{
  "fixes": {
    "slug": "openai-embeddings",
    "name": "OpenAI embeddings",
    "listing": "https://www.anchorterminal.com/tools/openai-embeddings",
    "markdown": "# Fix list: OpenAI embeddings\n\nFrom Anchor Terminal's listing at https://www.anchorterminal.com/tools/openai-embeddings, the October 2026 research run, assessed 1 October 2026. Grade BB, 73.4 out of 100.\n\nThis is everything the published grade says the listing lacks, the biggest possible gain to the total first. It comes from the reason given for each score, the checklist each category was scored against (https://www.anchorterminal.com/benchmark/#checklist), the provenance checks, the deductions, what we couldn't check and what the review panel asked for. A fix counts at the next check, once it's public.\n\nFor a coding agent working on OpenAI embeddings: work through the items below in the product, its docs and its public pages. Each category gives the reason for its score, with the points each checklist item earned, and the checklist itself, so the gap is the items that earned less than their points. Change the product, not the wording, and keep a note of what you changed and where it's published.\n\n## 1. Payments \u0026 pricing, 30 out of 100, up to 8.8 more on the total\n\nWhy it scored 30: No x402, MPP or L402 (0). Per-token prices published without a login, $0.02 and $0.13 per million tokens and half that in batch (20). The model page lists a free tier for embeddings (100 requests and 40,000 tokens a minute) in allowed countries, but credits are prepaid after adding payment details ($5 minimum) and nothing says a new account can call without a card, so half (10 of 20). A person signs up in a browser and creates the key (0).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-payments):\n\nThe published rubric, also on the [x402 page](https://www.anchorterminal.com/x402/).\n\n- 40, a machine payment protocol (x402, MPP or L402) on the tool's own endpoints. 10 to 30 when it covers only some endpoints or only goes through a third party, and the note says which.\n- 20, per-call or per-unit pricing published without a login. 10 for public plan-only pricing, 0 for \"contact sales\" or prices behind a login.\n- 20, a free tier or trial that doesn't need a card.\n- 20, autonomous onboarding, meaning an agent can get access without a person signing up in a browser (keyless use, x402, a programmatic key API).\n\nPayment platforms and agent wallets rarely charge for their own API over a machine protocol, so the first line has steps for them, and the highest one that applies counts. 40 when x402, MPP or L402 runs on all their own endpoints, 30 when it runs on part of their own API, 25 when their merchants can accept one, 20 for running a facilitator, 15 for paying as a buyer, and 0 when the only protocol is their own. Merchant acceptance sits above a facilitator because the platform's own customers can charge agents through it, while a facilitator settles for sellers who wire up the protocol themselves. The counter-argument (a facilitator does more for the protocol as a whole) has a point. Each note says which step applied.\n\nOpen-source software you run yourself is scored on its hosted or paid option if it has one. A free, self-hosted package with nothing to buy gets 20, 20 and 20 for the last three lines, and 0 to 40 for the first only if it ships a payment protocol.\n\n## 2. Reliability, 65 out of 100, up to 7 more on the total\n\nWhy it scored 65: status.openai.com (incident.io) has an Embeddings component with 90 days of history (20). Two incidents in the window list Embeddings among the affected components, elevated errors across API models on 17 September 2026 (about 1 hour 30 minutes) and failed requests across 30 components on 29 September 2026 (about 5 hours 22 minutes). Both are posted as degraded performance and the component still reads 100 per cent, but each is an hour or more of wide errors, so two majors. Our batch rule gives one major 10 and two majors 5 (5 of 30). Rate limits per spend tier are on the model page, from 100 requests and 40,000 tokens a minute on the free tier to 10,000 and 10 million at tier 5 (15). The rate-limit and error-code guides say to honour Retry-After and back off with jitter, and document x-ratelimit headers (15). The Scale Tier 99.9 per cent SLA lists GPT and o-series models and doesn't mention embeddings, so no SLA for this endpoint (0). Both models are GA (10).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-reliability):\n\nHosted APIs, MCP servers, models and platforms.\n\n- 20, a public status page with component history (Statuspage, Instatus, BetterStack or the vendor's own).\n- 0 to 30, the incident record for the last 90 days on that page. 30 for a clean record or trivial incidents only, 20 for minor incidents only, 10 for one major outage (an hour or more of a core API down, or errors across the board), 0 for several. 5 when there's no history we could read, and the note says so.\n- 15, rate limits documented with numbers.\n- 15, documented 429 or overload handling (Retry-After, backoff guidance), and idempotency keys or safe-retry guidance where writes are involved.\n- 10, an SLA published for any paid tier.\n- 10, the surface agents use is generally available, not beta or preview.