{ "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 Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on payments \u0026 pricing.", "b": { "slug": "eden-ai", "name": "Eden AI", "vendor": "Eden AI SAS", "vendorUrl": "https://www.edenai.co", "kind": "router", "category": "inference", "summary": "Eden AI is a French AI gateway with one OpenAI-compatible API key for about 1,000 chat and expert models from 80 or more providers, including OCR, speech, translation and image analysis, with an EU-only endpoint and a hosted MCP server.", "url": "https://www.anchorterminal.com/tools/eden-ai", "markdownUrl": "https://www.anchorterminal.com/tools/eden-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/eden-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/eden-ai.json", "dns": "eden-ai.grade.anchorterminal.com", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://mcp.edenai.run/mcp", "packages": [], "auth": "api-key", "authNotes": "Bearer key created in the dashboard at app.edenai.run after a browser signup. Keys carry budgets, expiry and attribution, can be managed through a key API, and can be limited to chosen models by deny-by-default guardrails. The MCP server takes the same key in the Authorization header.", "pricing": "usage", "pricingNotes": "Provider list prices with no markup plus a 5.5% platform fee applied when buying credits. Prepaid credits through Stripe by card, PayPal or bank transfer, with optional auto-refill. Some models are billed $0 for tests. The Advanced plan is by quote.", "priceSummary": "5.5% fee", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in llms.txt, the OpenAPI spec, the pricing pages or the agent catalogue (checked 2026-11-06). Credits are bought through Stripe.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://www.edenai.co/docs", "llmsTxt": "https://www.edenai.co/llms.txt", "openapi": "https://api.edenai.run/v3/docs/openapi.json", "capabilities": [ "inference.router", "inference.llm", "embed.text", "translate.text", "translate.documents", "docs.ocr", "speech.stt", "speech.tts", "image.generate", "guard.moderation" ], "tags": [ "hosted", "router", "openai-compatible", "usage-priced", "prepaid", "mcp", "remote-mcp", "api-key", "llms-txt", "openapi", "eu", "eu-region", "soc2", "iso27001", "batch", "closed-source" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 59.9, "grade": "C", "agentReady": false, "rank": 672, "ranked": true, "rankOf": 1486, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 58, "payments": 30, "reliability": 33, "schema": 81, "security": 82, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.", "bestFor": "Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.", "strengths": [ "OpenAI-compatible v3 API with an OpenAPI 3.1 spec and a keyless model catalogue with prices and regions", "EU endpoint that refuses models not cleared for European processing with HTTP 451", "Content isn't stored or used for training by default, with opt-in logs and enforceable Zero Data Retention", "Per-key budgets, expiry and deny-by-default guardrails, plus a key management API", "Hosted MCP server exposing OCR, speech, translation, web search and other expert models as tools" ], "weaknesses": [ "No public status page, incident history or SLA found", "The rate-limits page says 10 requests a second and the plans page says 7, raisable to 15", "No public changelog or deprecation policy", "The OpenAPI spec documents few error responses", "Credits are prepaid through Stripe with a 5.5% platform fee on every purchase" ], "agentNotes": [ "Read GET https://api.edenai.run/v3/models at run time for model ids, prices and regions instead of hard-coding them", "Use https://api.eu.edenai.run/v3 when data must stay in the EU, and pick another model on a 451 region_not_allowed", "Set max_tokens to 500 or more for reasoning models, or the answer can come back empty", "Pass a fallbacks list so a provider 429 or error retries inside one request", "Poll check_job for MCP tools marked long-running instead of calling them again" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.9 } ], "editorialScores": { "ergonomics": 75, "maintenance": 58, "payments": 30, "reliability": 33, "schema": 81, "security": 82, "transparency": 65 }, "provenanceScore": 44, "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": { "http": "curl https://api.edenai.run/v3/models", "claudeCode": "claude mcp add edenai --transport http https://mcp.edenai.run/mcp --header \"Authorization: Bearer $EDENAI_API_KEY\"", "config": { "mcpServers": { "edenai": { "headers": { "Authorization": "Bearer YOUR_EDENAI_API_KEY" }, "url": "https://mcp.edenai.run/mcp" } } } }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/eden-ai" }, "area": "models", "unitPrices": [ { "item": "Platform fee on credit purchases", "unit": "pct", "usd": 5.5, "note": "On top of provider list prices, which carry no markup" } ], "provenance": { "legalEntity": "Eden AI SAS", "domain": "edenai.co", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://www.edenai.co/terms", "privacy": "https://www.edenai.co/privacy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The terms, last updated 22 October 2026, name Eden AI SAS, share capital €4,992, 19 rue Louis Guérin, 69100 Villeurbanne, France, under French law with courts in Lyon.", "The API and MCP server run on edenai.run, a second domain of the same company, while the site and docs are on edenai.co.", "robots.txt on www.edenai.co allows all agents and carries `Content-Signal: search=yes, ai-input=yes, ai-train=yes`.", "/.well-known/security.txt returns 404. No status page is linked, and status.edenai.co didn't resolve on 6 November 2026.", "RDAP for edenai.co wasn't read: rdap.org rate-limited us and the .co registry server wasn't tried." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/eden-ai.json", "live": { "slug": "eden-ai", "probe": { "target": "https://mcp.edenai.run/mcp", "method": "get", "lastAt": "2026-11-07T18:20:22.496540152Z", "lastOk": true, "lastStatus": 405, "lastMs": 123, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 50, "p95ms24h": 129, "samples24h": 200, "samples30d": 200, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "securityTxt": { "url": "https://edenai.co/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T15:59:41.694549385Z" }, "llmsTxt": { "url": "https://www.edenai.co/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:20.299431316Z" }, "pages": [ { "url": "https://www.edenai.co/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:28:13.706474894Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "21a1da47a2ce" }, { "url": "https://www.edenai.co/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:28:15.881335606Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9cd0db8f0f61" } ], "mcpTools": { "url": "https://mcp.edenai.run/mcp", "checkedAt": "2026-11-07T01:44:01.27972989Z", "status": "ok", "protocol": "2025-11-25", "tools": [ { "name": "list_models", "description": "List available providers/models and their pricing.", "inputSchema": { "additionalProperties": false, "properties": { "tool": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Optional tool name (as returned by tools/list) to filter on." } }, "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "list_voices", "description": "List the voices a text-to-speech model accepts, for audio_tts's 'voice'.\n\nThe model's default voice comes first.", "inputSchema": { "additionalProperties": false, "properties": { "gender": { "anyOf": [ { "enum": [ "female", "male", "unknown" ], "type": "string" }, { "type": "null" } ], "default": null, "description": "Keep voices of this gender." }, "language": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Keep voices speaking this language: 'fr' matches every region,\n'fr-CA' only that locale. Multilingual voices are always kept." }, "limit": { "default": 50, "description": "Most voices to return (default 50, max 200); 'total' counts every match.", "type": "integer" }, "model": { "description": "The audio_tts model, as 'provider' or 'provider/model' (e.g. 'amazon/neural').", "type": "string" } }, "required": [ "model" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "upload_file", "description": "Upload a file to Eden AI and get a file ID to use in other tools.