{ "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", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "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" ], "breakdown": [ { "key": "reliability", "name": "Reliability", "weight": 16, "effectiveWeight": 20, "score": 40, "points": 8, "reason": "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.", "checked": "2026-11-09" }, { "key": "performance", "name": "Performance", "weight": 10, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending. Latency is measured per call by our probes, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until the first probe window closes." }, { "key": "schema", "name": "Schema \u0026 documentation", "weight": 13, "effectiveWeight": 16.25, "score": 60, "points": 9.75, "reason": "Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.", "checked": "2026-11-09" }, { "key": "ergonomics", "name": "Agent ergonomics", "weight": 13, "effectiveWeight": 16.25, "score": 72, "points": 11.7, "reason": "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.", "checked": "2026-11-09" }, { "key": "security", "name": "Security \u0026 auth", "weight": 14, "effectiveWeight": 17.5, "score": 64, "points": 11.2, "reason": "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.", "checked": "2026-11-09" }, { "key": "payments", "name": "Payments \u0026 pricing", "weight": 10, "effectiveWeight": 12.5, "score": 20, "points": 2.5, "reason": "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.", "checked": "2026-11-09" }, { "key": "tasks", "name": "Task success", "weight": 10, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending. Task success needs the category task suites run through each tool, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until then. A data provider's data-quality score is published on its listing now and becomes half of this category when it's scored." }, { "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7, "effectiveWeight": 8.75, "score": 46, "points": 4.03, "reason": "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.", "checked": "2026-11-09" }, { "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7, "effectiveWeight": 8.75, "score": 53, "points": 4.64, "note": "editorial 41, provenance 64", "reason": "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.", "checked": "2026-11-09" } ], "assessment": { "date": "2026-11-09", "basis": "public evidence", "confidence": "medium", "notes": { "ergonomics": "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.", "maintenance": "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.", "payments": "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.", "reliability": "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.", "schema": "Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.", "security": "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.", "transparency": "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." }, "sources": [ { "what": "robots.txt, allows our reader", "url": "https://dev.meta.ai/robots.txt", "seen": "2026-11-09" }, { "what": "robots.txt, allows our reader", "url": "https://research.meta.ai/robots.txt", "seen": "2026-11-09" }, { "what": "announcement, 9 November 2026, positioning, benchmarks, cost per task, multimodality, context, training with Muse Wonder Full, safety evaluation", "url": "https://research.meta.ai/blog/muse-ember", "seen": "2026-11-09" }, { "what": "developer launch post, availability, price, Muse Code plans, worked cost examples, partner quotes", "url": "https://dev.meta.ai/resources/blog/build-with-muse-ember", "seen": "2026-11-09" }, { "what": "Muse Ember page, model ID, inputs, context, max output, effort, endpoints, uses, Standard mode only", "url": "https://dev.meta.ai/docs/muse-ember.md", "seen": "2026-11-09" }, { "what": "choose a model, comparison with Muse Spark and Wonder, workloads, Claude Code and Codex mapping, differences", "url": "https://dev.meta.ai/docs/choose-a-model.md", "seen": "2026-11-09" }, { "what": "models, hosted and open-weight lines", "url": "https://dev.meta.ai/docs/models.md", "seen": "2026-11-09" }, { "what": "pricing and rate limits, Muse Ember tiers, search grounding, per-model limits", "url": "https://dev.meta.ai/docs/pricing-rate-limits.md", "seen": "2026-11-09" }, { "what": "reasoning, effort