\n\nLocal packages, SDKs, frameworks and stdio MCP servers.\n\n- 20, installs from an official package with supported runtimes stated.\n- 25, a public CI and test suite, passing on the default branch.\n- 0 to 25, open crash or regression issues relative to activity (25 for few and handled, 0 for many, old and unanswered).\n- 15, semver discipline and breaking changes called out in a changelog.\n- 15, version 1.0 or later, or declared stable.\n\nProtocols are read from their reference implementations, the public facilitators or servers, spec stability and test vectors.\n\n## 3. Maintenance \u0026 community, 60 out of 100, up to 3.5 more on the total\n\nWhy it scored 60: The embedding models are text-embedding-3-small and -large from 25 January 2024, the docs still call them the newest, and no changelog entry since June 2026 touches embeddings (0). The platform changelog has 16 dated entries between 4 June and 26 August 2026 (20). openai-python has 219 open issues and 392 open pull requests, and the twelve newest open issues showed no visible maintainer reply, though maintainers do answer and close others (15 of 25). Current official SDKs, openai 3.22.1 on PyPI on 30 September 2026 and openai 7.25.0 on npm (15). GitHub Actions CI, Python 3.10 or later, generated from the OpenAPI spec (10).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-maintenance):\n\n- 0 to 30, time since the last release, or the last published model or API change for a closed service. 30 within 30 days, 20 within 90, 10 within 180, 0 older.\n- 20, at least three releases or dated changelog entries in the last 90 days.\n- 0 to 25, responsiveness. Issues and pull requests answered on GitHub (the open issues and how recent the replies are). For closed services, a public changelog and a support or community channel that answers, 0 to 15.\n- 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models).\n- 10, package health, current dependencies and CI.\n\nModels are read for deprecation notice periods and model churn rather than release counts.\n\n## 4. Schema \u0026 documentation, 89 out of 100, up to 1.8 more on the total\n\nWhy it scored 89: OpenAPI document in openai/openai-openapi, generated from upstream and synced, covering /v1/embeddings (25). llms.txt at developers.openai.com (10). The guide explains what embeddings are for (search, clustering, recommendations, anomaly detection, classification) and how to shorten vectors, but says little about when another model or a reranker fits better (14 of 20). input and model are required, dimensions has a minimum, encoding_format is an enum of float or base64, and the per-input and per-request token caps are stated (13 of 15). A curl example and a full response object on the reference page, and a separate error-code page, though the reference page itself lists no errors (12 of 15). Dated public changelog and pinned model ids (15).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-schema):\n\nAPIs and MCP servers.\n\n- 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool).\n- 10, llms.txt or Markdown docs served for agents.\n- 0 to 20, descriptions that say what a tool is for, when to use it and when not to, read from the tool definitions in the source or the API reference.\n- 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs.\n- 0 to 15, examples and documented error responses.\n- 15, versioning and a public changelog.\n\nModels are read from the API reference, the OpenAPI file, llms.txt, the structured-output and tool-use docs and the model cards. Frameworks from docs a model can follow, typed interfaces, examples and the API reference.\n\n## 5. Agent ergonomics, 90 out of 100, up to 1.6 more on the total\n\nWhy it scored 90: The dimensions parameter cuts either model to any size, and base64 encoding shrinks the payload, but there's no int8 or binary output (20 of 25). Up to 2,048 inputs and 300,000 tokens a request, and no truncation switch, so an over-long input fails rather than being cut (15 of 20). The error-code page gives each 401, 403, 429, 500 and 503 case a cause and a fix, and separates quota errors from rate limits (20). Embedding calls are stateless and the docs give Retry-After and backoff guidance (20). Two required parameters and official SDKs in Python, TypeScript and other languages (15).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-ergonomics):\n\n- 0 to 25, context cost. For MCP, the number and size of the tool definitions (25 for ten or fewer compact tools, 15 for 11 to 30, 5 for more than 30, plus up to 10 back for toolsets, dynamic loading or read-only subsets). For APIs, whether responses can be sized (field selection, limits, summaries).\n- 20, pagination, filtering and output-size controls.\n- 20, actionable, documented error responses, codes and messages an agent can recover from.\n- 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations.\n- 15, sensible defaults, few required parameters, and official SDKs in at least two languages.\n\nModels are read for tool use, structured output, prompt caching, context length, batch and SDKs. Frameworks for how much code and how many defaults a tool-calling agent with MCP needs.\n\n## 6. Transparency \u0026 trust, 88 out of 100, up to 1.1 more on the total\n\nMade of editorial 75, provenance 100.