{ "data": { "a": { "slug": "groq", "name": "GroqCloud", "vendor": "Groq", "vendorUrl": "https://groq.com", "kind": "model", "category": "inference", "summary": "OpenAI-compatible inference API serving open-weight models on Groq's processors.", "url": "https://www.anchorterminal.com/tools/groq", "markdownUrl": "https://www.anchorterminal.com/tools/groq.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/groq.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/groq.json", "dns": "groq.grade.anchorterminal.com", "repo": "https://github.com/groq/groq-python", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.groq.com/openai/v1", "packages": [ { "registry": "pypi", "name": "groq", "main": true }, { "registry": "npm", "name": "groq-sdk", "main": true } ], "auth": "api-key", "authNotes": "Bearer key from the GroqCloud console.", "pricing": "freemium", "pricingNotes": "Free with no card at 30 requests a minute and 1,000 a day. The Developer plan is postpaid by card, bank or SEPA. Cached input half price on gpt-oss only, batch half price (https://groq.com/pricing).", "priceSummary": "from $0.075 / 1M in", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 619, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://console.groq.com/docs", "rateLimitsUrl": "https://console.groq.com/docs/rate-limits", "llmsTxt": "https://console.groq.com/llms.txt", "capabilities": [ "inference.llm", "inference.fast", "inference.open-weights" ], "tags": [ "hosted", "model", "fast", "free-tier", "no-card", "llms-txt" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 74.5, "grade": "BB", "agentReady": true, "rank": 81, "ranked": true, "rankOf": 1515, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 80, "maintenance": 72, "payments": 40, "reliability": 90, "schema": 64, "security": 82, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.", "bestFor": "Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.", "strengths": [ "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss", "Per-model limits published, `x-ratelimit-*` headers on every response and `retry-after` on 429", "API keys scoped to a project, with per-project request limits, model permissions, request logs and documented rotation", "No retention by default and zero retention as a self-serve setting, and server errors aren't charged", "Strict structured outputs, a half-price Batch API and a 99.9% SLA on the enterprise Performance Tier" ], "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", "The changelog's newest entry is 18 April 2026; shutdowns since then appear only on the deprecations page", "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" ], "agentNotes": [ "Call `/models` at start-up. Four model ids stopped working between July and September", "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; server errors aren't billed" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 8, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 74.5 } ], "editorialScores": { "ergonomics": 80, "maintenance": 72, "payments": 40, "reliability": 90, "schema": 64, "security": 82, "transparency": 71 }, "provenanceScore": 98, "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": "2026-10-17", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install groq # or: npm i groq-sdk", "http": "curl https://api.groq.com/openai/v1/chat/completions \\\n -H \"Authorization: Bearer $GROQ_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"openai/gpt-oss-120b\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/groq" }, "sameCompany": [ "groq-speech-to-text" ], "area": "models", "provenance": { "legalEntity": "Groq LLC", "domain": "groq.com", "domainRegistered": "2007-07-22", "domainNote": "groq.com was registered in 2007, before Groq existed. Customers in the EEA contract with Groq UK Limited.", "endpointOnVendorDomain": true, "terms": "https://console.groq.com/docs/legal/services-agreement", "privacy": "https://groq.com/privacy-policy", "statusPage": "https://groqstatus.com", "changelog": "https://console.groq.com/docs/changelog", "securityTxt": "valid", "checked": "2026-09-26", "score": 98 }, "requirements": { "setupChecked": true, "complianceChecked": true, "card": "paid-only", "freeTier": true, "minutes": 3, "credential": "api-key", "hosting": [ "US", "global" ], "euResidency": "unknown", "trainsOnData": "no", "zeroRetention": "yes", "certifications": [ "SOC 2 Type II", "GDPR" ], "dpa": true, "selfHost": "no" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/groq.json", "live": { "slug": "groq", "probe": { "target": "https://api.groq.com/openai/v1", "method": "get", "lastAt": "2026-11-09T20:49:53.624380042Z", "lastOk": true, "lastStatus": 404, "lastMs": 192, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 188, "p95ms24h": 292, "samples24h": 244, "samples30d": 7881, "days": [ { "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": 246 }, { "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": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "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": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 212, "ok": 212 } ], "since": "2026-10-08T00:00:00Z" }, "vendorStatus": { "page": "https://groqstatus.