{ "data": { "a": { "slug": "claude-fable-5-2", "name": "Claude Fable 5.2", "vendor": "Anthropic", "vendorUrl": "https://www.anthropic.com", "kind": "model", "category": "inference", "summary": "Claude Fable 5.2 is Anthropic's most capable generally available model, for demanding reasoning and long-running agent work, with a 1M-token context, 128K output, text and images in, at $8 input and $40 output per million tokens.", "url": "https://www.anchorterminal.com/tools/claude-fable-5-2", "markdownUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json", "dns": "claude-fable-5-2.grade.anchorterminal.com", "license": "Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service", "transports": [], "remoteUrl": "https://api.anthropic.com/v1", "packages": [], "auth": "api-key", "authNotes": "A Claude API key from the Console, sent as `x-api-key` or a bearer token with `anthropic-version`, per the Claude API listing. Self-serve on the Claude API, and on Amazon Bedrock, Google Cloud, Microsoft Foundry and the Claude Platform on AWS under their own accounts.", "pricing": "usage", "pricingNotes": "$8 per million input tokens, $40 output, cache writes $10 (5 minutes) or $16 (1 hour), cache reads $0.20, batch at half price, fast mode $16 and $80. Requires 30-day retention. New users get a small amount of free credit, per the Claude API listing (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Anthropic's pricing page, which bills tokens to a Console account (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://platform.claude.com/docs/en/models/fable-5-2/overview", "llmsTxt": "https://platform.claude.com/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "disclosure": "Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.", "anchor": { "graded": true, "score": 77.3, "grade": "BB", "agentReady": true, "rank": 35, "ranked": true, "rankOf": 1517, "categoryRank": 7, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 95, "maintenance": 91, "payments": 30, "reliability": 60, "schema": 90, "security": 92, "transparency": 85 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.", "bestFor": "Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.", "disclosure": "Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.", "strengths": [ "$8 input and $40 output per million tokens, 20% below Fable 5.1, with cache reads at 2.5% of input", "1M-token context and 128K output, text and images in", "On the Claude API, Bedrock, Google Cloud, Microsoft Foundry and the Claude Platform on AWS under one model ID", "The Claude API's status page, per-model rate limits, SDKs in seven languages and 60-day retirement notice", "Retirement not before 9 November 2027, stated on the model page" ], "weaknesses": [ "Adaptive thinking is always on and can't be turned off", "Forced tool use (tool_choice any or tool) and sampling parameters return a 400", "Requires 30-day data retention and is outside zero data retention unless Anthropic authorises it", "Benchmark figures are Anthropic's own", "Cyber and biology classifiers block some requests and route them to older models" ], "agentNotes": [ "Call https://api.anthropic.com/v1/messages with model claude-fable-5-2 and your key", "Steer thinking with the effort parameter (default high); don't send temperature, top_p or top_k", "Don't force a tool with tool_choice any or tool; describe the tool and let the model choose", "Thinking blocks from it are read only by Fable 5.2 and Mythos 5.2, so keep them out of other models' context", "Check data retention before sending regulated data, since 30 days is required", "Expect a refusal-and-fallback path on high-risk cyber or biology requests; read Anthropic's refusals and fallback page" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 77.3 } ], "editorialScores": { "ergonomics": 95, "maintenance": 91, "payments": 30, "reliability": 60, "schema": 90, "security": 92, "transparency": 75 }, "provenanceScore": 95, "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 https://api.anthropic.com/v1/messages \\\n -H \"x-api-key: $ANTHROPIC_API_KEY\" -H \"anthropic-version: 2023-06-01\" \\\n -H \"content-type: application/json\" \\\n -d '{\"model\":\"claude-fable-5-2\",\"max_tokens\":1024,\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/claude-fable-5-2" }, "sameCompany": [ "claude-agent-sdk", "claude-code" ], "sameModel": [ "anthropic-api", "claude-mythos-5-2" ], "area": "models", "unitPrices": [ { "item": "Claude Fable 5.2 input", "unit": "1m-tokens", "usd": 8, "note": "Fast mode $16" }, { "item": "Claude Fable 5.2 cached input", "unit": "1m-tokens", "usd": 0.2, "note": "0.025x input; 5-minute write $10, 1-hour write $16" }, { "item": "Claude Fable 5.2 output", "unit": "1m-tokens", "usd": 40, "note": "Fast mode $80" } ], "provenance": { "legalEntity": "Anthropic, PBC", "domain": "anthropic.com", "domainRegistered": "2001-10-02", "domainNote": "The API is on anthropic.com. anthropic.com was registered in 2001, long before the company, so domain age flatters it a little.", "endpointOnVendorDomain": true, "terms": "https://www.anthropic.com/legal/commercial-terms", "privacy": "https://www.anthropic.com/legal/privacy", "statusPage": "https://status.claude.com", "changelog": "https://platform.claude.com/docs/en/release-notes/overview", "securityTxt": "valid", "checked": "2026-11-09", "notes": [ "Company and platform facts are the Claude API listing's (checked 1 