\n\nWhy it scored 88: Closed service under a published services agreement, SDKs Apache-2.0 (15). API data isn't used for training by default, abuse-monitoring logs are kept up to 30 days and zero retention is available by approval, and the guides agree on this. We didn't read the DPA in this run (25 of 30). The deprecations page states at least six months' notice for GA models and three for specialised variants, with dated entries (20). Regional processing can be chosen per request since 21 August 2026, and the subprocessor list wasn't checked in this run (15 of 20).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-transparency):\n\n- 0 to 30, source availability and licence clarity. 30 for open source under an OSI licence, 15 for closed with clear terms, 0 for unclear terms.\n- 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors).\n- 0 to 20, a deprecation policy or notices with dates.\n- 0 to 20, telemetry disclosed with an opt-out (local software), or subprocessors and data locations disclosed (hosted).\n\nThe other half of Transparency and trust is the provenance score, computed from checked facts (below). The category score is the mean of the two.\n\n## 7. Security \u0026 auth, 95 out of 100, up to 0.9 more on the total\n\nWhy it scored 95: Project-scoped keys with Restricted and Read-only modes that set None, Read or Write per endpoint, plus service-account keys and admin keys kept separate (30). A restricted key can drop write access to files, fine-tuning and other endpoints, and the embedding endpoint has no destructive action (20). Returns vectors only, no untrusted text (10). Usage and cost dashboards can be filtered by API key since 4 August 2026, and enterprise organisations get audit logs (15). security.txt is valid, a public bug bounty and SOC 2 Type 2, as checked for the OpenAI API listing, and the Mixpanel incident was disclosed in public with what was and wasn't exposed (20).\n\nThe checklist (https://www.anchorterminal.com/benchmark/#checklist-security):\n\n- 0 to 30, the credential model. 30 for OAuth 2.1 with scopes, or scoped and revocable keys with rotation. 20 for plain revocable API keys. 10 for one all-powerful key. 10 off when a secret can travel in a URL query string as a documented option.\n- 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions.\n- 0 to 15, prompt-injection posture where the tool returns untrusted content (documented mitigations or guidance). A tool that returns no untrusted content gets 10.\n- 0 to 15, audit logs or per-call visibility for the operator.\n- 0 to 20, a security programme. security.txt or a disclosure policy, a bug bounty, SOC 2 or ISO 27001, advisories handled in public.\n\nModels are read for retention, whether API data trains models (and whether that's off by default), zero-retention options and certifications. Frameworks for telemetry defaults, approval hooks, guardrails and sandboxing.\n\n## Deductions\n\nEach comes off the total. A fixed and documented problem counts for less at the next check.\n\n- A breach at Mixpanel, OpenAI's analytics vendor, began on 2025-11-09 and was reported to OpenAI on 2025-11-25. It exposed names, email addresses, coarse location, browser data and organisation and user IDs of platform.openai.com users, but no API keys, API requests or usage data. OpenAI removed Mixpanel, notified those affected and published the details. Fixed and documented, so a small, decayed deduction (-2). https://openai.com/index/mixpanel-incident/\n\n## What we couldn't check\n\nWhat we couldn't read counted as absent. Publishing it on a page a plain HTTP fetch can read (not only in a browser) lets the next check count it.\n\n- Whether a brand-new account can call the embeddings endpoint on the free tier without adding a card. The rate-limits page lists a free tier, the billing help says credits are bought after adding payment details.\n- The incident pages call both September incidents degraded performance, and the Embeddings component still shows 100 per cent, so how many embedding calls failed isn't public. The root-cause analysis for 29 September was promised within five business days.\n- Whether OpenAI plans a successor to text-embedding-3. Nothing in the changelog or deprecations page says so.\n\n## Weaknesses\n\n- No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff\n- Text only, 8,192 tokens an input, and no reranker\n- Over-long inputs fail rather than being truncated, and output is float or base64 only\n- A free tier is listed, but credits are prepaid after adding payment details, and nothing confirms a start without a card\n- Elevated errors across the API including Embeddings on 17 and 29 September 2026, for about 1.5 and 5.4 hours\n\n## What costs an agent a turn today\n\nThe notes we give agents before they call it. Each one is a workaround an agent shouldn't need.\n\n- Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens\n- Count tokens before sending. An input over 8,192 tokens is rejected, not truncated\n- Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself\n- Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window\n- Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)\n\n## What the review panel asked for\n\n- State failed-call billing\n- List the error codes on the reference page\n\n## When it's done\n\nSend what changed and where it's published as a dispute (https://www.anchorterminal.com/builders/#disputes, or `POST https://www.anchorterminal.com/api/v1/contact` with `\"kind\": \"dispute\"`). Disputes are answered in public, and the listing is checked again by the same checklist. Paying for an audit or a listing claim changes nothing here.\n",