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-09T20:46:17.656378922Z" }, "versions": [ { "registry": "github", "name": "groq/groq-python", "version": "v1.7.0", "released": "2026-08-26", "seenAt": "2026-11-09T02:24:42.371259181Z" }, { "registry": "npm", "name": "groq-sdk", "version": "1.6.0", "seenAt": "2026-11-09T02:24:42.268192204Z" }, { "registry": "pypi", "name": "groq", "version": "1.7.0", "released": "2026-08-26", "seenAt": "2026-11-09T02:24:42.156408797Z" } ], "githubStars": 621, "npmWeekly": 1221904, "pypiWeekly": 4194784, "securityTxt": { "url": "https://groq.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-11-09T16:04:43.648036081Z" }, "llmsTxt": { "url": "https://console.groq.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:39:44.706283587Z" }, "domain": { "domain": "groq.com", "registered": "2007-07-22", "source": "https://rdap.verisign.com/com/v1/domain/groq.com", "checkedAt": "2026-11-08T14:48:48.829497279Z" }, "pages": [ { "url": "https://console.groq.com/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-08T18:16:39.832959533Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "ac2cb86ef05a" }, { "url": "https://console.groq.com/docs/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:33:17.729497744Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "396a91396e58" }, { "url": "https://groq.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-09T15:41:49.272186964Z", "changedAt": "2026-10-09T18:39:57.403860983Z", "fingerprint": "5d2ffa871aea" }, { "url": "https://groq.com/privacy-policy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-08T18:20:46.366514564Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "05dc8da7bc78" }, { "url": "https://console.groq.com/docs/legal/services-agreement", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:16:44.104843177Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a0b93cf713d4" } ], "models": { "openai/gpt-oss-120b": { "openrouterId": "openai/gpt-oss-120b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 131072, "maxOutput": 65536, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "openai/gpt-oss-20b": { "openrouterId": "openai/gpt-oss-20b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 131072, "maxOutput": 117964, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "qwen/qwen3.8-27b": { "openrouterId": "qwen/qwen3.8-27b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "image", "text", "video" ], "promptCaching": true, "contextTokens": 1024000, "maxOutput": 235929, "checkedAt": "2026-11-08T23:49:27.027409601Z" } }, "github": { "repo": "groq/groq-python", "checkedAt": "2026-11-09T10:28:43.551292254Z", "stars": 621, "archived": false, "issuesEnabled": false, "pushedAt": "2026-09-04T20:45:45Z", "openIssues": 0, "openPRs": 1, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 2, "ci": { "branch": "main", "runs": 39, "success": 29, "failure": 2, "other": 8, "latest": "skipped", "latestAt": "2026-09-04T20:45:23Z" } }, "updatedAt": "2026-11-09T20:49:53.624380042Z" }, "streak": { "days": 32, "from": "2026-10-08", "sinceWatching": true, "last": { "kind": "shutdown", "date": "2026-09-21", "what": "`groq/compound` and `groq/compound-mini` shut down", "source": "https://console.groq.com/docs/deprecations" }, "best": 32 } }, "answer": "GroqCloud scores 74.5 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.", "b": { "slug": "muse-ember", "name": "Muse Ember", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.", "url": "https://www.anchorterminal.com/tools/muse-ember", "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json", "dns": "muse-ember.grade.anchorterminal.com", "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "transports": [], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://dev.meta.ai/docs/muse-ember", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "openai-compatible", "anthropic-compatible", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 51.8, "grade": "D", "agentReady": false, "rank": 1017, "ranked": true, "rankOf": 1515, "categoryRank": 36, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "strengths": [ "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login", "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens", "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints", "Tool calling, structured output, computer use and search grounding", "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to", "3,000 requests and 4,000,000 tokens a minute per team, published" ], "weaknesses": [ "No weights and no parameter count published, so it can't be run locally", "Requests over 200K input tokens are billed at three times the rate for the whole request", "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower", "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing", "Benchmark and cost figures are Meta's own, several on internal benchmarks" ], "agentNotes": [ "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY", "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing", "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate", "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises", "Use