{ "data": { "a": { "slug": "hugging-face-inference-providers", "name": "Hugging Face Inference Providers", "vendor": "Hugging Face, Inc.", "vendorUrl": "https://huggingface.co", "kind": "router", "category": "inference", "summary": "Hugging Face's serverless router that sends model calls to partner inference providers such as Groq, Cerebras, Together and Fireworks through one Hugging Face token, with an OpenAI-compatible API at router.huggingface.co/v1 and Python and JavaScript clients.", "url": "https://www.anchorterminal.com/tools/hugging-face-inference-providers", "markdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json", "dns": "hugging-face-inference-providers.grade.anchorterminal.com", "repo": "https://github.com/huggingface/huggingface_hub", "license": "Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "transports": [ "http" ], "remoteUrl": "https://router.huggingface.co/v1", "packages": [ { "registry": "pypi", "name": "huggingface_hub" }, { "registry": "npm", "name": "@huggingface/inference" } ], "auth": "api-key", "authNotes": "Hugging Face user access token sent as `Authorization: Bearer $HF_TOKEN`. The docs recommend a fine-grained token with only the `Make calls to Inference Providers` permission. Tokens are made in account settings in a browser, can be rotated and revoked, and a leaked token can be revoked by anyone through `POST /api/credentials/revoke`. A provider's own key can be stored in settings instead, and Hugging Face swaps it in when routing.", "pricing": "usage", "pricingNotes": "Pay as you go at each provider's own per-token rates with no Hugging Face markup, drawn from prepaid credits on the Hugging Face account. Free users get no included credits since 7 October 2026 and must buy credits. PRO users and Team or Enterprise seats get $2.00 a month of compute credits. Requests with a custom provider key are billed by the provider (https://huggingface.co/docs/inference-providers/pricing, checked 2026-11-06).", "priceSummary": "$2 / mo", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Inference Providers docs, llms.txt or the router's model list. Calls draw on prepaid Hugging Face credits bought in the account's billing settings (checked 2026-11-06).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 483470, "pypiWeekly": 71518708, "asOf": "2026-11-06" }, "docsUrl": "https://huggingface.co/docs/inference-providers/index", "llmsTxt": "https://huggingface.co/docs/inference-providers/llms.txt", "capabilities": [ "inference.router", "inference.open-weights", "inference.llm" ], "tags": [ "hosted", "router", "usage", "prepaid", "openai-compatible", "llms-txt", "python", "javascript", "status-page", "soc2" ], "lastRelease": "2026-10-29", "graded": true, "anchor": { "graded": true, "score": 54, "grade": "C", "agentReady": false, "rank": 926, "ranked": true, "rankOf": 1510, "categoryRank": 28, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "bestFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "strengths": [ "router.huggingface.co/v1/models lists every routed model with each provider's price, context, tool support, latency and throughput, without a token", "Provider choice by suffix (`:fastest`, `:cheapest`, `:preferred` or a provider name) on an OpenAI-compatible endpoint", "Fine-grained tokens can be limited to the `Make calls to Inference Providers` permission and revoked at once", "No markup on provider rates, and request bodies and responses are not stored when routed", "Official clients in Python (`huggingface_hub` 2.4.0, 29 October 2026) and JavaScript (`@huggingface/inference` 4.13.32)" ], "weaknesses": [ "No rate limits, 429 behaviour or error responses documented for the router", "status.huggingface.co has no component for Inference Providers or the router", "Free users have had no included credits since 7 October 2026, and buying credits needs a person in the account settings", "No OpenAPI document for the router, no batch API, and the Responses API is beta", "Data handling past the router depends on each provider's own policy" ], "agentNotes": [ "Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider", "Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider", "Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation", "Back off on 429 without assuming a Retry-After header, since none is documented for the router" ], "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": 54 } ], "editorialScores": { "ergonomics": 58, "maintenance": 80, "payments": 20, "reliability": 25, "schema": 62, "security": 79, "transparency": 58 }, "provenanceScore": 84, "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 huggingface_hub # or: pip install openai, with base_url https://router.huggingface.co/v1", "http": "curl https://router.huggingface.co/v1/chat/completions \\\n -H \"Authorization: Bearer $HF_TOKEN\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"openai/gpt-oss-120b:fastest\",\"messages\":[{\"role\":\"user\",\"content\":\"How many Gs in huggingface?\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/hugging-face-inference-providers" }, "sameCompany": [ "hugging-face-inference-endpoints", "smolagents", "carbon-x", "smollm3-3b" ], "area": "models", "unitPrices": [ { "item": "openai/gpt-oss-120b input, cheapest provider listed (DeepInfra)", "unit": "1m-tokens", "usd": 0.037, "note": "Output $0.17. Groq lists $0.15/$0.75 and Cerebras $0.35/$0.75, per the router's model list of 6 November 2026" }, { "item": "PRO monthly compute credits", "unit": "month", "usd": 2, "note": "Also $2.00 per seat for Team and Enterprise organisations" } ], "provenance": { "legalEntity": "Hugging Face, Inc.", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": true, "terms": "https://huggingface.co/terms-of-service", "privacy": "https://huggingface.co/privacy", "statusPage": "https://status.huggingface.co", "changelog": "https://huggingface.co/changelog", "securityTxt": "valid", "checked": "2026-11-06", "notes": [ "The Terms of Service (effective 15 September 2022) name Hugging Face, Inc. and define Inference Providers as a hosted service for running inference through an API on Hub models.", "huggingface.co/robots.txt and router.huggingface.co/robots.txt allow all. status.huggingface.co serves an empty robots.txt.", "status.huggingface.co is a Better Stack page with components for the Hub, git hosting, Inference Endpoints, Spaces, the CDNs and Jobs, and none for Inference Providers or the router.", "huggingface.co/changelog covers the whole Hub and had no Inference Providers entry on its first page on 6 November 2026. Dated changes to the docs are in the hub-docs repository's history.", "security.txt, privacy policy and subprocessor list as recorded on the Inference Endpoints listing (checked 8 October and 2 November 2026); not re-read for this listing." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hugging-face-inference-providers.json", "live": { "slug": "hugging-face-inference-providers", "probe": { "target": "https://router.huggingface.co/v1", "method": "get", "lastAt": "2026-11-07T19:22:12.474650545Z", "lastOk": true, "lastStatus": 404, "lastMs": 145, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 109, "p95ms24h": 196, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.huggingface.co", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:55.542550557Z" }, "versions": [ { "registry": "github", "name": "huggingface/huggingface_hub", "version": "v2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:27.894225298Z" }, { "registry": "npm", "name": "@huggingface/inference", "version": "4.13.32", "seenAt": "2026-11-06T23:33:27.102558197Z" }, { "registry": "pypi", "name": "huggingface_hub", "version": "2.4.0", "released": "2026-10-29", "seenAt": "2026-11-06T23:33:26.910773744Z" } ], "githubStars": 4034, "npmWeekly": 483470, "pypiWeekly": 71518708, "securityTxt": { "url": "https://huggingface.co/.well-known/security.txt", "state": "valid", "expires": "2030-07-01T08:42:00.000Z", "checkedAt": "2026-11-07T16:00:00.493446773Z" }, "llmsTxt": { "url": "https://huggingface.co/docs/inference-providers/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:44.910268229Z" }, "pages": [ { "url": "https://huggingface.co/docs/inference-providers/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-07T14:11:37.221950067Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "2cbe821ca9d2" } ], "updatedAt": "2026-11-07T19:22:12.474650545Z" } }, "answer": "OpenAI API scores 83.9 (A) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in every scored category.", "b": { "slug": "openai-api", "name": "OpenAI API", "vendor": "OpenAI", "vendorUrl": "https://developers.openai.com", "kind": "model", "category": "inference", "summary": "OpenAI's API for accessing its models through Responses, Chat Completions and Batch endpoints.", "url": "https://www.anchorterminal.com/tools/openai-api", "markdownUrl": "https://www.anchorterminal.com/tools/openai-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-api.json", "dns": "openai-api.grade.anchorterminal.com", "repo": "https://github.com/openai/openai-python", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.openai.com/v1", "packages": [ { "registry": "pypi", "name": "openai", "main": true }, { "registry": "npm", "name": "openai", "main": true } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a project key. Some models and tools need business or ID verification first.", "pricing": "usage", "pricingNotes": "Prepaid credit, with the Build tier reached at $5 in credit purchases, Launch at $100 and Grow at $500. Long context over 272K tokens costs 2x input and 1.5x output. Cache reads 0.1x input (0.05x on GPT-6.1 Sol and GPT-6.1 Terra), cache writes 1.25x from GPT-5.6 on, or 2.5x for the 12-hour lifetime in the Responses API since 20 October. Batch and Flex half price, Fast mode 2x, Ultrafast 6x (GPT-6 Astra, and GPT-6.1 Sol since 8 October). Data residency endpoints add 10% for models released on or after 5 March 2026, and FedRAMP endpoints add 10% from 1 November 2026. Web search $10 per 1,000 calls plus search content tokens, file search $2.50 per 1,000, hosted containers from $0.03 per 20-minute session (https://developers.openai.com/api/docs/pricing).", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. Access needs an account with prepaid credit.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 31300, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://developers.openai.com/api/docs", "rateLimitsUrl": "https://developers.openai.com/api/docs/guides/rate-limits", "llmsTxt": "https://developers.openai.com/llms.txt", "openapi": "https://github.com/openai/openai-openapi", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "card-required", "openapi", "llms-txt" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 83.9, "grade": "A", "agentReady": true, "rank": 6, "ranked": true, "rankOf": 1510, "categoryRank": 2, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 87, "payments": 30, "reliability": 75, "schema": 100, "security": 100, "transparency": 90 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.", "bestFor": "Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.", "strengths": [ "Official OpenAPI document and an llms.txt index", "Project keys can be restricted per endpoint to None, Read or Write, and mutual TLS workload identity is GA", "A dated deprecations page, and `GET /v1/models` returns each model's `shutdown_date`", "GPT-6 Luna at $0.10/$0.50 per million tokens with the same 1.05M context as Astra", "Scale Tier comes with a 99.9% uptime SLA" ], "weaknesses": [ "Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026", "Heavy migration calendar. Assistants API gone on 2026-08-26, Agent Builder, Evals and `v1/prompts` on 2026-11-30, GPT-5 and o3 on 2026-12-11", "GPT-6 models aren't available on the Free tier, so a card and prepaid credit come first in practice", "GPT-6 