levels and where to set them", "url": "https://dev.meta.ai/docs/reasoning.md", "seen": "2026-11-09" }, { "what": "docs index", "url": "https://dev.meta.ai/llms.txt", "seen": "2026-11-09" }, { "what": "Model Designation and Geographic Use Policy, last updated 12 October 2026, unchanged", "url": "https://dev.meta.ai/legal/geographic-use-policy", "seen": "2026-11-09" } ], "openQuestions": [ "Meta's launch thread says Muse Ember beats 'all other flash models' on intelligence per dollar. Its announcement compares only Claude Haiku 5.5 and GPT-6 Luna, so the flash claim isn't on a first-hand page and isn't on the listing", "Benchmarks and cost per task are Meta's, at max effort, several on internal benchmarks; artificialanalysis.ai wasn't requested, per the rules", "unchecked: parameter count and architecture, not published", "Whether weights will follow. Nothing on Meta's pages says so on 9 November", "Platform readings (status, keys, retries, retention, support, terms) are the meta-model-api listing's of 12 October to 4 November, not re-read" ] }, "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 }, "scoreHistory": [ { "date": "2026-11-09", "grade": "D", "kind": "listed", "method": "0.7", "score": 51.8 } ] }, "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" ], "notable": [ "Released on 9 November 2026 as Meta's smallest and most efficient model, and the first trained using Muse Wonder Full, per Meta's announcement (https://research.meta.ai/blog/muse-ember)", "Meta says it gets 'more intelligence per dollar than Claude Haiku 5.5 or GPT-6 Luna at any reasoning setting' and matches Haiku's best score at about a third of the cost per Artificial Analysis task, $0.136 against $0.411. All three list $0.10 input and $0.50 output per million tokens, so the gap is tokens used per task. Vendor-reported (https://research.meta.ai/blog/muse-ember)", "Meta's table puts it at 36 on the Artificial Analysis Intelligence Index, level with Claude Haiku 5.5 and ahead of GPT-6 Luna at 28, and behind Haiku on FrontierCode 2.0. Several rows are Meta's internal benchmarks (https://research.meta.ai/blog/muse-ember)", "Meta suggests mapping Claude Code's haiku alias and sub-agent model to muse-ember-1.0 (https://dev.meta.ai/docs/choose-a-model)", "Hosted only. Meta publishes no weights or parameter count for it, unlike Muse Spark Open and Muse Glimmer (https://dev.meta.ai/docs/models)" ], "area": "models", "details": [ { "label": "Surface graded", "value": "The model as Meta Model API serves it, read from Meta's announcement, the developer launch post, the Muse Ember, models, choose-a-model and pricing pages on 9 November 2026. Platform readings (status, keys, retries, terms, data use, support) are the meta-model-api listing's of 12 October to 4 November. No account was made and nothing was called" }, { "label": "Model", "value": "Meta's smallest and most efficient model, natively multimodal, the first trained using Muse Wonder Full. Meta doesn't publish its parameter count or architecture" }, { "label": "Where it runs", "value": "Meta Model API in Standard mode only, as muse-ember-1.0, on the Responses, Chat Completions and Messages endpoints, and in Muse Code on pay-as-you-go and every plan" }, { "label": "Context", "value": "1,048,576 tokens in, up to 524,288 out. Requests over 200K input tokens bill at the long-context rate for the whole request" }, { "label": "Pricing", "value": "Up to 200K input tokens, $0.10 input, $0.004 cached, $0.50 output per million. Over 200K, $0.30, $0.012 and $1.50. Muse Spark is $1.25 and $4.25, Muse Wonder $4.00 and $20.00. Claude Haiku 5.5 and GPT-6 Luna both list $0.10 and $0.50" }, { "label": "Meta's figures", "value": "Vendor-reported, at max reasoning. Artificial Analysis Intelligence Index 36 (Haiku 5.5 36, GPT-6 Luna 28), Coding Agent Index 46, Terminal-Bench 4.0 40.9%, AutomationBench 37.8%, FrontierCode 2.0 27.2% (Haiku 34.9%), OSWorld 2.1 69.5% partial credit. About $0.14 per Artificial Analysis task against $0.41 for Haiku. Agents' Last Exam, the Personal Superintelligence Index and PerceptionBench are internal" }, { "label": "Safety", "value": "Evaluated under Meta's Superintelligence Scaling Framework for chemical and biological, cyber and loss-of-control risk, below its high-risk thresholds per Meta, with