GroqCloud by Groq

# Fix list: GroqCloud

From Anchor Terminal's listing at https://www.anchorterminal.com/tools/groq, the October 2026 research run, assessed 1 October 2026. Grade BB, 75.7 out of 100.

This 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.

For a coding agent working on GroqCloud: 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.

## 1. Payments & pricing, 40 out of 100, up to 7.5 more on the total

Why it scored 40: No machine payment protocol (0). Per-token prices on the models page, read without a login, such as gpt-oss-120b at $0.15 in and $0.60 out per million (20). Free plan with no card (20). Console sign-up in a browser (0).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-payments):

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.
- 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.
- 20, a free tier or trial that doesn't need a card.
- 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.

## 2. Schema & documentation, 64 out of 100, up to 5.9 more on the total

Why it scored 64: No OpenAPI document in the docs, and the SDK's .stats.yml carries only an endpoint count of 17, no spec URL (0). llms.txt at console.groq.com/llms.txt (10). Reference not read in full this run (15 of 20). Structured outputs have a Strict mode as well as Best-effort (15). The errors page lists 15 status codes with recovery advice, including 498 for Flex capacity and 424 for remote MCP auth, and the `error` object with `message` and `type` (14 of 15). The changelog linked from llms.txt is labelled legacy and wasn't read (10 of 15).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-schema):

APIs and MCP servers.

- 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool).
- 10, llms.txt or Markdown docs served for agents.
- 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.
- 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs.
- 0 to 15, examples and documented error responses.
- 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.

## 3. Security & auth, 77 out of 100, up to 4 more on the total

Why it scored 77: Model reading. Bearer keys scoped to a project, with custom request limits per project and model permissions at organisation and project level. We didn't find rotation documented (25 of 30). The services agreement bars training on inputs and outputs, per the listing; the data page doesn't mention training (20). No retention by default, up to 30 days for reliability and abuse monitoring, and zero retention as a setting any customer can turn on in Data Controls, read first-hand on the data page (15). Request logs, usage per project and a read-only Reader role (12 of 15). groq.com's security.txt holds a Contact line and nothing else, and the trust centre at trust.groq.com renders only with JavaScript, so certifications and any bug bounty are unchecked (5 of 20).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-security):

- 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.
- 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions.
- 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.
- 0 to 15, audit logs or per-call visibility for the operator.
- 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.

## 4. Agent ergonomics, 80 out of 100, up to 3.3 more on the total

Why it scored 80: Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use documented, parallel calls and forced choice not checked (15 of 20). Strict structured outputs (15). Automatic prompt caching at 50% off cached input, with a 2-hour cache life, on gpt-oss-20b, gpt-oss-120b and gpt-oss-safeguard-20b only (10 of 15). 131,072-token context on every self-serve model; only MiniMax M2.7, a preview on enterprise pricing, reaches 196,608 (5 of 15). Batch at half price (10). Official SDKs for Python and JavaScript (10). Errors page with codes, recovery advice and a typed error object (15).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-ergonomics):

- 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).
- 20, pagination, filtering and output-size controls.
- 20, actionable, documented error responses, codes and messages an agent can recover from.
- 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations.
- 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.

## 5. Maintenance & community, 72 out of 100, up to 2.5 more on the total

Why it scored 72: Model reading. `groq/compound` and `compound-mini` shut down on 21 September (30). Production models get an email and a migration path, previews may go at short notice, and no minimum period is stated. Llama 3.1 8B and 3.3 70B got 60 days on the free and developer tiers, Compound 28 days with no replacement named (4 of 12). Four shutdown dates in the last 90 days, 17 July, 16 August, 14 September and 21 September (0 of 8). Changelog marked legacy and not read, community and support not checked (10 of 15). SDK issue replies not sampled, since GitHub's API refused our shell (5 of 10). Python SDK 1.7.0 and TypeScript SDK 1.6.0, both on 25 August 2026, with six SDK releases between them since 3 July (15). CI, release-please and lock-file vulnerability fixes in 1.7.0 (8 of 10).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-maintenance):

- 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.
- 20, at least three releases or dated changelog entries in the last 90 days.
- 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.
- 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models).
- 10, package health, current dependencies and CI.

Models are read for deprecation notice periods and model churn rather than release counts.

## 6. Transparency & trust, 86 out of 100, up to 1.2 more on the total

Made of editorial 71, provenance 100.

Why it scored 86: Closed service with clear terms, SDKs Apache-2.0 (15). The data page agrees with the listing on retention and adds detail, batch files kept 30 days unless deleted, fine-tuning data kept until the customer deletes it, and SCCs for transfers. The training ban rests on the services agreement per the listing (26 of 30). Deprecations page with announcement and shutdown dates (20). All customer data in Google Cloud buckets in the US, per the data page. No sub-processor list read, since the trust centre needs JavaScript (10 of 20).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-transparency):

- 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.
- 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors).
- 0 to 20, a deprecation policy or notices with dates.
- 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.

## What we couldn't check

What 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.

- unchecked: certifications, sub-processors and any bug bounty. trust.groq.com renders only with JavaScript and security.txt has a Contact line only
- unchecked: the pricing page itself, which renders client-side. Prices here come from the models page, which lists Llama 3.1 8B and 3.3 70B under enterprise pricing, consistent with the deprecation's tier scope, so no deduction
- unchecked: replies on the SDK issue trackers, since GitHub's API refused our shell
- The training ban comes from the services agreement per the listing; the data page doesn't mention training
- Whether a status page with nothing posted since November 2025 reflects uptime or posting habits

## Weaknesses

- Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice
- 131,072-token context on every self-serve model
- No OpenAPI document, and the SDK metadata carries no spec URL
- Cached input is half price on the three gpt-oss models only
- The deprecations page still names qwen/qwen3.6-27b as a Llama 3.3 70B replacement, and that model shut down on 2026-09-14

## What costs an agent a turn today

The notes we give agents before they call it. Each one is a workaround an agent shouldn't need.

- Call `/models` at start-up. Four model ids stopped working this quarter
- Read `retry-after` on a 429 and the `x-ratelimit-remaining-tokens` header before the next call
- Free plan allows 8,000 tokens a minute on gpt-oss, so keep prompts small or batch them
- Don't build on Qwen 3.8 27B. It's a preview and previews can go at short notice
- Treat a 498 as Flex capacity and retry later; 5xx responses aren't billed

## What the review panel asked for

- programmatic key creation
- Minimum shutdown notice
- An OpenAPI document
- Publish an OpenAPI file
- Check the deprecations page against shutdown dates
- a minimum notice period
- an OpenAPI document
- Post incidents publicly
- State minimum notice
- documented key rotation
- readable trust centre
- a minimum notice period for production models
- Document a spend cap

## When it's done

Send 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.