Preview takes text, images and video with a 1M-token context and up to 64k output tokens", "Strict JSON Schema output and tool calls on Step 5 Preview, per the structured-output guide", "Per-token prices with cache-hit rates published without a login, and rate-limit tiers published with numbers", "OpenAI-style Chat Completions and Responses endpoints and an Anthropic-style Messages endpoint", "llms.txt and a Markdown copy of every docs page" ], "weaknesses": [ "Step 5 Preview is labelled a preview, and no release date or support commitment was found", "No free API allowance found, and individual accounts need identity verification for tiered limits", "No OpenAPI file, no official SDK and no batch endpoint found", "The privacy policy allows training on user data where law and program terms permit, with no zero-retention option found", "No SLA, security.txt or certification found" ], "agentNotes": [ "Set `max_tokens` yourself. It defaults to unlimited on Step 5 Preview, and reasoning tokens are billed as output", "Use `response_format` with `type: json_schema` and `strict: true` when the shape matters. JSON Mode alone isn't schema-checked", "Back off and retry on 429, 500, 503 and 504. The docs give no Retry-After header or backoff schedule", "Expect HTTP 451 or `finish_reason: content_filter` when content fails StepFun's review, and don't retry the same input", "Use api.stepfun.ai for the international platform. api.stepfun.com is the China platform with yuan pricing" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 44.3 } ], "editorialScores": { "ergonomics": 68, "maintenance": 50, "payments": 20, "reliability": 28, "schema": 53, "security": 41, "transparency": 51 }, "provenanceScore": 62, "checked": { "categories": { "ergonomics": "2026-10-12", "maintenance": "2026-10-12", "payments": "2026-10-12", "reliability": "2026-10-12", "schema": "2026-10-12", "security": "2026-10-12", "transparency": "2026-10-12" }, "last": "2026-10-12", "lastMoved": "2026-10-28", "oldest": "2026-10-12", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://api.stepfun.ai/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $STEPFUN_API_KEY\" \\\n -d '{\"model\":\"step-5-preview\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/stepfun-api" }, "area": "models", "unitPrices": [ { "item": "Step 5 Preview input, cache miss", "unit": "1m-tokens", "usd": 1, "note": "Includes writing new content to the cache." }, { "item": "Step 5 Preview input, cache hit", "unit": "1m-tokens", "usd": 0.1 }, { "item": "Step 5 Preview output", "unit": "1m-tokens", "usd": 2.7, "note": "Reasoning and final answer tokens." } ], "provenance": { "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED", "domain": "stepfun.ai", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://platform.stepfun.ai/docs/en/agreement/userservice", "privacy": "https://platform.stepfun.ai/docs/en/agreement/userprivacy", "statusPage": "", "changelog": "https://platform.stepfun.ai/docs/en/guides/model-migration", "securityTxt": "none", "checked": "2026-10-12", "notes": [ "The terms and the privacy policy name STEPFUN TECHNOLOGY (HK) LIMITED. Disputes go to HKIAC arbitration under Hong Kong law.", "platform.stepfun.ai/.well-known/security.txt returned 404, and stepfun.ai/.well-known/security.txt redirects to the platform home page.", "status.stepfun.ai refused our reader by robots.txt, and the shell couldn't connect, so whether it is a status page wasn't established.", "The X handle comes from the QR code labelled X account on the platform home page, which decodes to https://x.com/StepFun_ai.", "Domain registration date was not checked." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/stepfun-api.json", "live": { "slug": "stepfun-api", "probe": { "target": "https://api.stepfun.ai/v1", "method": "get", "lastAt": "2026-11-07T18:20:57.196687446Z", "lastOk": true, "lastStatus": 404, "lastMs": 300, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 297, "p95ms24h": 407, "samples24h": 239, "samples30d": 6600, "days": [ { "date": "2026-10-12", "probes": 221, "ok": 221 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "securityTxt": { "url": "https://stepfun.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:00:20.384102443Z" }, "llmsTxt": { "url": "https://platform.stepfun.