    "grade": "BB",
    "score": 73.4,
    "assessed": "2026-10-01",
    "run": "October 2026 research run",
    "categories": [
      {
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "score": 30,
        "maxGain": 8.8,
        "reason": "No x402, MPP or L402 (0). Per-token prices published without a login, $0.02 and $0.13 per million tokens and half that in batch (20). The model page lists a free tier for embeddings (100 requests and 40,000 tokens a minute) in allowed countries, but credits are prepaid after adding payment details ($5 minimum) and nothing says a new account can call without a card, so half (10 of 20). A person signs up in a browser and creates the key (0).",
        "checklist": [
          "The published rubric, also on the [x402 page](https://www.anchorterminal.com/x402/).",
          "- 40, a machine payment protocol (x402, MPP or L402) on the tool's own endpoints. 10 to 30 when it covers only some endpoints or only goes through a third party, and the note says which.\n- 20, per-call or per-unit pricing published without a login. 10 for public plan-only pricing, 0 for \"contact sales\" or prices behind a login.\n- 20, a free tier or trial that doesn't need a card.\n- 20, autonomous onboarding, meaning an agent can get access without a person signing up in a browser (keyless use, x402, a programmatic key API).",
          "Payment platforms and agent wallets rarely charge for their own API over a machine protocol, so the first line has steps for them, and the highest one that applies counts. 40 when x402, MPP or L402 runs on all their own endpoints, 30 when it runs on part of their own API, 25 when their merchants can accept one, 20 for running a facilitator, 15 for paying as a buyer, and 0 when the only protocol is their own. Merchant acceptance sits above a facilitator because the platform's own customers can charge agents through it, while a facilitator settles for sellers who wire up the protocol themselves. The counter-argument (a facilitator does more for the protocol as a whole) has a point. Each note says which step applied.",
          "Open-source software you run yourself is scored on its hosted or paid option if it has one. A free, self-hosted package with nothing to buy gets 20, 20 and 20 for the last three lines, and 0 to 40 for the first only if it ships a payment protocol."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-payments"
      },
      {
        "key": "reliability",
        "name": "Reliability",
        "score": 65,
        "maxGain": 7,
        "reason": "status.openai.com (incident.io) has an Embeddings component with 90 days of history (20). Two incidents in the window list Embeddings among the affected components, elevated errors across API models on 17 September 2026 (about 1 hour 30 minutes) and failed requests across 30 components on 29 September 2026 (about 5 hours 22 minutes). Both are posted as degraded performance and the component still reads 100 per cent, but each is an hour or more of wide errors, so two majors. Our batch rule gives one major 10 and two majors 5 (5 of 30). Rate limits per spend tier are on the model page, from 100 requests and 40,000 tokens a minute on the free tier to 10,000 and 10 million at tier 5 (15). The rate-limit and error-code guides say to honour Retry-After and back off with jitter, and document x-ratelimit headers (15). The Scale Tier 99.9 per cent SLA lists GPT and o-series models and doesn't mention embeddings, so no SLA for this endpoint (0). Both models are GA (10).",
        "checklist": [
          "Hosted APIs, MCP servers, models and platforms.",
          "- 20, a public status page with component history (Statuspage, Instatus, BetterStack or the vendor's own).\n- 0 to 30, the incident record for the last 90 days on that page. 30 for a clean record or trivial incidents only, 20 for minor incidents only, 10 for one major outage (an hour or more of a core API down, or errors across the board), 0 for several. 5 when there's no history we could read, and the note says so.\n- 15, rate limits documented with numbers.\n- 15, documented 429 or overload handling (Retry-After, backoff guidance), and idempotency keys or safe-retry guidance where writes are involved.\n- 10, an SLA published for any paid tier.\n- 10, the surface agents use is generally available, not beta or preview.",
          "Local packages, SDKs, frameworks and stdio MCP servers.",
          "- 20, installs from an official package with supported runtimes stated.\n- 25, a public CI and test suite, passing on the default branch.\n- 0 to 25, open crash or regression issues relative to activity (25 for few and handled, 0 for many, old and unanswered).\n- 15, semver discipline and breaking changes called out in a changelog.\n- 15, version 1.0 or later, or declared stable.",
          "Protocols are read from their reference implementations, the public facilitators or servers, spec stability and test vectors."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-reliability"