Astra has no custom temperature, no logprobs and no tool calling outside the Responses API", "GPT-4o and GPT-4.1 snapshots got 92 days' notice on 14 October, against the 6 months the deprecations page promises GA models" ], "agentNotes": [ "Build on the Responses API. Astra calls tools only there", "Use `gpt-6-luna` for routing and extraction, `gpt-6.1-terra` for screens, images and computer use at half Sol's price, and `gpt-6.1-sol` for most work. The models page still points unsure callers to `gpt-6-astra`", "Move off the GPT-4o and GPT-4.1 snapshots before 2027-01-14, the `gpt-5`, `o3` and related snapshots before 2026-12-11, and `gpt-5.1`, `gpt-5.3-codex` and `gpt-5.4-nano` before 2027-04-01. Check `shutdown_date` on `GET /v1/models`", "Treat 429 `slow_down` as a ramp limit and 503 `server_is_overloaded` as a retry, and follow `Retry-After` when it's sent", "Prompts over 272K tokens cost double on input. Trim before you pay for it" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 8, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 83.9 } ], "editorialScores": { "ergonomics": 98, "maintenance": 87, "payments": 30, "reliability": 75, "schema": 100, "security": 100, "transparency": 85 }, "provenanceScore": 95, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai # or: npm i openai", "http": "curl https://api.openai.com/v1/responses \\\n -H \"Authorization: Bearer $OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"gpt-6.1-sol\",\"input\":\"hello\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/openai-api" }, "sameCompany": [ "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-agents-api", "openai-codex", "gpt-oss-120b", "gpt-oss-20b" ], "area": "models", "unitPrices": [ { "item": "Web search tool", "unit": "1k-requests", "usd": 10 }, { "item": "File search tool", "unit": "1k-requests", "usd": 2.5 } ], "provenance": { "legalEntity": "OpenAI OpCo, LLC", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": true, "terms": "https://openai.com/policies/services-agreement/", "privacy": "https://openai.com/policies/privacy-policy/", "statusPage": "https://status.openai.com", "changelog": "https://developers.openai.com/api/docs/changelog", "securityTxt": "valid", "checked": "2026-09-26", "score": 95 }, "requirements": { "setupChecked": true, "complianceChecked": true, "card": "paid-only", "freeTier": true, "minutes": 5, "credential": "api-key", "hosting": [ "US", "EU", "UK", "CA", "APAC" ], "euResidency": "enterprise", "trainsOnData": "no", "zeroRetention": "enterprise", "certifications": [ "SOC 2 Type II", "ISO 27001", "ISO 27701", "ISO 42001", "CSA STAR", "HIPAA (BAA available)", "GDPR" ], "dpa": true, "selfHost": "no" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-api.json", "live": { "slug": "openai-api", "probe": { "target": "https://api.openai.com/v1", "method": "get", "lastAt": "2026-11-07T19:22:23.634808611Z", "lastOk": true, "lastStatus": 404, "lastMs": 157, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 162, "p95ms24h": 227, "samples24h": 238, "samples30d": 7920, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "vendorStatus": { "page": "https://status.openai.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-07T19:22:50.781952068Z" }, "versions": [ { "registry": "github", "name": "openai/openai-python", "version": "v3.47.0", "released": 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"2026-10-16T22:20:47.232011113Z", "fingerprint": "3261efc5e241" }, { "url": "https://developers.openai.com/api/docs/deprecations", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-07T14:04:23.062971759Z", "changedAt": "2026-11-06T13:26:58.963754521Z", "fingerprint": "0e64ced508a8" }, { "url": "https://developers.openai.com/api/docs/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-07T14:04:25.090876509Z", "changedAt": "2026-11-06T13:27:00.948019243Z", "fingerprint": "a83cd96e6a1c" } ], "models": { "gpt-6-astra": { "openrouterId": "openai/gpt-6-astra", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gpt-6-luna": { "openrouterId": "openai/gpt-6-luna", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gpt-6-sol": { "openrouterId": "openai/gpt-6-sol", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-10-04T21:56:15.872733218Z" }, "gpt-6.1-sol": { "openrouterId": "openai/gpt-6.1-sol", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gpt-6.1-terra": { "openrouterId": "openai/gpt-6.1-terra", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-06T23:49:25.225778715Z" } }, "github": { "repo": "openai/openai-python", "checkedAt": "2026-11-02T09:13:10.459648475Z", "stars": 32015, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-02T03:47:02Z", "openIssues": 40, "openPRs": 54, "issuesOpened90d": 42, "issuesClosed90d": 220, "prsMerged90d": 437, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-11-02T03:19:31Z" }, "replies": { "sampled": 25, "unanswered": 9, "medianDays": 3.6 } }, "updatedAt": "2026-11-07T19:22:50.781952068Z" } }, "edges": [ { "by": 50, "category": "reliability", "leads": "openai-api", "name": "Reliability", "reasons": { "a": "Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.", "b": "Status page at status.openai.com with component history (20). Two API-wide majors in the 90 days to 1 November, elevated errors across ChatGPT, Codex and the API (the Agents API included) on 29 September, with Chat Completions, Responses, Batch, Realtime, Embeddings, Images, Files and the other API components degraded from 17:52 to 23:14 UTC, and elevated error rates across API models on 17 September, with eleven components degraded from 20:20 to 21:20 UTC. Two majors give 5 of 30. The 25 July incident is now outside the window. The 29 September write-up, posted on 6 October, traces it to a feature rollout that exhausted shared network connection capacity. Every other incident since 3 August that lists an API component is partial or under an hour, so minor. Degraded performance across