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:34:03.95242188Z" }, "domain": { "domain": "stepfun.ai", "registered": "2023-03-23", "source": "https://rdap.identitydigital.services/rdap/domain/stepfun.ai", "checkedAt": "2026-11-01T14:23:53.578776563Z" }, "pages": [ { "url": "https://platform.stepfun.ai/docs/en/guides/model-migration", "kind": "changelog", "status": 200, "checkedAt": "2026-11-07T14:16:53.997459265Z", "changedAt": "2026-11-06T13:38:33.586652298Z", "fingerprint": "05d8b90a13cc" }, { "url": "https://platform.stepfun.ai/docs/en/guides/image-offline-notice", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T14:16:52.104408963Z", "changedAt": "2026-11-06T13:38:32.195771661Z", "fingerprint": "ce427e012456" }, { "url": "https://platform.stepfun.ai/docs/en/agreement/userprivacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:16:47.619696391Z", "changedAt": "2026-11-06T13:38:27.237331116Z", "fingerprint": "597fa1278404" }, { "url": "https://platform.stepfun.ai/docs/en/agreement/userservice", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:16:49.979240402Z", "changedAt": "2026-11-06T13:38:30.139946008Z", "fingerprint": "2ff748ab87c5" } ], "updatedAt": "2026-11-07T18:20:57.196687446Z" } }, "edges": [ { "by": 54, "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. Bearer API keys, revocable by deletion. Organisations can create one key per project, billed to a project's credit with a per-member cap. No scopes or read-only keys found (20 of 30). Training on API data is not off by default in plain words. The privacy policy (effective 7 August 2026) says StepFun may use and retain data to improve its models, including training and fine-tuning where law and program terms permit, and the terms name early-access programmes as one such case. No opt-out found (5 of 20). No retention period for prompts and outputs and no zero-retention option. IP addresses are kept three years (3 of 15). The console's usage details list each billed call by key, model and date, with export (8 of 15). No security.txt, bug bounty or certification found. A DPA with breach notification exists (5 of 20)." }, "scores": { "a": 95, "b": 41 } }, { "by": 52, "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": "No status page read. status.stepfun.ai refused our reader by robots.txt and the shell couldn't connect, so we couldn't establish whether it is a status page or read any history. Scored absent for both lines, with 5 for unreadable history, and named in the open questions (0 + 5). Rate limits published with numbers per tier, from 5 concurrent, 100 RPM and 500,000 TPM to 130, 2,600 and 13,000,000 (15). The error and exception guides say to retry 429, 500, 503 and 504 after a short delay, with no Retry-After header, backoff schedule or idempotency guidance (8 of 15). No SLA found in the terms or pricing (0). Step 5 Preview is labelled a preview (0)." }, "scores": { "a": 80, "b": 28 } }, { "by": 36, "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. The image models `step-2x-large` and `step-image-edit-2` were retired on 10 October 2026, and a Step 5 Preview price change is announced for 14 October (30). No stated notice period. The 10 October image retirement had a dated notice page and the July retirements a migration table, but we couldn't date when either notice went up (4 of 12). One retirement date in the last 90 days, 10 October, with nine models retired on 8 July just outside that window (6 of 8). Dated announcements, a Discord server and platform@stepfun.com for support. We didn't test whether either answers (10 of 15). No official SDK (0 of 15, 0 of 10)." }, "scores": { "a": 86, "b": 50 } }, { "by": 34, "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": "No OpenAPI or similar machine-readable contract found. The API reference is Markdown pages (0). llms.txt at platform.stepfun.ai/docs/llms.txt, and every page has a Markdown copy (10). The Chat Completions reference describes each parameter with its default, and the model page lists what Step 5 Preview supports, but little on when not to use an option (12 of 20). Tool definitions take JSON Schema parameters, structured output takes a JSON Schema with `strict: true`, and `reasoning_effort` is an enum of low, medium and high. No `tool_choice` parameter is documented (11 of 15). Worked requests and responses on each page, and an error table of eight codes with causes and fixes, but no error bodies (12 of 15). No API changelog. A model lifecycle page with dated retirements and announcement pages (8 of 15)." }, "scores": { "a": 87, "b": 53 } }, { "by": 30, "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 calls through Chat Completions, Responses and Messages, with no documented way to force or forbid a call (15 of 20). Strict JSON Schema output on Step 5 Preview (13 of 15). Prompt caching with a published cache-hit price at a tenth of the miss price (15). 