\n\nOnly needed for local file content. If you already have a public URL\nfor the file, pass that URL directly to the tool's file parameter\ninstead — no upload needed.", "inputSchema": { "additionalProperties": false, "properties": { "content_base64": { "description": "The file content, base64-encoded.", "type": "string" }, "expires_in_days": { "default": 30, "description": "How long Eden AI keeps the file (1-30, default 30).", "type": "integer" }, "filename": { "description": "Name with extension (used to detect the file type).", "type": "string" } }, "required": [ "content_base64", "filename" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "check_job", "description": "Get the status/result of an async job started by a long-running tool.", "inputSchema": { "additionalProperties": false, "properties": { "job_id": { "description": "The job id returned when the job was submitted.", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "manage_collections", "description": "Create and manage collections: stored sets of items (e.g. faces) that a tool searches against. Tools that take a collection_id: image_face_recognition. Flow: create, then add_item once per file (returns the item ids it indexed), then call the tool with the collection_id; delete when done. Arguments per action: create(model, tool?, name?); list(tool?, model?, page?); get, delete, list_items(collection_id); add_item(collection_id, file); delete_item(collection_id, item_id).", "inputSchema": { "additionalProperties": false, "properties": { "action": { "enum": [ "create", "list", "get", "delete", "add_item", "list_items", "delete_item" ], "type": "string" }, "collection_id": { "description": "From create or list.", "type": "string" }, "file": { "description": "File to add (add_item). Pass a file ID (UUID) from upload_file, or a direct file URL.", "type": "string" }, "item_id": { "description": "Item to delete (delete_item), from add_item or list_items.", "type": "string" }, "model": { "description": "Provider the collection binds to (create; list filter). image_face_recognition: amazon.", "type": "string" }, "name": { "description": "Optional label (create).", "type": "string" }, "page": { "description": "Result page (list).", "minimum": 1, "type": "integer" }, "tool": { "description": "Tool the collection serves (create; list filter). Defaults to image_face_recognition.", "enum": [ "image_face_recognition" ], "type": "string" } }, "required": [ "action" ], "type": "object" } }, { "name": "audio_speech_to_text", "description": "Speech Recognition (or speech to text or voice to text) can recognize and transcribe spoken words (voice), that will be converted to text with or without diarization. It is also possible to filter profanity. The type of files allowed are: mp3, wav, m4a. If convert_to_wav option is set to true, then you can transcribe any type of audio or video files. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Audio file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: audio/*.", "type": "string" }, "language": { "description": "Language code in ISO format (e.g., 'en', 'fr', 'es')", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, assembly, deepgram, deepgram/base, deepgram/enhanced, deepgram/nova-3, gladia, google, gradium, gradium/stt-translate, greenpt, greenpt/green-s, greenpt/green-s-pro, microsoft, mistral, mistral/voxtral-mini-2602, mistral/voxtral-mini-latest, mistral/voxtral-mini-transcribe-3, mistral/voxtral-small-transcribe-3, openai. Call list_models for pricing.", "type": "string" }, "profanity_filter": { "default": false, "description": "Whether to filter profanity and replace inappropriate words with a series of asterisks", "type": "boolean" }, "speakers": { "default": 2, "description": "Number of speakers present in the audio", "minimum": 1, "type": "integer" }, "vocabulary": { "description": "List of words or composed words to be detected by the speech to text engine", "items": { "type": "string" }, "type": "array" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "audio_tts", "description": "Text-to-speech (TTS) is a technology that converts written text into spoken audio, enabling computers and digital devices to \"read\" text aloud. It is also known as speech synthesis, read aloud technology, or computer-generated speech.", "inputSchema": { "additionalProperties": false, "properties": { "audio_format": { "description": "Desired audio format (e.g., 'mp3', 'wav')", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon/neural, amazon/standard, deepgram/aura, deepgram/aura-2, elevenlabs, elevenlabs/eleven_flash_v2, elevenlabs/eleven_flash_v2_5, elevenlabs/eleven_multilingual_v2, elevenlabs/eleven_turbo_v2, elevenlabs/eleven_turbo_v2_5, elevenlabs/eleven_v3, google/casual, google/chirp-hd, google/chirp3-hd, google/gemini-2.5-flash-tts, google/gemini-2.5-pro-tts, google/gemini-3.1-flash-tts-preview, google/neural2, google/news, google/polyglot, google/standard, google/studio, google/wavenet, gradium, microsoft/neural, microsoft/neural-hd, mistral/voxtral-mini-tts-3, openai/gpt-4o-mini-tts, openai/tts-1, openai/tts-1-hd, ovhcloud/nvr-tts-de-de, ovhcloud/nvr-tts-en-us, ovhcloud/nvr-tts-es-es, ovhcloud/nvr-tts-it-it. Call list_models for pricing.", "type": "string" }, "speaking_pitch": { "description": "Increase or decrease the speaking pitch by a percentage from -100% to 100%, 0 is normal.", "maximum": 100, "minimum": -100, "type": "integer" }, "speaking_volume": { "description": "Increase or decrease the audio volume by a percentage from -100% to 100%, 0 is normal.", "maximum": 100, "minimum": -100, "type": "integer" }, "speed": { "description": "Speed multiplier for speech synthesis", "maximum": 4, "minimum": 0.25, "type": "number" }, "text": { "description": "Text to convert to speech", "minLength": 1, "type": "string" }, "voice": { "description": "Voice to synthesize with: an id or name from list_voices(model=...), matched case-insensitively. Omit it to use the model's default voice. An id the list doesn't know (e.g. your own cloned voice) is sent to the provider unchanged.", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "image_ai_detection", "description": "Determine whether an image was generated by AI or is a real photograph or artwork. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble, winstonai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_anonymization", "description": "Image Anonymization API, also known as image de-identification or image de-personalization, refers to the process of automatically removing personal or sensitive information from images. The main objective is to protect the privacy and security of individuals who may be depicted in images shared online or used for research purposes. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_background_removal", "description": "Background removal is a digital image processing technique designed to seamlessly eliminate the backdrop of a photo, leaving behind only the main subject. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai, clipdrop, photoroom, picsart, sentisight, stabilityai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_deepfake_detection", "description": "Detect whether a face in a real photo has been swapped or manipulated (face-swap deepfakes). For fully AI-generated images, use AI Image Detection instead. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble, sightengine. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_explicit_content", "description": "Explicit Content Detection detects adult only content in images, that is generally inappropriate for people under the age of 18 and includes nudity, sexual activity, pornography, violence, gore content, etc. (type of files allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, google, microsoft, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_face_compare", "description": "Compare two faces and decide whether they are from the same person. The API expects 2 images, reference and query, where the former is the ground truth (e.g. user's official ID) and the latter is the validation image (e.g. a selfie). File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file1": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "file2": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, base64, facepp. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file1", "file2" ], "type": "object" } }, { "name": "image_face_detection", "description": "Face Detection is a computer technology being used in a variety of applications that identifies human faces in digital images with different attributes like landmarks, sentiments and physical attire (type of file allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_face_recognition", "description": "Recognizing a face in an image is a more advanced feature than just detecting the face in the image. It allows the user to search for the detected face from a database of existing images and extract the faces that are most similar to the face displayed in the query image. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "collection_id": { "description": "Id of one of your collections. Make one with manage_collections (action='create', then add_item for each file to search against); action='list' shows the ones you have. 'model' must be the collection's provider.", "type": "string" }, "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "collection_id", "file" ], "type": "object" } }, { "name": "image_generation", "description": "Image Generation is an advanced feature that generates compelling images based on a given text prompt. It can easily produce high-quality and original images in a matter of seconds, without the need for specialized design skills or software File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "mask": { "description": "Optional inpainting mask. Must be paired with at least one reference image (otherwise the request returns 422). Support varies by model; models that do not accept a mask surface the error as status=\"fail\" with a provider message in the response body. Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: bytedance, bytedance/seedream-4-0-250828, bytedance/seedream-4-5-251128, bytedance/seedream-5-0-260128, google, google/gemini-2.5-flash-image, google/gemini-3-pro-image-preview, infomaniak/flux, ionos/black-forest-labs/FLUX.1-schnell, ionos/black-forest-labs/FLUX.2-klein-4B, leonardo, leonardo/AlbedoBase XL, leonardo/Leonardo Anime XL, leonardo/Leonardo Diffusion XL, leonardo/Leonardo Kino XL, leonardo/Leonardo Lightning XL, leonardo/Leonardo Phoenix, leonardo/Leonardo Vision XL, leonardo/SDXL 0.9, minimax, minimax/image-01, openai, openai/gpt-image-1, openai/gpt-image-1-mini, openai/gpt-image-1.5, openai/gpt-image-2, ovhcloud/stable-diffusion-xl-base-v10, pruna/p-image, pruna/p-image-edit, pruna/p-image-ideogram, pruna/p-image-ideogram-low, pruna/p-image-ideogram-medium, pruna/p-image-ideogram-very-high, pruna/p-image-ideogram-very-low, replicate, replicate/anime-style, replicate/classic, replicate/vintedois-diffusion, stabilityai, stabilityai/stable-diffusion-xl-1024-v1-0. Call list_models for pricing.", "type": "string" }, "num_images": { "default": 1, "description": "Number of images to generate", "maximum": 10, "minimum": 1, "type": "integer" }, "reference_images": { "description": "Optional reference images. Plain file IDs/URLs are treated as ReferenceImage with kind=None. Support and per-model limits vary by model; models that do not accept reference images surface the error as status=\"fail\" with a provider message in the response body.", "items": {}, "maxItems": 64, "type": "array" }, "resolution": { "description": "Image resolution as a named size (e.g. '1024x1024'). Optional: providers may also accept native sizing knobs (width/height, aspect_ratio, image_size tier, etc.) in the top-level `provider_params` request field, next to `input` rather than inside it, in which case `provider_params` takes precedence.", "pattern": "^\\d+x\\d+$", "type": "string" }, "text": { "description": "Text prompt for image generation", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "image_logo_detection", "description": "Logo Detection is a powerful technology that enables the automatic identification and recognition of popular logos within an image. It provides accurate and efficient detection of known (and less known) brand logos. It's a valuable tool for a variety of industries, including advertising, retail, and e-commerce. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai, api4ai/v1, api4ai/v2, google, microsoft, openai, openai/gpt-4-turbo, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_object_detection", "description": "Object Detection is a computer vision technique that allows users to identify and locate (with bounding boxes) objects in an image. The detected objects can be animals, people, electronics, vehicles etc. (type of file allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google, microsoft, sentisight. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr", "description": "Optical Character Recognition or Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text. The image can be a scanned document or a photo of a license plate, a road sign or anything else that contains text (type of files allowed: jpg, jpeg, png, tiff) . File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "language": { "description": "Document language code. Most providers detect the language themselves and ignore this field; providers that cannot detect it reject requests that omit it. Check the provider's supported languages before relying on it.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google, ionos, ionos/lightonai/LightOnOCR-2-1B, microsoft, mistral, mistral/mistral-ocr-2512, mistral/mistral-ocr-3, mistral/mistral-ocr-3-0, mistral/mistral-ocr-4, mistral/mistral-ocr-4-0, mistral/mistral-ocr-4-1, mistral/mistral-ocr-latest, sentisight. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_async", "description": "OCR or Optical Character Recognition is also referred to as text recognition or text extraction. It allows users to extract text data from PDFs with multiple pages. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, microsoft, mistral, mistral/mistral-ocr-2512, mistral/mistral-ocr-3, mistral/mistral-ocr-3-0, mistral/mistral-ocr-4, mistral/mistral-ocr-4-0, mistral/mistral-ocr-4-1, mistral/mistral-ocr-latest. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_financial_parser", "description": "Financial Parser API is a powerful and versatile tool designed to streamline your financial data extraction process. This cutting-edge API combines Optical Character Recognition (OCR) technology with advanced parsing capabilities to seamlessly extract and organize relevant information from invoices and receipts. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "document_type": { "default": "invoice", "description": "Specify the type of your document. Can be set to 'auto-detect' for automatic detection if the provider supports it.", "enum": [ "auto-detect", "invoice", "receipt" ], "type": "string" }, "file": { "description": "File ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*, application/*, text/*.", "type": "string" }, "language": { "description": "Document language code. Most providers detect the language themselves and ignore this field; providers that cannot detect it reject requests that omit it. Check the provider's supported languages before relying on it.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, amazon, base64, eagledoc, extracta, google, klippa, microsoft, mindee, openai, openai/gpt-4o, tabscanner, veryfi. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_identity_parser", "description": "ID Document parsing technology allows users to automatically extract information from an ID Document such as passport, ID card, driving license and more. This API is ideal for developers looking to create applications that require identity verification or access control. It extracts information like name, date of birth, nationality, age and more. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, amazon, base64, klippa, microsoft, mindee, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_resume_parser", "description": "Resume Parser enables users to extract various information from resumes (curriculum vitae, CV) that could be in a variety of formats and returns structured data (name, job list, education, skills) to automate their processing. (type of files allowed: pdf, jpg, jpeg, png) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "File ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*, application/*, text/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, extracta, klippa, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_tables", "description": "OCR Table (Optical Character Recognition for tabular documents) allows users to analyze documents containing tables and returns a structured representation of the detected tables in the form of a Json object that can be converted to a CSV. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, google, microsoft. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "text_ai_detection", "description": "The AI Detection API is a tool designed to determine whether a given text was generated by an artificial intelligence chatbot, or a Language Model (LLM).", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: sapling, winstonai. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for AI detection, at least 300 characters long. Shorter texts carry too little signal to be scored reliably.", "minLength": 300, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_anonymization", "description": "The personally identifiable information (PII) detection feature can identify and redact sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. Removing these personally identifiable information helps with the Anonymization of a text and privacy protection.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, limina, microsoft. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to detect personal information in", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_moderation", "description": "Text moderation scans text for offensive, sexually explicit or suggestive content, it also checks if there is any content of self-harm, violence, racist or hate speech.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: google, microsoft, openai, openai/text-moderation-007, openai/text-moderation-latest, openai/text-moderation-stable, ovhcloud/Qwen3Guard-Gen-0.6B, ovhcloud/Qwen3Guard-Gen-8B. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to moderate", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_named_entity_recognition", "description": "Named Entity Recognition (also called entity identification or extraction) is an information extraction technique that automatically identifies named entities in a text and classifies them into predefined categories such as dates, persons names, company names, dates, products etc.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, microsoft, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for named entities", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_plagia_detection", "description": "The Plagiarism Detection API is a tool designed to scan a content string for plagiarism using a plagiarism detection module", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: winstonai. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text content on which plagiarism detection will be run", "minLength": 1, "type": "string" }, "title": { "description": "Content title", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_spell_check", "description": "Spell Check analyzes the inputted text by comparing it to a dictionary of correctly spelled words to identify and highlight any spelling errors or grammatical mistakes.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: prowritingaid, sapling. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to be spell-checked", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_topic_extraction", "description": "Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: google, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for topics", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "translation_automatic_translation", "description": "Automatic translation or machine translation (MT) is the process of automatically translating a text from one language to another language. The aim of it is to enable people to communicate and access information in different languages, without the need for a human translator.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: amazon, deepl, google, microsoft, modernmt, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "source_language": { "description": "Source language code", "minLength": 1, "type": "string" }, "target_language": { "description": "Target language code", "minLength": 1, "type": "string" }, "text": { "description": "Text to translate", "minLength": 1, "type": "string" } }, "required": [ "model", "text", "target_language" ], "type": "object" } }, { "name": "translation_document_translation", "description": "Document Translation API translates whole documents in supported languages and various file formats (like pdf or doc) while keeping their structure intact. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Document file ID from /v3/upload or direct file URL (PDF, DOCX, PPTX) Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/vnd.openxmlformats-officedocument.presentationml.presentation.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: deepl, google. Call list_models for pricing.", "type": "string" }, "source_language": { "description": "Source language code", "minLength": 1, "type": "string" }, "target_language": { "description": "Target language code", "minLength": 1, "type": "string" } }, "required": [ "model", "file", "target_language" ], "type": "object" } }, { "name": "video_deepfake_detection", "description": "Detect deepfake and synthetically manipulated video. Returns an overall deepfake score and prediction, along with per-frame scores when the provider supplies them. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Video file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: video/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "video_generation", "description": "The Video Generation API enables easy creation of high-quality videos from text,and images, perfect for marketing, education, and more—all through one integration. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "dimension": { "default": "1280x720", "description": "Video resolution in 'widthxheight' format (e.g., '1280x720')", "pattern": "^\\d+x\\d+$", "type": "string" }, "duration": { "description": "Length of the generated video in seconds. Omit to use the model's default duration.", "maximum": 120, "minimum": 1, "type": "integer" }, "file": { "description": "Initial keyframe image (JPEG or PNG) file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/jpeg, image/png.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: bytedance, bytedance/dreamina-seedance-2-0-260128, bytedance/dreamina-seedance-2-0-fast-260128, bytedance/dreamina-seedance-2-0-mini-260615, bytedance/dreamina-seedance-2-5-260628, bytedance/seedance-1-0-pro-250528, bytedance/seedance-1-0-pro-fast-251015, bytedance/seedance-1-5-pro-251215, minimax, minimax/MiniMax-Hailuo-02, minimax/MiniMax-Hailuo-2.3, minimax/S2V-01, minimax/T2V/I2V-01-Director, openai, openai/sora-2, openai/sora-2-pro, pixverse, pixverse/c1, pixverse/v5, pixverse/v5.5, pixverse/v6, pruna, pruna/p-video, pruna/p-video-2, pruna/p-video-2-pro, pruna/p-video-2-pro-cost, pruna/p-video-2-pro-quality, pruna/p-video-animate, pruna/p-video-avatar, pruna/p-video-edit, xai/grok-imagine-video-1.5, xai/grok-imagine-video-1.5-lite. Call list_models for pricing.", "type": "string" }, "seed": { "description": "Random seed for generation reproducibility (0 to 2,147,483,646). Omit for varied output.", "maximum": 2147483646, "minimum": 0, "type": "integer" }, "text": { "description": "Text prompt describing the content and style of the video to generate", "minLength": 1, "type": "string" }, "video": { "description": "Source video to edit, for models that edit a video (their `requires_video` is true in /v3/info). The output keeps the source's length, which is what is billed, so `duration` and `dimension` do not apply to them. Other models refuse it. Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: video/*.", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "web_batch_scrape", "description": "Scrape many URLs in one request and return each page's content. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl. Call list_models for pricing.", "type": "string" }, "urls": { "description": "Explicit list of URLs to scrape (async job)", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "model", "urls" ], "type": "object" } }, { "name": "web_crawl", "description": "Crawl a site from a starting URL and return the content of its pages. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "Root URL to crawl (async job)", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_map", "description": "Discover and return the list of URLs on a site (a sitemap-style link map).", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "Root URL to map (discover its links)", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_research", "description": "Run deep, multi-step web research on a query and return a synthesized answer with sources. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, linkup, tavily. Call list_models for pricing.", "type": "string" }, "prompt": { "description": "Natural-language research task for the agent", "minLength": 1, "type": "string" }, "reasoning_depth": { "default": "L", "description": "Research reasoning-effort tier (billed per tier; priced pre-call)", "enum": [ "S", "M", "L", "XL" ], "type": "string" }, "schema": { "description": "JSON schema describing the structured output", "type": "object" } }, "required": [ "model", "prompt" ], "type": "object" } }, { "name": "web_scraping", "description": "Fetch a single URL and return its clean content as markdown, text, or HTML, with optional JavaScript rendering.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: deepseek, firecrawl, linkup, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "URL of the web page to scrape", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_search", "description": "Search the web for a query and return ranked results (title, URL, and a snippet or the page content).", "inputSchema": { "additionalProperties": false, "properties": { "depth": { "default": "standard", "description": "Search depth tier. `deep` is refused with a 400 on deepseek, perplexityai and zai, which run one tier only.", "enum": [ "standard", "deep" ], "type": "string" }, "max_results": { "description": "Max number of search results to return", "minimum": 1, "type": "integer" }, "model": { "description": "Which provider/model to run, exactly one of: deepseek, firecrawl, linkup, perplexityai, tavily, zai. Call list_models for pricing.", "type": "string" }, "query": { "description": "The web search query", "minLength": 1, "type": "string" } }, "required": [ "model", "query" ], "type": "object" } }, { "name": "web_structured_extraction", "description": "Extract structured data from one or more web pages against a schema or prompt. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl. Call list_models for pricing.", "type": "string" }, "prompt": { "description": "Natural-language description of what to extract", "type": "string" }, "schema": { "description": "JSON schema describing the structured output", "type": "object" }, "urls": { "description": "URLs to extract structured data from (async job)", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "model", "urls" ], "type": "object" } } ], "schemaTokens": 10725, "changedAt": "2026-11-07T01:44:01.27972989Z", "check": { "checker": "anchor-check/1.0", "totalTokens": 10725, "counts": { "error": 0, "note": 1, "warn": 49 }, "findings": [ { "rule": "TC11", "severity": "warn", "tool": "manage_collections", "message": "1 parameter without a description: action", "fix": "Describe each one: format, units, an example, and what happens when it's left out." }, { "rule": "TC12", "severity": "warn", "tool": "image_generation", "message": "no type for reference_images[]", "fix": "Give every parameter a type (or an enum)." }, { "rule": "TC13", "severity": "warn", "tool": "web_research", "message": "schema (object with no properties)", "fix": "Declare the properties (or additionalProperties with a schema) and the array's items." }, { "rule": "TC13", "severity": "warn", "tool": "web_structured_extraction", "message": "schema (object with no properties)", "fix": "Declare the properties (or additionalProperties with a schema) and the array's items." }, { "rule": "TC14", "severity": "warn", "tool": "image_face_recognition", "message": "allowed values are in the description, not an enum: collection_id", "fix": "Move them into enum." }, { "rule": "TC14", "severity": "warn", "tool": "image_generation", "message": "allowed values are in the description, not an enum: resolution", "fix": "Move them into enum." }, { "rule": "TC16", "severity": "warn", "tool": "audio_speech_to_text", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "audio_tts", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "check_job", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"check\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "image_ai_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_anonymization", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_background_removal", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_deepfake_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_explicit_content", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_compare", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_recognition", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_generation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_logo_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_object_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "list_models", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"list\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "list_voices", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"list\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "manage_collections", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_async", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_financial_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_identity_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_resume_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_tables", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_ai_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_anonymization", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_moderation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_named_entity_recognition", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_plagia_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_spell_check", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"check\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "text_topic_extraction", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "translation_automatic_translation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "translation_document_translation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "upload_file", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "video_deepfake_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "video_generation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_batch_scrape", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_crawl", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_map", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_research", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_scraping", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_search", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"search\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "web_structured_extraction", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC23", "severity": "warn", "message": "42 tools", "fix": "Offer a smaller default toolset and let clients opt into the rest." }, { "rule": "TC24", "severity": "note", "message": "38 of 42 tools have no outputSchema", "fix": "Declare outputSchema for tools that return structured data, and return structuredContent that matches it." } ] } }, "updatedAt": "2026-11-07T18:20:22.496540152Z" } }, "edges": [ { "by": 47, "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": "Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support \u0026 SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33." }, "scores": { "a": 80, "b": 33 } }, { "by": 28, "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": "Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58." }, "scores": { "a": 86, "b": 58 } }, { "by": 28, "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": "Editorial half only. Closed service under terms last updated 22 October 2026, naming Eden AI SAS and French law with courts in Lyon (15). The data retention page, terms and DPA agree that content isn't stored by default, and the docs carry a per-provider table of training, retention and ZDR (27). No deprecation policy found; models are dropped in docs updates without dated notice that we found (5). The DPA's Annex 2 names operational subprocessors (AWS EMEA, ClickHouse, Sentry, Metabase, Stripe, Brevo and others) with their roles, and the EU endpoint keeps routing in the EU (18). 