ChatGPT, Codex and the APIs on 21 October listed every API component but ran 22 minutes (02:26 to 02:48 UTC). Single-model incidents were GPT-5.6 Sol errors on 11 September (09:08 to 11:21 UTC), `gpt-5.6-luna` errors on 21 October (70 minutes) and GPT-6.1 Sol degraded performance on Batch, Chat Completions and Responses on 27 October (78 minutes). Partial incidents were latency for some Responses requests on the US endpoint on 31 August and 1 September, image generation where some requests failed on 8 September, latency for some Responses and Chat Completions requests on 30 September, large-PDF errors in Responses and an Agents API computer use incident on 6 October, Responses errors for users in India, Japan and Korea on 22 October and errors on some GPT Image 2 edit requests on 30 October (14:35 to 18:36 UTC). Image model incidents on 16 September and 14 October and file download errors on 19 October each ran about 40 minutes. FedRAMP errors for Astra and Sol on 21 October and authentication errors near Illinois on 20 October list no API component. Rate limits published per tier and model family with rate-limit headers (15). 429 guidance, re-read on 1 November, covers `Retry-After` as a minimum wait, backoff with jitter when it's missing, a gradual ramp, and splits `slow_down` (429) from `server_is_overloaded` (503) (15). Scale Tier carries a 99.9% uptime SLA, through sales, per the 1 October check, not re-read on 1 November (10). Responses and Chat Completions are GA (10). 20 + 5 + 15 + 15 + 10 + 10 = 75." }, "scores": { "a": 25, "b": 75 } }, { "by": 40, "category": "ergonomics", "leads": "openai-api", "name": "Agent ergonomics", "reasons": { "a": "Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and forced tool choice, 18 of 20 because GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra call tools only through the Responses API (18). Strict structured outputs (15). Automatic prompt caching with cached input at 0.1x on Astra and Luna ($0.01 per million on Luna) and 0.05x on GPT-6.1 Sol ($0.10) and GPT-6.1 Terra ($0.05), and a 12-hour cache lifetime in the Responses API since 20 October (15). 1.05M context on Astra, GPT-6.1 Sol, GPT-6.1 Terra and Luna (15). Batch at half price (10). Official SDKs in Python, JavaScript and more (10). Documented error codes with retry guidance (15)." }, "scores": { "a": 58, "b": 98 } }, { "by": 38, "category": "schema", "leads": "openai-api", "name": "Schema \u0026 documentation", "reasons": { "a": "No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).", "b": "Official OpenAPI document (openapi.yaml and openapi.json) in the openai/openai-openapi repository, whose openapi.yaml (version 2.3.0, updated 31 October) lists `gpt-6.1-terra` (25). llms.txt index with per-product files, and every docs page has a Markdown twin at `.md` (10). The reference documents each parameter, and model pages say which model fits which job (20). Structured outputs with `strict: true` on schemas and function tools (15). Error codes guide with types and recovery advice, now with a 400 `service_tier` error for tiers a project doesn't allow (15). Dated model snapshots and a dated changelog, last entry 30 October (15)." }, "scores": { "a": 62, "b": 100 } }, { "by": 21, "category": "security", "leads": "openai-api", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).", "b": "Model reading. Project keys with All, Restricted (per-endpoint None, Read or Write) and Read Only permissions, service accounts, and mutual TLS with X.509 workload identity GA since 26 August, per the 1 October check. The changelog adds project key expiry dates on 10 September, key-creation controls on 15 September and, on 21 October, organisation and project settings that limit which service tiers a project may use (30). API data not used for training unless the customer opts in, since 1 March 2023 (20). Abuse-monitoring logs kept up to 30 days, and Zero Data Retention or Modified Abuse Monitoring by approval. The data-controls guide re-read on 1 November lists Chat Completions, Responses, Decisions and ten other endpoints as ZDR-eligible; `/v1/agents`, Conversations, Files and vector stores aren't (15). Usage and Costs filter by API key since 4 August, with Admin APIs to match, and audit logs for enterprise (15). SOC 2 Type 2, ISO 27001, 27017, 27018, 27701 and 42001 per the 1 October check, not re-read on 1 November, a Bugcrowd bug bounty and a coordinated disclosure policy, both linked from a signed security.txt. That security.txt had no Expires field on 27 October, which RFC 9116 requires, so the disclosure policy carries this line. It wasn't re-read on 1 November (20)." }, "scores": { "a": 79, "b": 100 } }, { "by": 19, "category": "transparency", "leads": "openai-api", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).", "b": "Closed service with clear terms, SDKs Apache-2.0 (15). The data-controls guide, re-read on 1 November, gives retention periods per endpoint, the training default and the ZDR route. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum for transfers, and nothing in it conflicts with the guide, per the 4 October check, not re-read on 1 November (30). Deprecations page with announcement and shutdown dates, and `GET /v1/models` returns each model's `shutdown_date` (20). The subprocessor list, updated 9 July 2026, names its third-party subprocessors and OpenAI affiliates with the processing countries for each and a form to get notice of new ones, and data residency regions are listed, per the 4 October check, not re-read on 1 November (20)." }, "scores": { "a": 71, "b": 90 } }, { "by": 10, "category": "payments", "leads": "openai-api", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Per-token prices for every model and provider published without a login in the router's /v1/models and on model pages (20). No free tier since 7 October 2026, when free users lost their included monthly credits; PRO's $2.00 of credits needs