1M-token context and 64k output (15). No batch endpoint found (0). No official SDK. The docs use the OpenAI and Anthropic clients against compatible endpoints (0). Eight error codes with a fix each, plus `finish_reason` handling for length, content filter and tool calls (10 of 15)." }, "scores": { "a": 98, "b": 68 } }, { "by": 26, "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 Hong Kong law, with HKIAC arbitration and liability capped at six months of fees. Some earlier Step models have open weights, which doesn't change the hosted service (15). The privacy policy, terms and DPA agree that data may be used for training under program terms, but the terms' licence to Content is broad and perpetual, no retention period is given for prompts and outputs, and no subprocessor list was found (12 of 30). A model lifecycle page with dated retirements and replacements (14 of 20). Servers are disclosed as in the United States. The DPA gives general authorisation for subprocessors without a published list (10 of 20)." }, "scores": { "a": 83, "b": 57 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Microsoft", "b": "StepFun", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://api.stepfun.ai/v1", "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": "Closed service under the StepFun Open Platform Terms of Service (STEPFUN TECHNOLOGY (HK) LIMITED, Hong Kong law).", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "none", "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": "none", "name": "Popularity" }, { "a": "none", "b": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against StepFun API's 44.3 (E), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Azure OpenAI in Foundry Models or StepFun API?" }, { "answer": "Azure OpenAI in Foundry Models takes an API key or an OAuth sign-in. StepFun API needs an API key.", "question": "Do Azure OpenAI in Foundry Models and StepFun 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 StepFun API at https://api.stepfun.ai/v1.", "question": "Can an agent call Azure OpenAI in Foundry Models and StepFun API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 28", "Schema \u0026 documentation, 87 against 53", "Agent ergonomics, 98 against 68", "Security \u0026 auth, 95 against 41", "Maintenance \u0026 community, 86 against 50", "Transparency \u0026 trust, 83 against 57" ], "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": "Long-context agent and coding work over text, screenshots and video at a low per-token price, where a preview model is acceptable.", "slug": "stepfun-api", "watchFor": "Step 5 Preview is labelled a preview, and no release date or support commitment was found" } ], "history": { "azure-openai": null, "stepfun-api": [ { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 44.4, "fromGrade": "E", "to": 44.3, "toGrade": "E" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.3, "from": 44.1, "fromGrade": "E", "to": 44.4, "toGrade": "E" } ] }, "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-stepfun-api.json", "title": "Amazon Bedrock vs StepFun API", "url": 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"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-stepfun-api.json", "title": "BlockRun.AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-stepfun-api.json", "title": "Cerebras Inference vs StepFun API", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-stepfun-api.json", "title": "Cloudflare AI Gateway vs StepFun API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-stepfun-api.json", "title": "Cloudflare Workers AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-stepfun-api.json", "title": "Cohere Chat API (Command models) vs StepFun API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api.json", "title": "DeepInfra vs StepFun API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-stepfun-api.json", "title": "DeepSeek API vs StepFun API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api.json", "title": "Eden AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-stepfun-api.json", "title": "Glimpse vs StepFun API", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-stepfun-api.json", "title": "Fireworks AI Inference vs StepFun API", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-stepfun-api" }, { "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/groq-vs-stepfun-api.json", "title": "GroqCloud vs StepFun API", "url": "https://www.anchorterminal.com/compare/groq-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api.json", "title": "Hugging Face Inference Providers vs StepFun API", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-stepfun-api.json", "title": "Inkling 2 Small vs StepFun API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api.json", "title": "Jais 3 vs StepFun API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.json", "title": "Meta Model API vs StepFun API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api" }, { "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/mistral-api-vs-stepfun-api.json", "title": "Mistral AI API vs StepFun API", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.json", "title": "Muse Spark Open vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-stepfun-api.json", "title": "Nemotron 3.5 Super VL vs StepFun API", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-stepfun-api.json", "title": "Novita AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-stepfun-api.json", "title": "OpenAI