November 2026): Anthropic, PBC, or Anthropic Ireland, Limited for the EEA, Switzerland and the UK, under the Commercial Terms; status page at status.claude.com; dated release notes; security.txt valid to 2026-12-31.", "www.anthropic.com's and platform.claude.com's robots.txt allow our reader on the pages read on 9 November (platform.claude.com disallows only /api/). domainRegistered is carried from the Claude API listing's RDAP reading." ], "score": 95 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/claude-fable-5-2.json", "live": { "slug": "claude-fable-5-2", "probe": { "target": "https://api.anthropic.com/v1", "method": "get", "lastAt": "2026-11-10T17:29:33.207482375Z", "lastOk": true, "lastStatus": 404, "lastMs": 148, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 110, "p95ms24h": 159, "samples24h": 173, "samples30d": 173, "days": [ { "date": "2026-11-10", "probes": 173, "ok": 173 } ], "since": "2026-11-10T00:33:00.716203063Z" }, "vendorStatus": { "page": "https://status.claude.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-10T17:32:54.222765815Z" }, "securityTxt": { "url": "https://anthropic.com/.well-known/security.txt", "state": "valid", "expires": "2026-12-31T23:59:00.000Z", "checkedAt": "2026-11-10T16:07:43.984717176Z" }, "llmsTxt": { "url": "https://platform.claude.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:42:25.283223688Z" }, "updatedAt": "2026-11-10T17:32:54.222765815Z" }, "streak": { "days": 0, "from": "2026-11-10", "sinceWatching": true, "best": 0 } }, "answer": "Claude Fable 5.2 scores 77.3 (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": 1019, "ranked": true, "rankOf": 1517, "categoryRank": 38, "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": "2026-11-10T17:30:00.504052506Z", "lastOk": true, "lastStatus": 404, "lastMs": 89, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 90, "samples24h": 219, "samples30d": 219, "days": [ { "date": "2026-11-09", "probes": 41, "ok": 41 }, { "date": "2026-11-10", "probes": 178, "ok": 178 } ], "since": "2026-11-09T19:55:51.172718582Z" }, "securityTxt": { "url": "https://meta.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:08:58.767810687Z" }, "llmsTxt": { "url": "https://dev.meta.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:43:52.864517103Z" }, "updatedAt": "2026-11-10T17:30:00.504052506Z" }, "streak": { "days": 1, "from": "2026-11-09", "sinceWatching": true, "best": 1 } }, "edges": [ { "by": 45, "category": "maintenance", "leads": "claude-fable-5-2", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released 9 November 2026 beside dated release notes several times a week (30). 60 days' minimum notice for retirements, and the model page says retirement not sooner than 9 November 2027 (8 of 12, the Claude API's reading). Model shutdowns kept to notice (8). Support centre and dated notes (15). The sampled SDK issue without a reply, per the 1 October check (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 8 + 8 + 15 + 5 + 15 + 10 = 91.", "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": 91, "b": 46 } }, { "by": 32, "category": "transparency", "leads": "claude-fable-5-2", "name": "Transparency \u0026 trust", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Commercial Terms with named contracting parties and Apache 2.0 SDKs (15). Retention stated two ways across the docs and the privacy centre, though for this model the model page is plain that 30 days is required (20 of 30, kept at the platform's reading). Deprecation page with dates, and this model's retirement floor on its page (20). Processing countries and subprocessors named (20). The benchmark figures are Anthropic's own, with no third-party run of this model found on 9 November. 15 + 20 + 20 + 20 = 75.", "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": 30, "category": "schema", "leads": "claude-fable-5-2", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. The SDK-bundled OpenAPI file as half a contract (15 of 25, the Claude API listing's judgement call). llms.txt with Markdown twins (10). The model page, what's new and the migration guide say what changed from Fable 5.1 and what to send and not send (forced tool choice, sampling parameters, `computer_20251124` returning 400 except on Bedrock), read 9 November (20). Structured outputs and strict tools GA (15). Documented error types with `request_id` (15). `anthropic-version` and dated release notes (15). 15 + 10 + 20 + 15 + 15 + 15 = 90.", "b": "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." }, "scores": { "a": 90, "b": 60 } }, { "by": 28, "category": "security", "leads": "claude-fable-5-2", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Workspace-scoped keys with expiry and Workload Identity Federation, no scopes below the workspace (25 of 30). No training on API data without express permission (20). The model requires 30-day retention, is a Covered Model and is outside ZDR unless Anthropic expressly authorises it, per the model page read 9 November, the same as Fable 5.1 (12 of 15). Usage and cost analytics, Compliance API and OpenTelemetry export (15). security.txt, HackerOne and SOC 2, ISO 27001 and ISO 42001 (20). Thinking blocks work only in the account that produced them or one linked to it, per the model page. 25 + 20 + 12 + 15 + 20 = 92.", "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": 92, "b": 64 } }, { "by": 23, "category": "ergonomics", "leads": "claude-fable-5-2", "name": "Agent ergonomics", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Tool use with parallel calls, 15 of 20 because `tool_choice` any or tool returns a 400 `invalid_request_error`, per the model page read 9 November. Structured outputs (15). Prompt caching with reads at $0.20, 0.025x input, per the pricing page read 9 November (15). 