a subscription (0). Sign-up, token creation and credit purchase are browser steps (0).", "b": "No machine payment protocol, and the pricing page names no machine or crypto payment on 1 November (0). Per-token prices published without a login (20). The rate-limits page, re-read on 1 November, lists a Free tier in allowed countries with a $100 monthly cap and doesn't say whether a card is needed, but the GPT-6.1 Sol, GPT-6.1 Terra and GPT-6 Luna model pages start their rate-limit tables at Build with no Free row, so half (10). Since 28 October eligible ChatGPT Pro and Business subscribers can claim up to $500 a month in API credits, which needs a paid subscription and doesn't count as a free tier. Signing up and making a key need a person in a browser (0)." }, "scores": { "a": 20, "b": 30 } } ], "facts": [ { "a": "Model router", "b": "Model API", "name": "Kind" }, { "a": "Hugging Face, Inc.", "b": "OpenAI", "name": "Vendor" }, { "a": "https://router.huggingface.co/v1", "b": "https://api.openai.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API 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 Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-29", "b": "2026-10-30", "name": "Last release" }, { "a": "2022-09-15", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "2023-03-28", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "yes", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "483k npm/wk, 71.5M PyPI/wk", "b": "31k stars", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (8)", "name": "Agent reviews" } ], "faq": [ { "answer": "OpenAI API scores 83.9 (A) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in every scored category.", "question": "Which is better for AI agents, Hugging Face Inference Providers or OpenAI API?" }, { "answer": "Both need an API key.", "question": "Do Hugging Face Inference Providers and OpenAI API need an API key?" }, { "answer": "Yes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and OpenAI API at https://api.openai.com/v1.", "question": "Can an agent call Hugging Face Inference Providers and OpenAI API without installing anything?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.", "slug": "hugging-face-inference-providers", "watchFor": "No rate limits, 429 behaviour or error responses documented for the router" }, { "aheadOn": [ "Reliability, 75 against 25", "Schema \u0026 documentation, 100 against 62", "Agent ergonomics, 98 against 58", "Security \u0026 auth, 100 against 79", "Payments \u0026 pricing, 30 against 20", "Maintenance \u0026 community, 87 against 80", "Transparency \u0026 trust, 90 against 71" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.", "slug": "openai-api", "watchFor": "Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026" } ], "history": { "hugging-face-inference-providers": null, "openai-api": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 1, "from": 82.9, "fromGrade": "A", "to": 83.9, "toGrade": "A" }, { "causes": [ "product" ], "date": "2026-10-17", "delta": -0.4, "from": 83.3, "fromGrade": "A", "to": 82.9, "toGrade": "A" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.2, "from": 83.5, "fromGrade": "A", "to": 83.3, "toGrade": "A" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hugging-face-inference-providers.json", "title": "Amazon Bedrock vs Hugging Face Inference 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"title": "OpenAI API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.json", "title": "OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-xai-api.json", "title": "OpenAI API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-hugging-face-inference-providers.json", "title": "Antseed vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/antseed-vs-hugging-face-inference-providers" }, { "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/hugging-face-inference-providers-vs-litellm.json", "title": "Hugging Face Inference Providers vs LiteLLM", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-litellm" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openrouter.json", "title": "Hugging Face Inference Providers vs OpenRouter", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-ramp-router.json", "title": "Hugging Face Inference Providers vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-routerplus.json", "title": "Hugging Face Inference Providers vs RouterPlus", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-tencent-tokenhub.json", "title": "Hugging Face Inference Providers vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vercel-ai-gateway.json", "title": "Hugging Face Inference Providers vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-nemotron-3-5-super-vl.json", "title": "Hugging Face Inference Providers vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference.json", "title": "Hugging Face Inference Providers vs Prism Inference", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-prism-inference" } ], "panel": null, "scores": [ { "by": 50, "edge": "openai-api", "hugging-face-inference-providers": 25, "key": "reliability", "name": "Reliability", "openai-api": 75, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 38, "edge": "openai-api", "hugging-face-inference-providers": 62, "key": "schema", "name": "Schema \u0026 documentation", "openai-api": 100, "weight": 13 }, { "by": 40, "edge": "openai-api", "hugging-face-inference-providers": 58, "key": "ergonomics", "name": "Agent ergonomics", "openai-api": 98, "weight": 13 }, { "by": 21, "edge": "openai-api", "hugging-face-inference-providers": 79, "key": "security", "name": "Security \u0026 auth", "openai-api": 100, "weight": 14 }, { "by": 10, "edge": "openai-api", "hugging-face-inference-providers": 20, "key": "payments", "name": "Payments \u0026 pricing", "openai-api": 30, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 7, "edge": "openai-api", "hugging-face-inference-providers": 80, "key": "maintenance", "name": "Maintenance \u0026 community", "openai-api": 87, "weight": 7 }, { "by": 19, "edge": "openai-api", "hugging-face-inference-providers": 71, "key": "transparency", "name": "Transparency \u0026 trust", "openai-api": 90, "weight": 7 } ], "summary": "OpenAI API scores 83.9 (A) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in every scored category. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "hugging-face-inference-providers": "One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.", "openai-api": "Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api", "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.min.md" }, "markdown": "OpenAI API scores 83.9 (A) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in every scored category. Both do llm inference.\n\n- Hugging Face Inference Providers: grade C, 54/100, rank #926 of 1510. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-providers.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-providers.json\n- OpenAI API: grade A, 83.9/100, rank #6 of 1510. Markdown https://www.anchorterminal.com/tools/openai-api.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-api.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Hugging Face Inference Providers (C)\n\nGood for: Agents and developers who want open-weight models from several hosts on one token and one bill, with a public price and capability list per provider.\n\nWatch for: No rate limits, 429 behaviour or error responses documented for the router\n\n### OpenAI API (A)\n\nGood for: Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.\n\nAhead on:\n- Reliability, 75 against 25\n- Schema \u0026 documentation, 100 against 62\n- Agent ergonomics, 98 against 58\n- Security \u0026 auth, 100 against 79\n- Payments \u0026 pricing, 30 against 20\n- Maintenance \u0026 community, 87 against 80\n- Transparency \u0026 trust, 90 against 71\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026\n\n\n## Why the edge\n\n### Reliability: OpenAI API +50\n\n- Hugging Face Inference Providers (25): Hosted lines. status.huggingface.co is a Better Stack page with 90-day component history for the Hub, Inference Endpoints, Spaces, the CDNs and Jobs, but no component for Inference Providers or the router (10 of 20). With no component, there is no readable incident history for the surface agents call, and the feed's 23 items from 10 June to 7 October 2026 don't name it (5). No rate limits for the router were found; the Hub rate-limits page gives numbers for Hub APIs, resolvers and pages only (0). No 429 or overload guidance for the router was found (0). No SLA found (0). Chat completions on the router is generally available; the Responses API is labelled beta (10). Routing between providers with `:fastest` or `:cheapest` softens a single provider's outage, which this checklist doesn't score.\n- OpenAI API (75): Status page at status.openai.com with component history (20). Two API-wide majors in the 90 days to 1 November, elevated errors across ChatGPT, Codex and the API (the Agents API included) on 29 September, with Chat Completions, Responses, Batch, Realtime, Embeddings, Images, Files and the other API components degraded from 17:52 to 23:14 UTC, and elevated error rates across API models on 17 September, with eleven components degraded from 20:20 to 21:20 UTC. Two majors give 5 of 30. The 25 July incident is now outside the window. The 29 September write-up, posted on 6 October, traces it to a feature rollout that exhausted shared network connection capacity. Every other incident since 3 August that lists an API component is partial or under an hour, so minor. Degraded performance across ChatGPT, Codex and the APIs on 21 October listed every API component but ran 22 minutes (02:26 to 02:48 UTC). Single-model incidents were GPT-5.6 Sol errors on 11 September (09:08 to 11:21 UTC), `gpt-5.6-luna` errors on 21 October (70 minutes) and GPT-6.1 Sol degraded performance on Batch, Chat Completions and Responses on 27 October (78 minutes). Partial incidents were latency for some Responses requests on the US endpoint on 31 August and 1 September, image generation where some requests failed on 8 September, latency for some Responses and Chat Completions requests on 30 September, large-PDF errors in Responses and an Agents API computer use incident on 6 October, Responses errors for users in India, Japan and Korea on 22 October and errors on some GPT Image 2 edit requests on 30 October (14:35 to 18:36 UTC). Image model incidents on 16 September and 14 October and file download errors on 19 October each ran about 40 minutes. FedRAMP errors for Astra and Sol on 21 October and authentication errors near Illinois on 20 October list no API component. Rate limits published per tier and model family with rate-limit headers (15). 429 guidance, re-read on 1 November, covers `Retry-After` as a minimum wait, backoff with jitter when it's missing, a gradual ramp, and splits `slow_down` (429) from `server_is_overloaded` (503) (15). Scale Tier carries a 99.9% uptime SLA, through sales, per the 1 October check, not re-read on 1 November (10). Responses and Chat Completions are GA (10). 20 + 5 + 15 + 15 + 10 + 10 = 75.\n\n### Agent ergonomics: OpenAI API +40\n\n- Hugging Face Inference Providers (58): Router reading of the model checklist (tool use, structured output, caching, context, batch, SDKs, errors), as for openrouter. Tool calling through the OpenAI-compatible API with `tool_choice`, support flagged per provider in /v1/models (15 of 20). Structured output documented, support flagged per provider and false on some (10 of 15). No prompt caching guidance found, though the Enterprise usage API counts cached input tokens (3 of 15). Routes to models with up to 1,048,576 tokens of context per the model list, a judgement call since a router has no default model (15). No batch API (0 of 10). Official clients in Python and JavaScript, plus any OpenAI SDK (10). Errors not documented (5 of 15).