15+27+5+18 = 65." }, "scores": { "a": 83, "b": 55 } }, { "by": 23, "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": "Router, read on the model lines. Chat completions, Responses and an Anthropic Messages route in the OpenAI shape, with tool calling depending on the routed model (14 of 20) and structured output likewise (10 of 15). Caching is named in the terms as part of the service, without a per-model price table we read (8 of 15). Context depends on the model; the keyless /v3/models catalogue lists capabilities, prices and regions per model so an agent can choose (12 of 15). A Batches API (10 of 10). No SDK of its own; the docs use the OpenAI Python and TypeScript SDKs, and a hosted MCP server exposes about 40 expert-model tools plus upload_file, check_job and list_models (21 of 25). 14+10+8+12+10+21 = 75." }, "scores": { "a": 98, "b": 75 } }, { "by": 13, "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": "Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82." }, "scores": { "a": 95, "b": 82 } }, { "by": 10, "category": "payments", "leads": "eden-ai", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-1M-token prices are public in the Azure Retail Prices API without a login, while the pricing page fills its table with JavaScript and showed GPT-6 prices as pending on 6 October (20). The quota tiers include a Free tier, but an Azure subscription needs a payment method (0). A person signs up for Azure in a browser and creates a resource and a deployment before the first call (0).", "b": "No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30." }, "scores": { "a": 20, "b": 30 } } ], "facts": [ { "a": "Model API", "b": "Model router", "name": "Kind" }, { "a": "Microsoft", "b": "Eden AI SAS", "name": "Vendor" }, { "a": "https://\u003cresource\u003e.openai.azure.com/openai/v1", "b": "https://mcp.edenai.run/mcp", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP, Streamable 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": "none", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-11-05", "name": "Last release" }, { "a": "no date given", "b": "2026-10-22", "name": "Terms last updated" }, { "a": "2026-09-01", "b": "2026-10-22", "name": "Privacy policy last updated" }, { "a": "yes", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "454k npm/wk", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Azure OpenAI in Foundry Models or Eden AI?" }, { "answer": "Azure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Eden AI needs an API key.", "question": "Do Azure OpenAI in Foundry Models and Eden AI 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 Eden AI at https://mcp.edenai.run/mcp.", "question": "Can an agent call Azure OpenAI in Foundry Models and Eden AI without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 80 against 33", "Schema \u0026 documentation, 87 against 81", "Agent ergonomics, 98 against 75", "Security \u0026 auth, 95 against 82", "Maintenance \u0026 community, 86 against 58", "Transparency \u0026 trust, 83 against 55" ], "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": [ "Payments \u0026 pricing, 30 against 20" ], "also": null, "goodFor": "Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.", "slug": "eden-ai", "watchFor": "No public status page, incident history or SLA found" } ], "history": null, "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-eden-ai.json", "title": "Amazon Bedrock vs Eden AI", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai" }, { "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-eden-ai.json", "title": "Claude API vs Eden AI", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai" }, { "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/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-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": "https://www.anchorterminal.com/compare/azure-openai-vs-inkling-2-small.json", "title": "Azure OpenAI in Foundry Models vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.json", "title": "Azure OpenAI in Foundry Models vs Jais 3", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-meta-model-api.json", "title": "Azure OpenAI in Foundry Models vs Meta Model API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.json", "title": "Azure OpenAI in Foundry Models vs MiniMax API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-mistral-api.json", "title": "Azure OpenAI in Foundry Models vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open.json", "title": "Azure OpenAI in Foundry Models vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-nemotron-3-5-super-vl.json", "title": "Azure OpenAI in Foundry Models vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-novita-ai.json", "title": "Azure OpenAI in Foundry Models vs Novita AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-openai-api.json", "title": "Azure OpenAI in Foundry Models vs OpenAI API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-openrouter.json", "title": "Azure OpenAI in Foundry Models vs OpenRouter", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference.json", "title": "Azure OpenAI in Foundry Models vs Prism Inference", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-ramp-router.json", "title": "Azure OpenAI in Foundry Models vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-reflection-beam.json", "title": "Azure OpenAI in Foundry Models vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-routerplus.json", "title": "Azure OpenAI in Foundry Models vs RouterPlus", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-sambanova.json", "title": "Azure OpenAI in Foundry Models vs SambaCloud", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.json", "title": "Azure OpenAI in Foundry Models vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.json", "title": "Azure OpenAI in Foundry Models vs Simplismart", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.json", "title": "Azure OpenAI in Foundry Models vs StepFun API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-swe-2-flash.json", "title": "Azure OpenAI in Foundry Models 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)", "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-eden-ai.json", "title": "BlockRun.AI vs Eden AI", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai.json", "title": "Cerebras Inference vs Eden AI", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai.json", "title": "Cloudflare AI Gateway vs Eden AI", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai.json", "title": "Cloudflare Workers AI vs Eden AI", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai.json", "title": "Cohere Chat API (Command models) vs Eden AI", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.json", "title": "DeepInfra vs Eden AI", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai.json", "title": "DeepSeek API vs Eden AI", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse.json", "title": "Eden AI vs Glimpse", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference.json", "title": "Eden AI vs Fireworks AI Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.json", "title": "Eden AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-groq.json", "title": "Eden AI vs GroqCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small.json", "title": "Eden AI vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.json", "title": "Eden AI vs Jais 3", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api.json", "title": "Eden AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api.json", "title": "Eden AI vs MiniMax API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api.json", "title": "Eden AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.json", "title": "Eden AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl.json", "title": "Eden AI vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai.json", "title": "Eden AI vs Novita AI", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-openai-api.json", "title": "Eden AI vs OpenAI API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference.json", "title": "Eden AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.json", "title": "Eden AI vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-sambanova.json", "title": "Eden AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.json", "title": "Eden AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart.json", "title": "Eden AI vs Simplismart", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart" }, { "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/eden-ai-vs-swe-2-flash.json", "title": "Eden AI vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-together-inference.json", "title": "Eden