agent tests for respecting intent, sensitive information, prompt injection and asking before high-impact actions" }, { "label": "Data use", "value": "Standard mode only, so content isn't used to train Meta models under the terms, per the meta-model-api listing's reading of 4 November" } ], "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, "checks": [ { "check": "Legal entity named", "value": "Meta Platforms, Inc.", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "meta.ai, registered 2017-12-16 (8 years)", "points": 11, "max": 15, "state": "part" }, { "check": "Endpoint on the vendor's domain", "value": "api.meta.ai", "points": 15, "max": 15, "state": "ok" }, { "check": "Terms of service", "value": "read, states 6 of the 7 things a reader expects, and has 2 clauses that cost points", "points": 5.1, "max": 10, "state": "part" }, { "check": "Privacy policy", "value": "published, but our reader couldn't read it", "points": 7, "max": 10, "state": "part" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "9 named with their role, 2 with a public profile", "points": 10, "max": 10, "state": "ok" }, { "check": "Support route", "value": "help pages, a community", "points": 5, "max": 5, "state": "ok" } ], "policies": [ { "kind": "terms", "url": "https://dev.meta.ai/legal/terms-of-service", "state": "read", "readAt": "2026-10-30", "statedDate": "2026-10-02", "words": 10228, "points": 5.1, "max": 10, "expected": [ { "key": "terms.date", "label": "Gives the date it was last updated", "found": true, "quote": "Last updated: 2 October 2026", "says": "Last updated 2026-10-02" }, { "key": "terms.law", "label": "Names the governing law or courts", "found": true, "quote": "…jurisdiction of either of these courts for the purpose of litigating any such Claim, and that the laws of the State of California will govern these Terms and any such Claim, without regard to conflict of law provisions.", "says": "The law of the State of California, with disputes in the courts of San Mateo County" }, { "key": "terms.liability", "label": "States a limit on its liability", "found": true, "quote": "…TO YOU AND YOUR AFFILIATES FOR ANY CLAIMS ARISING IN CONNECTION WITH THESE TERMS OR THE SERVICES WILL NOT EXCEED THE GREATER OF EITHER THE AMOUNTS PAID BY YOU TO META FOR USE OF THE SERVICES IN THE TWELVE (12) MONTH PERIOD PRECEDING THE CLAIM THAT GIVE RISE TO SUCH LIABILITY OR ONE THOUSAND DOLLARS (USD 1,000).", "says": "Capped at the greater of USD 1,000 and the fees paid in the 12 months before the claim" }, { "key": "terms.termination", "label": "Says how the agreement or account can be ended", "found": true, "quote": "If Meta terminates your Account, or prohibits your Account from accessing the Services, (i) these Terms will automatically terminate;" }, { "key": "terms.changes", "label": "Says how changes to the terms are announced", "found": true, "quote": "We will notify you in accordance with the law of the country in which your habitual residence is situated and at least 30 days before we make changes to these Terms and give you an opportunity to review them before they come into effect, unless the changes are required by law.", "says": "Gives 30 days of notice before a change" }, { "key": "terms.use", "label": "Lists what users may not do", "found": true, "quote": "you must require your End Users to comply with applicable laws, regulations and these Terms, and you will not knowingly enable them to violate any of the foregoing;" }, { "key": "terms.sla", "label": "Refers to a service level or uptime commitment", "found": false } ], "toKnow": [ { "key": "training", "label": "Says it may use customer content to train or improve models, and no opt-out was found", "found": true, "quote": "You agree that Meta may use your Content to train, develop, evaluate and improve Meta's artificial intelligence models, products and services.", "costsPoints": true }, { "key": "terms.benchmark", "label": "Restricts benchmarking or competitive use", "found": true, "quote": "For the avoidance of doubt, using Services or Outputs solely for evaluation, benchmarking or quality assessment of your own systems does not constitute a prohibited use under this Section;", "costsPoints": true }, { "key": "terms.cutoff", "label": "Says access 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