\n- OpenAI API (98): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and forced tool choice, 18 of 20 because GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra call tools only through the Responses API (18). Strict structured outputs (15). Automatic prompt caching with cached input at 0.1x on Astra and Luna ($0.01 per million on Luna) and 0.05x on GPT-6.1 Sol ($0.10) and GPT-6.1 Terra ($0.05), and a 12-hour cache lifetime in the Responses API since 20 October (15). 1.05M context on Astra, GPT-6.1 Sol, GPT-6.1 Terra and Luna (15). Batch at half price (10). Official SDKs in Python, JavaScript and more (10). Documented error codes with retry guidance (15).\n\n### Schema \u0026 documentation: OpenAI API +38\n\n- Hugging Face Inference Providers (62): No OpenAPI document for the router (openapi.json paths answer 404). The task reference pages are generated from typed JSON Schema specs in huggingface.js, and the router's /v1/models gives a machine-readable catalogue, so 12 of 25. llms.txt for the Inference Providers docs and a Markdown twin of every page via `.md` (10). Guides say what each task and policy is for, with a quick setup page for coding agents (14 of 20). Parameter tables with types for chat completion, though `response_format` and `tool_choice` show as unknown types (9 of 15). Examples in Python, JavaScript and curl on every task page, but no documented error responses for the router (9 of 15). No changelog of its own; the Hub changelog doesn't cover it and dated changes are only in the hub-docs history (8 of 15).\n- OpenAI API (100): Official OpenAPI document (openapi.yaml and openapi.json) in the openai/openai-openapi repository, whose openapi.yaml (version 2.3.0, updated 31 October) lists `gpt-6.1-terra` (25). llms.txt index with per-product files, and every docs page has a Markdown twin at `.md` (10). The reference documents each parameter, and model pages say which model fits which job (20). Structured outputs with `strict: true` on schemas and function tools (15). Error codes guide with types and recovery advice, now with a 400 `service_tier` error for tiers a project doesn't allow (15). Dated model snapshots and a dated changelog, last entry 30 October (15).\n\n### Security \u0026 auth: OpenAI API +21\n\n- Hugging Face Inference Providers (79): Model reading. Fine-grained user access tokens that can be limited to the `Make calls to Inference Providers` permission, rotated and revoked, plus a public revocation endpoint for leaked tokens, and organisation approval of fine-grained tokens on paid plans (30). Hugging Face says it doesn't store user data for training, but data handling past the router follows each provider's policy, with no per-request filter to exclude providers that train or retain (12 of 20). Request bodies and responses aren't stored when routed and debug logs are kept up to 30 days without user data or tokens, with provider retention outside that statement (10 of 15). Usage by model and provider in account settings, an Enterprise usage API, and Team and Enterprise admins can disable providers and set a spending limit (13 of 15). SOC 2 Type 2 for the Hub, of which the docs say Inference Providers is a feature, and security.txt valid to 2030 per the Inference Endpoints check; no bug bounty found (14 of 20).\n- OpenAI API (100): Model reading. Project keys with All, Restricted (per-endpoint None, Read or Write) and Read Only permissions, service accounts, and mutual TLS with X.509 workload identity GA since 26 August, per the 1 October check. The changelog adds project key expiry dates on 10 September, key-creation controls on 15 September and, on 21 October, organisation and project settings that limit which service tiers a project may use (30). API data not used for training unless the customer opts in, since 1 March 2023 (20). Abuse-monitoring logs kept up to 30 days, and Zero Data Retention or Modified Abuse Monitoring by approval. The data-controls guide re-read on 1 November lists Chat Completions, Responses, Decisions and ten other endpoints as ZDR-eligible; `/v1/agents`, Conversations, Files and vector stores aren't (15). Usage and Costs filter by API key since 4 August, with Admin APIs to match, and audit logs for enterprise (15). SOC 2 Type 2, ISO 27001, 27017, 27018, 27701 and 42001 per the 1 October check, not re-read on 1 November, a Bugcrowd bug bounty and a coordinated disclosure policy, both linked from a signed security.txt. That security.txt had no Expires field on 27 October, which RFC 9116 requires, so the disclosure policy carries this line. It wasn't re-read on 1 November (20).\n\n### Transparency \u0026 trust: OpenAI API +19\n\n- Hugging Face Inference Providers (71): Closed service under Terms of Service that name Inference Providers, with an Apache-2.0 Python client and an MIT JavaScript client (20). The security page and the docs agree on no training, no stored bodies and 30-day logs, but the privacy policy dates from 28 March 2023 and each provider's own data policy governs the rest (18 of 30). No deprecation policy found. Provider support changes and the 7 October removal of free credits are dated only in the docs repository history (5 of 20). The privacy policy lists 11 subprocessors with countries per the Inference Endpoints check, and the docs name every provider a request can reach, though not where each runs (15 of 20).\n- OpenAI API (90): Closed service with clear terms, SDKs Apache-2.0 (15). The data-controls guide, re-read on 1 November, gives retention periods per endpoint, the training default and the ZDR route. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum for transfers, and nothing in it conflicts with the guide, per the 4 October check, not re-read on 1 November (30). Deprecations page with announcement and shutdown dates, and `GET /v1/models` returns each model's `shutdown_date` (20). The subprocessor list, updated 9 July 2026, names its third-party subprocessors and OpenAI affiliates with the processing countries for each and a form to get notice of new ones, and data residency regions are listed, per the 4 October check, not re-read on 1 November (20).