API vs StepFun API", "url": "https://www.anchorterminal.com/compare/openai-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-stepfun-api.json", "title": "OpenRouter vs StepFun API", "url": "https://www.anchorterminal.com/compare/openrouter-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api.json", "title": "Prism Inference vs StepFun API", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-stepfun-api.json", "title": "Router.com (Ramp Router) vs StepFun API", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-stepfun-api.json", "title": "Beam 1.0 vs StepFun API", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-stepfun-api.json", "title": "RouterPlus vs StepFun API", "url": "https://www.anchorterminal.com/compare/routerplus-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api.json", "title": "SambaCloud vs StepFun API", "url": "https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api.json", "title": "SiliconFlow vs StepFun API", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api.json", "title": "Simplismart vs StepFun API", "url": "https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-swe-2-flash.json", "title": "StepFun API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-tencent-tokenhub.json", "title": "StepFun API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-together-inference.json", "title": "StepFun API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-vercel-ai-gateway.json", "title": "StepFun API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai.json", "title": "StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/stepfun-api-vs-xai-api.json", "title": "StepFun API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/stepfun-api-vs-xai-api" } ], "panel": null, "scores": [ { "azure-openai": 80, "by": 52, "edge": "azure-openai", "key": "reliability", "name": "Reliability", "stepfun-api": 28, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-openai": 87, "by": 34, "edge": "azure-openai", "key": "schema", "name": "Schema \u0026 documentation", "stepfun-api": 53, "weight": 13 }, { "azure-openai": 98, "by": 30, "edge": "azure-openai", "key": "ergonomics", "name": "Agent ergonomics", "stepfun-api": 68, "weight": 13 }, { "azure-openai": 95, "by": 54, "edge": "azure-openai", "key": "security", "name": "Security \u0026 auth", "stepfun-api": 41, "weight": 14 }, { "azure-openai": 20, "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "stepfun-api": 20, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-openai": 86, "by": 36, "edge": "azure-openai", "key": "maintenance", "name": "Maintenance \u0026 community", "stepfun-api": 50, "weight": 7 }, { "azure-openai": 83, "by": 26, "edge": "azure-openai", "key": "transparency", "name": "Transparency \u0026 trust", "stepfun-api": 57, "weight": 7 } ], "summary": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against StepFun API's 44.3 (E), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": [ { "a": "$110", "b": "$1.38", "cheaper": "stepfun-api", "task": "Research a product and write a sourced report", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, step-5-preview on StepFun API" }, { "a": "$25.07", "b": "$0.44", "cheaper": "stepfun-api", "task": "Build a small four-page website", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, step-5-preview on StepFun API" }, { "a": "$5.41", "b": "$0.11", "cheaper": "stepfun-api", "task": "Find and fix a bug in a large codebase", "what": "gpt-6-astra on Azure OpenAI in Foundry Models, step-5-preview on StepFun 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.", "stepfun-api": "StepFun's API serves Step 5 Preview with a 1M-token context, video input, strict JSON Schema output and published per-token prices, documented in Markdown with an llms.txt. The model is a preview with no release date found, there's no free allowance or SLA, and the terms and privacy policy leave room to train on customer data." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api", "json": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.min.md" }, "markdown": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against StepFun API's 44.3 (E), 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- StepFun API: grade E, 44.3/100, rank #1206 of 1486. Markdown https://www.anchorterminal.com/tools/stepfun-api.md · JSON https://www.anchorterminal.com/api/v1/tools/stepfun-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 28\n- Schema \u0026 documentation, 87 against 53\n- Agent ergonomics, 98 against 68\n- Security \u0026 auth, 95 against 41\n- Maintenance \u0026 community, 86 against 50\n- Transparency \u0026 trust, 83 against 57\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### StepFun API (E)\n\nGood for: Long-context agent and coding work over text, screenshots and video at a low per-token price, where a preview model is acceptable.