strict structured output for extraction and routing steps", "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.8 } ], "editorialScores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-09", "maintenance": "2026-11-09", "payments": "2026-11-09", "reliability": "2026-11-09", "schema": "2026-11-09", "security": "2026-11-09", "transparency": "2026-11-09" }, "last": "2026-11-09", "oldest": "2026-11-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"muse-ember-1.0\",\n \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n \"reasoning\": {\"effort\": \"low\"}\n}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-ember" }, "sameCompany": [ "muse-spark-open", "muse-code", "muse-glimmer-30b" ], "sameModel": [ "meta-model-api" ], "area": "models", "unitPrices": [ { "item": "Muse Ember input", "unit": "1m-tokens", "usd": 0.1, "note": "Up to 200K input tokens a request; $0.30 above, for the whole request" }, { "item": "Muse Ember cached input", "unit": "1m-tokens", "usd": 0.004, "note": "$0.012 above 200K input tokens" }, { "item": "Muse Ember output", "unit": "1m-tokens", "usd": 0.5, "note": "$1.50 above 200K input tokens" } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-09", "notes": [ "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.", "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json", "live": { "slug": "muse-ember", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-11-09T20:50:02.257490788Z", "lastOk": true, "lastStatus": 404, "lastMs": 46, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 72, "samples24h": 10, "samples30d": 10, "days": [ { "date": "2026-11-09", "probes": 10, "ok": 10 } ], "since": "2026-11-09T19:55:51.172718582Z" }, "updatedAt": "2026-11-09T20:50:02.257490788Z" }, "streak": { "days": 0, "from": "2026-11-09", "sinceWatching": true, "best": 0 } }, "edges": [ { "by": 50, "category": "reliability", "leads": "groq", "name": "Reliability", "reasons": { "a": "Status page at groqstatus.com on incident.io, with components for the API, each production and preview model and the website (20). The page's RSS feed and the incident pages it links, read on 1 November, list three incidents in the 90-day window, all in October, and nothing since 12 October apart from a console maintenance on 18 October. On 10 October the enterprise-only preview MiniMax M2.7 had failed requests from data centre hardware issues from 10:09 to 16:14 UTC. The incident's header and impact record say degraded performance, while its Identified update set the model to partial outage and warned of failed requests. On 12 October console.groq.com returned 500 errors on metrics, /models and key creation from 03:17 to 03:38, and the vendor said inference requests were not affected. On 12 October MiniMax M2.7 had a partial outage from 18:17 to 18:36. None took down a core API for an hour, since the six-hour incident hit one enterprise preview model that the status page describes as for evaluation only, so minor incidents only (20). A stricter reading would count it as a major and give 10. Limits published per model, 30 requests a minute, 1,000 a day and 8,000 tokens a minute on gpt-oss for the free plan, re-read on 1 November (15). 429 carries `retry-after`, `x-ratelimit-*` headers come on every response, and the errors page says to wait before retrying a 503 and that a 502 may clear on retry (15). The Performance Tier, enterprise only, lists a 99.9% availability SLA with details in the offline agreement, per the 6 October check, not re-read on 1 November (10). gpt-oss and Whisper models are production, Qwen 3.8 27B, MiniMax M2.7 and Muse Spark Open are previews (10). 20+20+15+15+10+10 = 90.", "b": "Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40." }, "scores": { "a": 90, "b": 40 } }, { "by": 32, "category": "transparency", "leads": "groq", "name": "Transparency \u0026 trust", "reasons": { "a": "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 services agreement bars training on inputs and outputs, and the DPA gives 15 days' notice of sub-processor changes to subscribers, both read on 6 October. The agreement extends zero retention to 'Eligible Customers' where the data page says all customers may enable it (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. The DPA points to a sub-processor list at trust.groq.com/subprocessors, which needs JavaScript and wasn't read (10 of 20).", "b": "Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41." }, "scores": { "a": 85, "b": 53 } }, { "by": 26, "category": "maintenance", "leads": "groq", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. Meta's Muse Spark Open went into limited preview on GroqCloud on 15 October 2026, per Groq's announcement, 17 days before this check, and the models page lists it as an enterprise model (30). The newest shutdown is still `groq/compound` and `compound-mini` on 21 September. The changelog's newest entry is 18 April 2026, the Python SDK 1.7.0 (tagged 25 August) and TypeScript SDK 1.6.0 are still the newest tags, with commits to 4 September and no release since, and the deprecations page has no announcement after 24 August. 