\n\n### Payments \u0026 pricing: OpenAI API +10\n\n- Hugging Face Inference Providers (20): No x402, MPP or L402 (0). Per-token prices for every model and provider published without a login in the router's /v1/models and on model pages (20). No free tier since 7 October 2026, when free users lost their included monthly credits; PRO's $2.00 of credits needs a subscription (0). Sign-up, token creation and credit purchase are browser steps (0).\n- OpenAI API (30): No machine payment protocol, and the pricing page names no machine or crypto payment on 1 November (0). Per-token prices published without a login (20). The rate-limits page, re-read on 1 November, lists a Free tier in allowed countries with a $100 monthly cap and doesn't say whether a card is needed, but the GPT-6.1 Sol, GPT-6.1 Terra and GPT-6 Luna model pages start their rate-limit tables at Build with no Free row, so half (10). Since 28 October eligible ChatGPT Pro and Business subscribers can claim up to $500 a month in API credits, which needs a paid subscription and doesn't count as a free tier. Signing up and making a key need a person in a browser (0).\n\n## Score by category\n\n| Category | Weight | Hugging Face Inference Providers | OpenAI API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 25 | 75 | OpenAI API +50 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 62 | 100 | OpenAI API +38 |\n| Agent ergonomics | 13% (16.2 this run) | 58 | 98 | OpenAI API +40 |\n| Security \u0026 auth | 14% (17.5 this run) | 79 | 100 | OpenAI API +21 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | OpenAI API +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 80 | 87 | OpenAI API +7 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 90 | OpenAI API +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **54 · C** | **83.9 · A** | |\n\n## Facts side by side\n\n| Fact | Hugging Face Inference Providers | OpenAI API |\n| --- | --- | --- |\n| Kind | Model router | Model API |\n| Vendor | Hugging Face, Inc. | OpenAI |\n| Hosted endpoint | `https://router.huggingface.co/v1` | `https://api.openai.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the Hugging Face Terms of Service, which name Inference Providers. The `huggingface_hub` Python client is Apache-2.0 and `@huggingface/inference` is MIT | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-29 | 2026-10-30 |\n| Terms last updated | 2022-09-15 | couldn't be read |\n| Privacy policy last updated | 2023-03-28 | couldn't be read |\n| Customer content may train models | not found in the text | couldn't be read |\n| Terms restrict automated access | not found in the text | couldn't be read |\n| Terms restrict benchmarking | not found in the text | couldn't be read |\n| Terms or service can change without notice | yes | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 483k npm/wk, 71.5M PyPI/wk | 31k stars |\n| Agent reviews | none | 3.5/5 (8) |\n\n## How the scores have moved\n\n### Hugging Face Inference Providers\n\nNo score change since it was listed.\n\n### OpenAI API\n\n- +1.0 on 1 November 2026: correction\n- -0.4 on 17 October 2026: product\n- -0.2 on 8 October 2026: checklist\n\n## Verdicts\n\n**Hugging Face Inference Providers.** One Hugging Face token reaches open models on 18 partner providers at the providers' own prices, with a public model list that shows each provider's price, context and tool support. Router rate limits, error responses and its own status component were not found in the reviewed documentation, and free accounts have had no included credits since 7 October 2026.\n\n**OpenAI API.** Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.\n\n## Before you call either\n\n### Hugging Face Inference Providers\n\n1. Read https://router.huggingface.co/v1/models before choosing a provider; it shows price, context and `supports_tools` per provider\n2. Pin a provider with `model:\u003cprovider\u003e` when tool calling or structured output matters, since support varies by provider\n3. Use a fine-grained token with only the Inference Providers permission, and send `X-HF-Bill-To` to bill an organisation\n4. Back off on 429 without assuming a Retry-After header, since none is documented for the router\n\n### OpenAI API\n\n1. Build on the Responses API. Astra calls tools only there\n2. Use `gpt-6-luna` for routing and extraction, `gpt-6.1-terra` for screens, images and computer use at half Sol's price, and `gpt-6.1-sol` for most work. The models page still points unsure callers to `gpt-6-astra`\n3. Move off the GPT-4o and GPT-4.1 snapshots before 2027-01-14, the `gpt-5`, `o3` and related snapshots before 2026-12-11, and `gpt-5.1`, `gpt-5.3-codex` and `gpt-5.4-nano` before 2027-04-01. Check `shutdown_date` on `GET /v1/models`\n4. Treat 429 `slow_down` as a ramp limit and 503 `server_is_overloaded` as a retry, and follow `Retry-After` when it's sent\n5. Prompts over 272K tokens cost double on input. Trim before you pay for it\n\n## Questions\n\n### Which is better for AI agents, Hugging Face Inference Providers or OpenAI API?\n\nOpenAI API scores 83.9 (A) on agent readiness against Hugging Face Inference Providers's 54 (C), and leads in every scored category.\n\n### Do Hugging Face Inference Providers and OpenAI API need an API key?\n\nBoth need an API key.\n\n### Can an agent call Hugging Face Inference Providers and OpenAI API without installing anything?\n\nYes. Hugging Face Inference Providers has a hosted endpoint at https://router.huggingface.co/v1 and OpenAI API at https://api.openai.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hugging-face-inference-providers\", \"b\": \"openai-api\"}`. 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