AI vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.json", "title": "Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-xai-api.json", "title": "Eden AI vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-eden-ai.json", "title": "Antseed vs Eden AI", "url": "https://www.anchorterminal.com/compare/antseed-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers.json", "title": "Eden AI vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-litellm.json", "title": "Eden AI vs LiteLLM", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-litellm" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-openrouter.json", "title": "Eden AI vs OpenRouter", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router.json", "title": "Eden AI vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-routerplus.json", "title": "Eden AI vs RouterPlus", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub.json", "title": "Eden AI vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway.json", "title": "Eden AI vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway" } ], "panel": null, "scores": [ { "azure-openai": 80, "by": 47, "eden-ai": 33, "edge": "azure-openai", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "azure-openai": 87, "by": 6, "eden-ai": 81, "edge": "azure-openai", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "azure-openai": 98, "by": 23, "eden-ai": 75, "edge": "azure-openai", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "azure-openai": 95, "by": 13, "eden-ai": 82, "edge": "azure-openai", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "azure-openai": 20, "by": 10, "eden-ai": 30, "edge": "eden-ai", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "azure-openai": 86, "by": 28, "eden-ai": 58, "edge": "azure-openai", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "azure-openai": 83, "by": 28, "eden-ai": 55, "edge": "azure-openai", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on payments \u0026 pricing. Both do llm inference.", "tasks": null, "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.", "eden-ai": "Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai", "json": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.min.md" }, "markdown": "Azure OpenAI in Foundry Models scores 80 (A) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on payments \u0026 pricing. Both do llm inference.\n\n- Azure OpenAI in Foundry Models: grade A, 80/100, rank #14 of 1486. Markdown https://www.anchorterminal.com/tools/azure-openai.md · JSON https://www.anchorterminal.com/api/v1/tools/azure-openai.json\n- Eden AI: grade C, 59.9/100, rank #672 of 1486. Markdown https://www.anchorterminal.com/tools/eden-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/eden-ai.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### 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 33\n- Schema \u0026 documentation, 87 against 81\n- Agent ergonomics, 98 against 75\n- Security \u0026 auth, 95 against 82\n- Maintenance \u0026 community, 86 against 58\n- Transparency \u0026 trust, 83 against 55\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### Eden AI (C)\n\nGood for: Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.\n\nAhead on:\n- Payments \u0026 pricing, 30 against 20\n\nWatch for: No public status page, incident history or SLA found\n\n\n## Why the edge\n\n### Reliability: Azure OpenAI in Foundry Models +47\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- Eden AI (33): Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support \u0026 SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33.\n\n### Maintenance \u0026 community: Azure OpenAI in Foundry Models +28\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- Eden AI (58): Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58.\n\n### Transparency \u0026 trust: Azure OpenAI in Foundry Models +28\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- Eden AI (55): Editorial half only. Closed service under terms last updated 22 October 2026, naming Eden AI SAS and French law with courts in Lyon (15). The data retention page, terms and DPA agree that content isn't stored by default, and the docs carry a per-provider table of training, retention and ZDR (27). No deprecation policy found; models are dropped in docs updates without dated notice that we found (5). The DPA's Annex 2 names operational subprocessors (AWS EMEA, ClickHouse, Sentry, Metabase, Stripe, Brevo and others) with their roles, and the EU endpoint keeps routing in the EU (18). 15+27+5+18 = 65.\n\n### Agent ergonomics: Azure OpenAI in Foundry Models +23\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- Eden AI (75): Router, read on the model lines. Chat completions, Responses and an Anthropic Messages route in the OpenAI shape, with tool calling depending on the routed model (14 of 20) and structured output likewise (10 of 15). Caching is named in the terms as part of the service, without a per-model price table we read (8 of 15). Context depends on the model; the keyless /v3/models catalogue lists capabilities, prices and regions per model so an agent can choose (12 of 15). A Batches API (10 of 10). No SDK of its own; the docs use the OpenAI Python and TypeScript SDKs, and a hosted MCP server exposes about 40 expert-model tools plus upload_file, check_job and list_models (21 of 25). 14+10+8+12+10+21 = 75.\n\n### Security \u0026 auth: Azure OpenAI in Foundry Models +13\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- Eden AI (82): Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82.\n\n### Payments \u0026 pricing: Eden AI +10\n\n- Azure OpenAI in Foundry Models (20): No x402, MPP or L402 (0). Per-1M-token prices are public in the Azure Retail Prices API without a login, while the pricing page fills its table with JavaScript and showed GPT-6 prices as pending on 6 October (20). The quota tiers include a Free tier, but an Azure subscription needs a payment method (0). A person signs up for Azure in a browser and creates a resource and a deployment before the first call (0).\n- Eden AI (30): No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30.\n\n## Score by category\n\n| Category | Weight | Azure OpenAI in Foundry Models | Eden AI | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 80 | 33 | Azure OpenAI in Foundry Models +47 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 87 | 81 | Azure OpenAI in Foundry Models +6 |\n| Agent ergonomics | 13% (16.2 this run) | 98 | 75 | Azure OpenAI in Foundry Models +23 |\n| Security \u0026 auth | 14% (17.5 this run) | 95 | 82 | Azure OpenAI in Foundry Models +13 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | Eden AI +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 86 | 58 | Azure OpenAI in Foundry Models +28 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 83 | 55 | Azure OpenAI in Foundry Models +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **80 · A** | **59.9 · C** | |\n\n## Facts side by side\n\n| Fact | Azure OpenAI in Foundry Models | Eden AI |\n| --- | --- | --- |\n| Kind | Model API | Model router |\n| Vendor | Microsoft | Eden AI SAS |\n| Hosted endpoint | `https://\u003cresource\u003e.openai.azure.com/openai/v1` | `https://mcp.edenai.run/mcp` |\n| Transports | HTTP | HTTP, Streamable 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 | none |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-10-26 | 2026-11-05 |\n| Terms last updated | no date given | 2026-10-22 |\n| Privacy policy last updated | 2026-09-01 | 2026-10-22 |\n| Customer content may train models | yes | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 454k npm/wk | none |\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**Eden AI.** Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.\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### Eden AI\n\n1. Read GET https://api.edenai.run/v3/models at run time for model ids, prices and regions instead of hard-coding them\n2. Use https://api.eu.edenai.run/v3 when data must stay in the EU, and pick another model on a 451 region_not_allowed\n3. Set max_tokens to 500 or more for reasoning models, or the answer can come back empty\n4. Pass a fallbacks list so a provider 429 or error retries inside one request\n5. Poll check_job for MCP tools marked long-running instead of calling them again\n\n## Questions\n\n### Which is better for AI agents, Azure OpenAI in Foundry Models or Eden AI?