\n\nWatch for: Step 5 Preview is labelled a preview, and no release date or support commitment was found\n\n\n## Why the edge\n\n### Security \u0026 auth: Azure OpenAI in Foundry Models +54\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- StepFun API (41): Model reading. Bearer API keys, revocable by deletion. Organisations can create one key per project, billed to a project's credit with a per-member cap. No scopes or read-only keys found (20 of 30). Training on API data is not off by default in plain words. The privacy policy (effective 7 August 2026) says StepFun may use and retain data to improve its models, including training and fine-tuning where law and program terms permit, and the terms name early-access programmes as one such case. No opt-out found (5 of 20). No retention period for prompts and outputs and no zero-retention option. IP addresses are kept three years (3 of 15). The console's usage details list each billed call by key, model and date, with export (8 of 15). No security.txt, bug bounty or certification found. A DPA with breach notification exists (5 of 20).\n\n### Reliability: Azure OpenAI in Foundry Models +52\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- StepFun API (28): No status page read. status.stepfun.ai refused our reader by robots.txt and the shell couldn't connect, so we couldn't establish whether it is a status page or read any history. Scored absent for both lines, with 5 for unreadable history, and named in the open questions (0 + 5). Rate limits published with numbers per tier, from 5 concurrent, 100 RPM and 500,000 TPM to 130, 2,600 and 13,000,000 (15). The error and exception guides say to retry 429, 500, 503 and 504 after a short delay, with no Retry-After header, backoff schedule or idempotency guidance (8 of 15). No SLA found in the terms or pricing (0). Step 5 Preview is labelled a preview (0).\n\n### Maintenance \u0026 community: Azure OpenAI in Foundry Models +36\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- StepFun API (50): Model reading. The image models `step-2x-large` and `step-image-edit-2` were retired on 10 October 2026, and a Step 5 Preview price change is announced for 14 October (30). No stated notice period. The 10 October image retirement had a dated notice page and the July retirements a migration table, but we couldn't date when either notice went up (4 of 12). One retirement date in the last 90 days, 10 October, with nine models retired on 8 July just outside that window (6 of 8). Dated announcements, a Discord server and platform@stepfun.com for support. We didn't test whether either answers (10 of 15). No official SDK (0 of 15, 0 of 10).\n\n### Schema \u0026 documentation: Azure OpenAI in Foundry Models +34\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- StepFun API (53): No OpenAPI or similar machine-readable contract found. The API reference is Markdown pages (0). llms.txt at platform.stepfun.ai/docs/llms.txt, and every page has a Markdown copy (10). The Chat Completions reference describes each parameter with its default, and the model page lists what Step 5 Preview supports, but little on when not to use an option (12 of 20). Tool definitions take JSON Schema parameters, structured output takes a JSON Schema with `strict: true`, and `reasoning_effort` is an enum of low, medium and high. No `tool_choice` parameter is documented (11 of 15). Worked requests and responses on each page, and an error table of eight codes with causes and fixes, but no error bodies (12 of 15). No API changelog. A model lifecycle page with dated retirements and announcement pages (8 of 15).\n\n### Agent ergonomics: Azure OpenAI in Foundry Models +30\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- StepFun API (68): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls through Chat Completions, Responses and Messages, with no documented way to force or forbid a call (15 of 20). Strict JSON Schema output on Step 5 Preview (13 of 15). Prompt caching with a published cache-hit price at a tenth of the miss price (15). 1M-token context and 64k output (15). No batch endpoint found (0). No official SDK. The docs use the OpenAI and Anthropic clients against compatible endpoints (0). Eight error codes with a fix each, plus `finish_reason` handling for length, content filter and tool calls (10 of 15).\n\n### Transparency \u0026 trust: Azure OpenAI in Foundry Models +26\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- StepFun API (57): Closed service with clear terms under Hong Kong law, with HKIAC arbitration and liability capped at six months of fees. Some earlier Step models have open weights, which doesn't change the hosted service (15). The privacy policy, terms and DPA agree that data may be used for training under program terms, but the terms' licence to Content is broad and perpetual, no retention period is given for prompts and outputs, and no subprocessor list was found (12 of 30). A model lifecycle page with dated retirements and replacements (14 of 20). Servers are disclosed as in the United States. The DPA gives general authorisation for subprocessors without a published list (10 of 20).