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). Three shutdown dates in the 90 days to 1 November, 16 August, 14 September and 21 September. Three in 90 days is still frequent churn (0 of 8). A dated changelog with an RSS feed and a community forum exist, but the changelog's newest entry is 18 April 2026; support replies 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 published on 26 August 2026 and still the newest tags on 1 November (15). CI, release-please and lock-file vulnerability fixes in 1.7.0 (8 of 10). 30+4+0+10+5+15+8 = 72.", "b": "Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46." }, "scores": { "a": 72, "b": 46 } }, { "by": 20, "category": "payments", "leads": "groq", "name": "Payments \u0026 pricing", "reasons": { "a": "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).", "b": "Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20." }, "scores": { "a": 40, "b": 20 } }, { "by": 18, "category": "security", "leads": "groq", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Bearer keys scoped to a project, with custom request limits per project and model permissions at organisation and project level. The security onboarding page advises rotating keys periodically and revoking them at once if compromise is suspected (30). The services agreement, read first-hand on 6 October and not re-read on 17 October, says Groq is not permitted to use inputs or outputs for training unless the customer grants permission (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, per 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 is a Vanta page that renders only with JavaScript, so certifications and any bug bounty are unchecked (5 of 20).", "b": "Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64." }, "scores": { "a": 82, "b": 64 } }, { "by": 8, "category": "ergonomics", "leads": "groq", "name": "Agent ergonomics", "reasons": { "a": "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).", "b": "Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72." }, "scores": { "a": 80, "b": 72 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Groq", "b": "Meta", "name": "Vendor" }, { "a": "https://api.groq.com/openai/v1", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDKs)", "b": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-15", "b": "2026-11-09", "name": "Last release" }, { "a": "2026-06-22", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "2025-11-12", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "619 stars", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (8)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "GroqCloud scores 74.5 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.", "question": "Which is better for AI agents, GroqCloud or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do GroqCloud and Muse Ember need an API key?" }, { "answer": "Yes. GroqCloud has a hosted endpoint at https://api.groq.com/openai/v1 and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call GroqCloud and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 90 against 40", "Agent ergonomics, 80 against 72", "Security \u0026 auth, 82 against 64", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 72 against 46", "Transparency \u0026 trust, 85 against 53" ], "also": [ "Agent-ready, a grade of BB or better", "Free to start without a card" ], "goodFor": "Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.", "slug": "groq", "watchFor": "Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice" }, { "aheadOn": null, "also": null, "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "groq": [ { "causes": [ "reliability", "evidence" ], "date": "2026-10-17", "delta": -1.1, "from": 75.6, "fromGrade": "BB", "to": 74.5, "toGrade": "BB" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.1, "from": 75.7, "fromGrade": "BB", "to": 75.6, "toGrade": "BB" } ], "muse-ember": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq.json", "title": "Amazon Bedrock vs GroqCloud", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember" }, { 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"https://www.anchorterminal.com/compare/muse-ember-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json", "title": "Muse Ember vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" } ], "panel": null, "scores": [ { "by": 50, "edge": "groq", "groq": 90, "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 4, "edge": "groq", "groq": 64, "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 8, "edge": "groq", "groq": 80, "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 18, "edge": "groq", "groq": 82, "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 20, "edge": "groq", "groq": 40, "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 26, "edge": "groq", "groq": 72, "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 32, "edge": "groq", "groq": 85, "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "GroqCloud scores 74.5 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "groq": "Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.", "muse-ember": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/groq-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/groq-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/groq-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/groq-vs-muse-ember.min.md" }, "markdown": "GroqCloud scores 74.5 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. Both do llm inference.