\n\nAzure OpenAI in Foundry Models scores 80 (A) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on payments \u0026 pricing.\n\n### Do Azure OpenAI in Foundry Models and Eden AI need an API key?\n\nAzure OpenAI in Foundry Models takes an API key or an OAuth sign-in. Eden AI needs an API key.\n\n### Can an agent call Azure OpenAI in Foundry Models and Eden AI without installing anything?\n\nYes. Azure OpenAI in Foundry Models has a hosted endpoint at https://\u003cresource\u003e.openai.azure.com/openai/v1 and Eden AI at https://mcp.edenai.run/mcp.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"azure-openai\", \"b\": \"eden-ai\"}`. From a terminal: `anchor compare azure-openai eden-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/azure-openai.json and https://www.anchorterminal.com/api/v1/tools/eden-ai.json\n\n## Other comparisons with Azure OpenAI in Foundry Models or Eden AI\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 Eden AI](https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai.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 Eden AI](https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai.md)\n- [Antseed vs Azure OpenAI in Foundry Models](https://www.anchorterminal.com/compare/antseed-vs-azure-openai.md)\n- [Azure OpenAI in Foundry Models vs BlockRun.AI](https://www.anchorterminal.com/compare/azure-openai-vs-blockrun-ai.md)\n- [Azure OpenAI in Foundry Models vs Cerebras Inference](https://www.anchorterminal.com/compare/azure-openai-vs-cerebras-inference.md)\n- [Azure OpenAI in Foundry Models vs Cloudflare AI Gateway](https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-ai-gateway.md)\n- [Azure OpenAI in Foundry Models vs Cloudflare Workers AI](https://www.anchorterminal.com/compare/azure-openai-vs-cloudflare-workers-ai.md)\n- [Azure OpenAI in Foundry Models vs Cohere Chat API (Command models)](https://www.anchorterminal.com/compare/azure-openai-vs-cohere-chat.md)\n- [Azure OpenAI in Foundry Models vs DeepInfra](https://www.anchorterminal.com/compare/azure-openai-vs-deepinfra.md)\n- [Azure OpenAI in Foundry Models vs DeepSeek API](https://www.anchorterminal.com/compare/azure-openai-vs-deepseek-api.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 in Foundry Models vs Meta Model API](https://www.anchorterminal.com/compare/azure-openai-vs-meta-model-api.md)\n- [Azure OpenAI in Foundry Models vs MiniMax API](https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.md)\n- [Azure OpenAI in Foundry Models vs Mistral AI API](https://www.anchorterminal.com/compare/azure-openai-vs-mistral-api.md)\n- [Azure OpenAI in Foundry Models vs Muse Spark Open](https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open.md)\n- [Azure OpenAI in Foundry Models vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/azure-openai-vs-nemotron-3-5-super-vl.md)\n- [Azure OpenAI in Foundry Models vs Novita AI](https://www.anchorterminal.com/compare/azure-openai-vs-novita-ai.md)\n- [Azure OpenAI in Foundry Models vs OpenAI API](https://www.anchorterminal.com/compare/azure-openai-vs-openai-api.md)\n- [Azure OpenAI in Foundry Models vs OpenRouter](https://www.anchorterminal.com/compare/azure-openai-vs-openrouter.md)\n- [Azure OpenAI in Foundry Models vs Prism Inference](https://www.anchorterminal.com/compare/azure-openai-vs-prism-inference.md)\n- [Azure OpenAI in Foundry Models vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/azure-openai-vs-ramp-router.md)\n- [Azure OpenAI in Foundry Models vs Beam 1.0](https://www.anchorterminal.com/compare/azure-openai-vs-reflection-beam.md)\n- [Azure OpenAI in Foundry Models vs RouterPlus](https://www.anchorterminal.com/compare/azure-openai-vs-routerplus.md)\n- [Azure OpenAI in Foundry Models vs SambaCloud](https://www.anchorterminal.com/compare/azure-openai-vs-sambanova.md)\n- [Azure OpenAI in Foundry Models vs SiliconFlow](https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.md)\n- [Azure OpenAI in Foundry Models vs Simplismart](https://www.anchorterminal.com/compare/azure-openai-vs-simplismart.md)\n- [Azure OpenAI in Foundry Models vs StepFun API](https://www.anchorterminal.com/compare/azure-openai-vs-stepfun-api.md)\n- [Azure OpenAI in Foundry Models vs SWE-2 Flash](https://www.anchorterminal.com/compare/azure-openai-vs-swe-2-flash.md)\n- [Azure OpenAI in Foundry Models vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/azure-openai-vs-tencent-tokenhub.md)\n- [Azure OpenAI in Foundry Models vs Together AI Inference](https://www.anchorterminal.com/compare/azure-openai-vs-together-inference.md)\n- [Azure OpenAI in Foundry Models vs Vercel AI Gateway](https://www.anchorterminal.com/compare/azure-openai-vs-vercel-ai-gateway.md)\n- [Azure OpenAI in Foundry Models vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/azure-openai-vs-vertex-ai.md)\n- [Azure OpenAI in Foundry Models vs xAI API (Grok)](https://www.anchorterminal.com/compare/azure-openai-vs-xai-api.md)\n- [BlockRun.AI vs Eden AI](https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai.md)\n- [Cerebras Inference vs Eden AI](https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai.md)\n- [Cloudflare AI Gateway vs Eden AI](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai.md)\n- [Cloudflare Workers AI vs Eden AI](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai.md)\n- [Cohere Chat API (Command models) vs Eden AI](https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai.md)\n- [DeepInfra vs Eden AI](https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.md)\n- [DeepSeek API vs Eden AI](https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai.md)\n- [Eden AI vs Glimpse](https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse.md)\n- [Eden AI vs Fireworks AI Inference](https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference.md)\n- [Eden AI vs Gemini Developer API](https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.md)\n- [Eden AI vs GroqCloud](https://www.anchorterminal.com/compare/eden-ai-vs-groq.md)\n- [Eden AI vs Inkling 2 Small](https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small.md)\n- [Eden AI vs Jais 3](https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.md)\n- [Eden AI vs Meta Model API](https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api.md)\n- [Eden AI vs MiniMax API](https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api.md)\n- [Eden AI vs Mistral AI API](https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api.md)\n- [Eden AI vs Muse Spark Open](https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.md)\n- [Eden AI vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl.md)\n- [Eden AI vs Novita AI](https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai.md)\n- [Eden AI vs OpenAI API](https://www.anchorterminal.com/compare/eden-ai-vs-openai-api.md)\n- [Eden AI vs Prism Inference](https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference.md)\n- [Eden AI vs Beam 1.0](https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.md)\n- [Eden AI vs SambaCloud](https://www.anchorterminal.com/compare/eden-ai-vs-sambanova.md)\n- [Eden AI vs SiliconFlow](https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.md)\n- [Eden AI vs Simplismart](https://www.anchorterminal.com/compare/eden-ai-vs-simplismart.md)\n- [Eden AI vs StepFun API](https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api.md)\n- [Eden AI vs SWE-2 Flash](https://www.anchorterminal.com/compare/eden-ai-vs-swe-2-flash.md)\n- [Eden AI vs Together AI Inference](https://www.anchorterminal.com/compare/eden-ai-vs-together-inference.md)\n- [Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.md)\n- [Eden AI vs xAI API (Grok)](https://www.anchorterminal.com/compare/eden-ai-vs-xai-api.md)\n- [Antseed vs Eden AI](https://www.anchorterminal.com/compare/antseed-vs-eden-ai.md)\n- [Eden AI vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers.md)\n- [Eden AI vs LiteLLM](https://www.anchorterminal.com/compare/eden-ai-vs-litellm.md)\n- [Eden AI vs OpenRouter](https://www.anchorterminal.com/compare/eden-ai-vs-openrouter.md)\n- [Eden AI vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router.md)\n- [Eden AI vs RouterPlus](https://www.anchorterminal.com/compare/eden-ai-vs-routerplus.md)\n- [Eden AI vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub.md)\n- [Eden AI vs Vercel AI Gateway](https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway.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", 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