\n\n## Score by category\n\n| Category | Weight | Azure OpenAI in Foundry Models | StepFun API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 28 | Azure OpenAI in Foundry Models +52 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 53 | Azure OpenAI in Foundry Models +34 |\n| Agent ergonomics | 13% (16.2 this run) | 98 | 68 | Azure OpenAI in Foundry Models +30 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 41 | Azure OpenAI in Foundry Models +54 |\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 | 50 | Azure OpenAI in Foundry Models +36 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 57 | Azure OpenAI in Foundry Models +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **80 · A** | **44.3 · E** | |\n\n## Facts side by side\n\n| Fact | Azure OpenAI in Foundry Models | StepFun API |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Microsoft | StepFun |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://api.stepfun.ai/v1` |\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 | Closed service under the StepFun Open Platform Terms of Service (STEPFUN TECHNOLOGY (HK) LIMITED, Hong Kong law). |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-26 | none |\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 | none |\n| Agent reviews | none | 3.3/5 (4) |\n\n## What the same task costs\n\n| Task | Azure OpenAI in Foundry Models | StepFun API |\n| --- | --- | --- |\n| Research a product and write a sourced report | $110 | $1.38 |\n| Build a small four-page website | $25.07 | $0.44 |\n| Find and fix a bug in a large codebase | $5.41 | $0.11 |\n\n## How the scores have moved\n\n### Azure OpenAI in Foundry Models\n\nNo score change since it was listed.\n\n### StepFun API\n\n- -0.1 on 28 October 2026: correction\n- +0.3 on 27 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**StepFun API.** StepFun's API serves Step 5 Preview with a 1M-token context, video input, strict JSON Schema output and published per-token prices, documented in Markdown with an llms.txt. The model is a preview with no release date found, there's no free allowance or SLA, and the terms and privacy policy leave room to train on customer data.\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### StepFun API\n\n1. Set `max_tokens` yourself. It defaults to unlimited on Step 5 Preview, and reasoning tokens are billed as output\n2. Use `response_format` with `type: json_schema` and `strict: true` when the shape matters. JSON Mode alone isn't schema-checked\n3. Back off and retry on 429, 500, 503 and 504. The docs give no Retry-After header or backoff schedule\n4. Expect HTTP 451 or `finish_reason: content_filter` when content fails StepFun's review, and don't retry the same input\n5. Use api.stepfun.ai for the international platform. api.stepfun.com is the China platform with yuan pricing\n\n## Questions\n\n### Which is better for AI agents, Azure OpenAI in Foundry Models or StepFun API?\n\nAzure OpenAI in Foundry Models scores 80 (A) on agent readiness against StepFun API's 44.3 (E), and leads in 6 of 7 scored categories.\n\n### Do Azure OpenAI in Foundry Models and StepFun API need an API key?\n\nAzure OpenAI in Foundry Models takes an API key or an OAuth sign-in. StepFun API needs an API key.\n\n### Can an agent call Azure OpenAI in Foundry Models and StepFun 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 StepFun API at https://api.stepfun.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-openai\", \"b\": \"stepfun-api\"}`. From a terminal: `anchor compare azure-openai stepfun-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-openai.json and https://www.anchorterminal.com/api/v1/tools/stepfun-api.json\n\n## Other comparisons with Azure OpenAI in Foundry Models or StepFun 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 StepFun API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-stepfun-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 StepFun API](https://www.anchorterminal.com/compare/anthropic-api-vs-stepfun-api.md)\n- [Antseed vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/antseed-vs-azure-openai.md)\n- [Antseed vs StepFun API](https://www.anchorterminal.com/compare/antseed-vs-stepfun-api.md)\n- [Azure OpenAI in Foundry Models vs 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AI](https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.md)\n- [Azure OpenAI in Foundry Models vs Glimpse](https://www.anchorterminal.com/compare/azure-openai-vs-elorian-glimpse.md)\n- [Azure OpenAI in Foundry Models vs Fireworks AI Inference](https://www.anchorterminal.com/compare/azure-openai-vs-fireworks-inference.md)\n- [Azure