      },
      {
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "score": 60,
        "maxGain": 3.5,
        "reason": "The embedding models are text-embedding-3-small and -large from 25 January 2024, the docs still call them the newest, and no changelog entry since June 2026 touches embeddings (0). The platform changelog has 16 dated entries between 4 June and 26 August 2026 (20). openai-python has 219 open issues and 392 open pull requests, and the twelve newest open issues showed no visible maintainer reply, though maintainers do answer and close others (15 of 25). Current official SDKs, openai 3.22.1 on PyPI on 30 September 2026 and openai 7.25.0 on npm (15). GitHub Actions CI, Python 3.10 or later, generated from the OpenAPI spec (10).",
        "checklist": [
          "- 0 to 30, time since the last release, or the last published model or API change for a closed service. 30 within 30 days, 20 within 90, 10 within 180, 0 older.\n- 20, at least three releases or dated changelog entries in the last 90 days.\n- 0 to 25, responsiveness. Issues and pull requests answered on GitHub (the open issues and how recent the replies are). For closed services, a public changelog and a support or community channel that answers, 0 to 15.\n- 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models).\n- 10, package health, current dependencies and CI.",
          "Models are read for deprecation notice periods and model churn rather than release counts."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-maintenance"
      },
      {
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "score": 89,
        "maxGain": 1.8,
        "reason": "OpenAPI document in openai/openai-openapi, generated from upstream and synced, covering /v1/embeddings (25). llms.txt at developers.openai.com (10). The guide explains what embeddings are for (search, clustering, recommendations, anomaly detection, classification) and how to shorten vectors, but says little about when another model or a reranker fits better (14 of 20). input and model are required, dimensions has a minimum, encoding_format is an enum of float or base64, and the per-input and per-request token caps are stated (13 of 15). A curl example and a full response object on the reference page, and a separate error-code page, though the reference page itself lists no errors (12 of 15). Dated public changelog and pinned model ids (15).",
        "checklist": [
          "APIs and MCP servers.",
          "- 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool).\n- 10, llms.txt or Markdown docs served for agents.\n- 0 to 20, descriptions that say what a tool is for, when to use it and when not to, read from the tool definitions in the source or the API reference.\n- 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs.\n- 0 to 15, examples and documented error responses.\n- 15, versioning and a public changelog.",
          "Models are read from the API reference, the OpenAPI file, llms.txt, the structured-output and tool-use docs and the model cards. Frameworks from docs a model can follow, typed interfaces, examples and the API reference."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-schema"
      },
      {
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "score": 90,
        "maxGain": 1.6,
        "reason": "The dimensions parameter cuts either model to any size, and base64 encoding shrinks the payload, but there's no int8 or binary output (20 of 25). Up to 2,048 inputs and 300,000 tokens a request, and no truncation switch, so an over-long input fails rather than being cut (15 of 20). The error-code page gives each 401, 403, 429, 500 and 503 case a cause and a fix, and separates quota errors from rate limits (20). Embedding calls are stateless and the docs give Retry-After and backoff guidance (20). Two required parameters and official SDKs in Python, TypeScript and other languages (15).",
        "checklist": [
          "- 0 to 25, context cost. For MCP, the number and size of the tool definitions (25 for ten or fewer compact tools, 15 for 11 to 30, 5 for more than 30, plus up to 10 back for toolsets, dynamic loading or read-only subsets). For APIs, whether responses can be sized (field selection, limits, summaries).\n- 20, pagination, filtering and output-size controls.\n- 20, actionable, documented error responses, codes and messages an agent can recover from.\n- 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations.\n- 15, sensible defaults, few required parameters, and official SDKs in at least two languages.",
          "Models are read for tool use, structured output, prompt caching, context length, batch and SDKs. Frameworks for how much code and how many defaults a tool-calling agent with MCP needs."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-ergonomics"
      },
      {
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "score": 88,
        "maxGain": 1.1,
        "reason": "Closed service under a published services agreement, SDKs Apache-2.0 (15). API data isn't used for training by default, abuse-monitoring logs are kept up to 30 days and zero retention is available by approval, and the guides agree on this. We didn't read the DPA in this run (25 of 30). The deprecations page states at least six months' notice for GA models and three for specialised variants, with dated entries (20). Regional processing can be chosen per request since 21 August 2026, and the subprocessor list wasn't checked in this run (15 of 20).",