\n\n- GroqCloud: grade BB, 74.5/100, rank #81 of 1515. Markdown https://www.anchorterminal.com/tools/groq.md · JSON https://www.anchorterminal.com/api/v1/tools/groq.json\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### GroqCloud (BB)\n\nGood for: Many small, latency-sensitive calls on open-weight models, routing, extraction and classification, and agents that need to start free.\n\nAhead on:\n- Reliability, 90 against 40\n- Agent ergonomics, 80 against 72\n- Security \u0026 auth, 82 against 64\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 72 against 46\n- Transparency \u0026 trust, 85 against 53\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Free to start without a card\n\nWatch for: Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice\n\n### Muse Ember (D)\n\nGood for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Reliability: GroqCloud +50\n\n- GroqCloud (90): Status page at groqstatus.com on incident.io, with components for the API, each production and preview model and the website (20). The page's RSS feed and the incident pages it links, read on 1 November, list three incidents in the 90-day window, all in October, and nothing since 12 October apart from a console maintenance on 18 October. On 10 October the enterprise-only preview MiniMax M2.7 had failed requests from data centre hardware issues from 10:09 to 16:14 UTC. The incident's header and impact record say degraded performance, while its Identified update set the model to partial outage and warned of failed requests. On 12 October console.groq.com returned 500 errors on metrics, /models and key creation from 03:17 to 03:38, and the vendor said inference requests were not affected. On 12 October MiniMax M2.7 had a partial outage from 18:17 to 18:36. None took down a core API for an hour, since the six-hour incident hit one enterprise preview model that the status page describes as for evaluation only, so minor incidents only (20). A stricter reading would count it as a major and give 10. Limits published per model, 30 requests a minute, 1,000 a day and 8,000 tokens a minute on gpt-oss for the free plan, re-read on 1 November (15). 429 carries `retry-after`, `x-ratelimit-*` headers come on every response, and the errors page says to wait before retrying a 503 and that a 502 may clear on retry (15). The Performance Tier, enterprise only, lists a 99.9% availability SLA with details in the offline agreement, per the 6 October check, not re-read on 1 November (10). gpt-oss and Whisper models are production, Qwen 3.8 27B, MiniMax M2.7 and Muse Spark Open are previews (10). 20+20+15+15+10+10 = 90.\n- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.\n\n### Transparency \u0026 trust: GroqCloud +32\n\n- GroqCloud (85): 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 services agreement bars training on inputs and outputs, and the DPA gives 15 days' notice of sub-processor changes to subscribers, both read on 6 October. The agreement extends zero retention to 'Eligible Customers' where the data page says all customers may enable it (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. The DPA points to a sub-processor list at trust.groq.com/subprocessors, which needs JavaScript and wasn't read (10 of 20).\n- Muse Ember (53): Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.\n\n### Maintenance \u0026 community: GroqCloud +26\n\n- GroqCloud (72): Model reading. Meta's Muse Spark Open went into limited preview on GroqCloud on 15 October 2026, per Groq's announcement, 17 days before this check, and the models page lists it as an enterprise model (30). The newest shutdown is still `groq/compound` and `compound-mini` on 21 September. The changelog's newest entry is 18 April 2026, the Python SDK 1.7.0 (tagged 25 August) and TypeScript SDK 1.6.0 are still the newest tags, with commits to 4 September and no release since, and the deprecations page has no announcement after 24 August. 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). Three shutdown dates in the 90 days to 1 November, 16 August, 14 September and 21 September. Three in 90 days is still frequent churn (0 of 8). A dated changelog with an RSS feed and a community forum exist, but the changelog's newest entry is 18 April 2026; support replies 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 published on 26 August 2026 and still the newest tags on 1 November (15). CI, release-please and lock-file vulnerability fixes in 1.7.0 (8 of 10). 30+4+0+10+5+15+8 = 72.\n- Muse Ember (46): Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.\n\n### Payments \u0026 pricing: GroqCloud +20\n\n- GroqCloud (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).\n- Muse Ember (20): Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.