OpenAI in Foundry Models vs Gemini Developer API](https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.md)\n- [Azure OpenAI in Foundry Models vs GroqCloud](https://www.anchorterminal.com/compare/azure-openai-vs-groq.md)\n- [Azure OpenAI in Foundry Models vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/azure-openai-vs-hugging-face-inference-providers.md)\n- [Azure OpenAI in Foundry Models vs Inkling 2 Small](https://www.anchorterminal.com/compare/azure-openai-vs-inkling-2-small.md)\n- [Azure OpenAI in Foundry Models vs Jais 3](https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.md)\n- [Azure OpenAI 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API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-stepfun-api.md)\n- [Cloudflare Workers AI vs StepFun API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-stepfun-api.md)\n- [Cohere Chat API (Command models) vs StepFun API](https://www.anchorterminal.com/compare/cohere-chat-vs-stepfun-api.md)\n- [DeepInfra vs StepFun API](https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api.md)\n- [DeepSeek API vs StepFun API](https://www.anchorterminal.com/compare/deepseek-api-vs-stepfun-api.md)\n- [Eden AI vs StepFun API](https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api.md)\n- [Glimpse vs StepFun API](https://www.anchorterminal.com/compare/elorian-glimpse-vs-stepfun-api.md)\n- [Fireworks AI Inference vs StepFun API](https://www.anchorterminal.com/compare/fireworks-inference-vs-stepfun-api.md)\n- [Gemini Developer API vs StepFun API](https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.md)\n- [GroqCloud vs StepFun API](https://www.anchorterminal.com/compare/groq-vs-stepfun-api.md)\n- [Hugging Face Inference Providers vs StepFun API](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-stepfun-api.md)\n- [Inkling 2 Small vs StepFun API](https://www.anchorterminal.com/compare/inkling-2-small-vs-stepfun-api.md)\n- [Jais 3 vs StepFun API](https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api.md)\n- [Meta Model API vs StepFun API](https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.md)\n- [MiniMax API vs StepFun API](https://www.anchorterminal.com/compare/minimax-api-vs-stepfun-api.md)\n- [Mistral AI API vs StepFun API](https://www.anchorterminal.com/compare/mistral-api-vs-stepfun-api.md)\n- [Muse Spark Open vs StepFun API](https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.md)\n- [Nemotron 3.5 Super VL vs StepFun API](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-stepfun-api.md)\n- [Novita AI vs StepFun API](https://www.anchorterminal.com/compare/novita-ai-vs-stepfun-api.md)\n- [OpenAI API vs StepFun API](https://www.anchorterminal.com/compare/openai-api-vs-stepfun-api.md)\n- [OpenRouter vs StepFun API](https://www.anchorterminal.com/compare/openrouter-vs-stepfun-api.md)\n- [Prism Inference vs StepFun API](https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api.md)\n- [Router.com (Ramp Router) vs StepFun API](https://www.anchorterminal.com/compare/ramp-router-vs-stepfun-api.md)\n- [Beam 1.0 vs StepFun API](https://www.anchorterminal.com/compare/reflection-beam-vs-stepfun-api.md)\n- [RouterPlus vs StepFun API](https://www.anchorterminal.com/compare/routerplus-vs-stepfun-api.md)\n- [SambaCloud vs StepFun API](https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api.md)\n- [SiliconFlow vs StepFun API](https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api.md)\n- [Simplismart vs StepFun API](https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api.md)\n- [StepFun API vs SWE-2 Flash](https://www.anchorterminal.com/compare/stepfun-api-vs-swe-2-flash.md)\n- [StepFun API vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/stepfun-api-vs-tencent-tokenhub.md)\n- [StepFun API vs Together AI Inference](https://www.anchorterminal.com/compare/stepfun-api-vs-together-inference.md)\n- [StepFun API vs Vercel AI Gateway](https://www.anchorterminal.com/compare/stepfun-api-vs-vercel-ai-gateway.md)\n- [StepFun API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/stepfun-api-vs-vertex-ai.md)\n- [StepFun API vs xAI API (Grok)](https://www.anchorterminal.com/compare/stepfun-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 StepFun API", "url": "" } ], "description": "Azure OpenAI in Foundry Models scores 80 (A) to StepFun's 44.3 (E) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Azure OpenAI in Foundry Models A 80", "StepFun API E 44.3", "scores" ], "h1": "Azure OpenAI in Foundry Models vs StepFun API", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/azure-openai-vs-stepfun-api", "published": "2026-10-01", "section": "tools", "title": "Azure OpenAI in Foundry Models vs StepFun for AI agents (2026)", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api" }, "tokens": { "markdown": 7300, "slim": 3730 }, "version": 1 }