        "blend": "editorial 75, provenance 100",
        "checklist": [
          "- 0 to 30, source availability and licence clarity. 30 for open source under an OSI licence, 15 for closed with clear terms, 0 for unclear terms.\n- 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors).\n- 0 to 20, a deprecation policy or notices with dates.\n- 0 to 20, telemetry disclosed with an opt-out (local software), or subprocessors and data locations disclosed (hosted).",
          "The other half of Transparency and trust is the provenance score, computed from checked facts (below). The category score is the mean of the two."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-transparency"
      },
      {
        "key": "security",
        "name": "Security \u0026 auth",
        "score": 95,
        "maxGain": 0.9,
        "reason": "Project-scoped keys with Restricted and Read-only modes that set None, Read or Write per endpoint, plus service-account keys and admin keys kept separate (30). A restricted key can drop write access to files, fine-tuning and other endpoints, and the embedding endpoint has no destructive action (20). Returns vectors only, no untrusted text (10). Usage and cost dashboards can be filtered by API key since 4 August 2026, and enterprise organisations get audit logs (15). security.txt is valid, a public bug bounty and SOC 2 Type 2, as checked for the OpenAI API listing, and the Mixpanel incident was disclosed in public with what was and wasn't exposed (20).",
        "checklist": [
          "- 0 to 30, the credential model. 30 for OAuth 2.1 with scopes, or scoped and revocable keys with rotation. 20 for plain revocable API keys. 10 for one all-powerful key. 10 off when a secret can travel in a URL query string as a documented option.\n- 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions.\n- 0 to 15, prompt-injection posture where the tool returns untrusted content (documented mitigations or guidance). A tool that returns no untrusted content gets 10.\n- 0 to 15, audit logs or per-call visibility for the operator.\n- 0 to 20, a security programme. security.txt or a disclosure policy, a bug bounty, SOC 2 or ISO 27001, advisories handled in public.",
          "Models are read for retention, whether API data trains models (and whether that's off by default), zero-retention options and certifications. Frameworks for telemetry defaults, approval hooks, guardrails and sandboxing."
        ],
        "checklistUrl": "https://www.anchorterminal.com/benchmark/#checklist-security"
      }
    ],
    "deductions": [
      "A breach at Mixpanel, OpenAI's analytics vendor, began on 2025-11-09 and was reported to OpenAI on 2025-11-25. It exposed names, email addresses, coarse location, browser data and organisation and user IDs of platform.openai.com users, but no API keys, API requests or usage data. OpenAI removed Mixpanel, notified those affected and published the details. Fixed and documented, so a small, decayed deduction (-2). https://openai.com/index/mixpanel-incident/"
    ],
    "unchecked": [
      "Whether a brand-new account can call the embeddings endpoint on the free tier without adding a card. The rate-limits page lists a free tier, the billing help says credits are bought after adding payment details.",
      "The incident pages call both September incidents degraded performance, and the Embeddings component still shows 100 per cent, so how many embedding calls failed isn't public. The root-cause analysis for 29 September was promised within five business days.",
      "Whether OpenAI plans a successor to text-embedding-3. Nothing in the changelog or deprecations page says so."
    ],
    "weaknesses": [
      "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff",
      "Text only, 8,192 tokens an input, and no reranker",
      "Over-long inputs fail rather than being truncated, and output is float or base64 only",
      "A free tier is listed, but credits are prepaid after adding payment details, and nothing confirms a start without a card",
      "Elevated errors across the API including Embeddings on 17 and 29 September 2026, for about 1.5 and 5.4 hours"
    ],
    "agentNotes": [
      "Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens",
      "Count tokens before sending. An input over 8,192 tokens is rejected, not truncated",
      "Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself",
      "Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window",
      "Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)"
    ],
    "requests": [
      {
        "text": "State failed-call billing",
        "reviews": 1
      },
      {
        "text": "List the error codes on the reference page",
        "reviews": 1
      }
    ],
    "recheck": "https://www.anchorterminal.com/builders/#disputes"
  },
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-05",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.3",
    "openapi": "https://www.anchorterminal.com/openapi.json",
    "preview": false,
    "run": "2026-10-01",
    "runLabel": "October 2026 research run"
  }
}