\n\n### Security \u0026 auth: GroqCloud +18\n\n- GroqCloud (82): Model reading. Bearer keys scoped to a project, with custom request limits per project and model permissions at organisation and project level. The security onboarding page advises rotating keys periodically and revoking them at once if compromise is suspected (30). The services agreement, read first-hand on 6 October and not re-read on 17 October, says Groq is not permitted to use inputs or outputs for training unless the customer grants permission (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, per 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 is a Vanta page that renders only with JavaScript, so certifications and any bug bounty are unchecked (5 of 20).\n- Muse Ember (64): Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.\n\n### Agent ergonomics: GroqCloud +8\n\n- GroqCloud (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).\n- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n\n## Score by category\n\n| Category | Weight | GroqCloud | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 90 | 40 | GroqCloud +50 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 64 | 60 | GroqCloud +4 |\n| Agent ergonomics | 13% (16.2 this run) | 80 | 72 | GroqCloud +8 |\n| Security \u0026 auth | 14% (17.5 this run) | 82 | 64 | GroqCloud +18 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 20 | GroqCloud +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 72 | 46 | GroqCloud +26 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 85 | 53 | GroqCloud +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **74.5 · BB** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | GroqCloud | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Groq | Meta |\n| Hosted endpoint | `https://api.groq.com/openai/v1` | `https://api.meta.ai/v1` |\n| Transports | HTTP | |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-15 | 2026-11-09 |\n| Terms last updated | 2026-06-22 | 2026-10-02 |\n| Privacy policy last updated | 2025-11-12 | couldn't be read |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 619 stars | none |\n| Agent reviews | 3.5/5 (8) | none |\n\n## How the scores have moved\n\n### GroqCloud\n\n- -1.1 on 17 October 2026: reliability, new evidence\n- -0.1 on 8 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\n\n**GroqCloud.** Free plan with no card, at 30 requests a minute and 1,000 a day on gpt-oss. Four model shutdown dates between 2026-07-17 and 2026-09-21, with no stated minimum notice.\n\n**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.\n\n## Before you call either\n\n### GroqCloud\n\n1. Call `/models` at start-up. Four model ids stopped working between July and September\n2. Read `retry-after` on a 429 and the `x-ratelimit-remaining-tokens` header before the next call\n3. Free plan allows 8,000 tokens a minute on gpt-oss, so keep prompts small or batch them\n4. Don't build on Qwen 3.8 27B. It's a preview and previews can go at short notice\n5. Treat a 498 as Flex capacity and retry later; server errors aren't billed\n\n### Muse Ember\n\n1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY\n2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing\n3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate\n4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises\n5. Use strict structured output for extraction and routing steps\n6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide\n\n## Questions\n\n### Which is better for AI agents, GroqCloud or Muse Ember?\n\nGroqCloud scores 74.5 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.\n\n### Do GroqCloud and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call GroqCloud and Muse Ember without installing anything?\n\nYes. GroqCloud has a hosted endpoint at https://api.groq.com/openai/v1 and Muse Ember at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/groq-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/groq-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"groq\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare groq muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/groq.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with GroqCloud or Muse Ember\n\n- [Amazon Bedrock vs GroqCloud](https://www.anchorterminal.com/compare/amazon-bedrock-vs-groq.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs GroqCloud](https://www.anchorterminal.com/compare/anthropic-api-vs-groq.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs GroqCloud](https://www.anchorterminal.com/compare/antseed-vs-groq.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs GroqCloud](https://www.anchorterminal.com/compare/azure-openai-vs-groq.md)\n- [Azure OpenAI in Foundry Models vs Muse 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Ember vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.md)\n- [Muse Ember vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.md)\n- [Muse Ember vs Together AI Inference](https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.md)\n- [Muse Ember vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Ember vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-09", 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