1M context by default and at most, 128K output (15). Batch at half price (10). Official SDKs in seven languages (10). Documented errors, `retry-after` and retries (15). 15 + 15 + 15 + 15 + 10 + 10 + 15 = 95.", "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": 95, "b": 72 } }, { "by": 20, "category": "reliability", "leads": "claude-fable-5-2", "name": "Reliability", "reasons": { "a": "Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at status.claude.com with component history (20). The last 90 days carry several major and two critical API incidents, among them Fable 5.1 and Mythos 5.1 on 3 September and Fable 5.1 on 23 and 25 October, so 0 for incident history. Rate limits published per model, Fable 5.x at 4,000 requests a minute at the Start tier (15). 429s with `retry-after` and SDK retries with backoff (15). No SLA on sale (0). The model page lists it as a current model with no preview label (10). 20 + 0 + 15 + 15 + 0 + 10 = 60. Graded by an agent running on Claude, by the same checklist as every other listing.", "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": 60, "b": 40 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Anthropic", "b": "Meta", "name": "Vendor" }, { "a": "https://api.anthropic.com/v1", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service", "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": "2026-11-09", "b": "2026-11-09", "name": "Last release" }, { "a": "no date given", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "no date given", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "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": "yes", "b": "yes", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.", "question": "Which is better for AI agents, Claude Fable 5.2 or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do Claude Fable 5.2 and Muse Ember need an API key?" }, { "answer": "Yes. Claude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/v1 and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call Claude Fable 5.2 and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 60 against 40", "Schema \u0026 documentation, 90 against 60", "Agent ergonomics, 95 against 72", "Security \u0026 auth, 92 against 64", "Payments \u0026 pricing, 30 against 20", "Maintenance \u0026 community, 91 against 46", "Transparency \u0026 trust, 85 against 53" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.", "slug": "claude-fable-5-2", "watchFor": "Adaptive thinking is always on and can't be turned off" }, { "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": null, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-claude-fable-5-2.json", "title": "Amazon Bedrock vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-claude-fable-5-2" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-claude-fable-5-2.json", "title": "Claude API vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-claude-fable-5-2" }, { 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"https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.json", "title": "Muse Ember vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.json", "title": "Muse Ember vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "title": "Muse Ember vs OpenAI API", "url": "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": 20, "claude-fable-5-2": 60, "edge": "claude-fable-5-2", "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 30, "claude-fable-5-2": 90, "edge": "claude-fable-5-2", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 23, "claude-fable-5-2": 95, "edge": "claude-fable-5-2", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 28, "claude-fable-5-2": 92, "edge": "claude-fable-5-2", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "claude-fable-5-2": 30, "edge": "claude-fable-5-2", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 45, "claude-fable-5-2": 91, "edge": "claude-fable-5-2", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 32, "claude-fable-5-2": 85, "edge": "claude-fable-5-2", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Claude Fable 5.2 scores 77.3 (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": { "claude-fable-5-2": "Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.", "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/claude-fable-5-2-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.min.md" }, "markdown": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. Both do llm inference.\n\n- Claude Fable 5.2: grade BB, 77.3/100, rank #35 of 1517. Markdown https://www.anchorterminal.com/tools/claude-fable-5-2.md · JSON https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json\n- Muse Ember: grade D, 51.8/100, rank #1019 of 1517. 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 956 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Claude Fable 5.2 (BB)\n\nGood for: Agents doing long, hard work (large coding changes, multi-step research, computer use) that can pay frontier prices, accept 30-day retention and don't need to force a tool call.\n\nAhead on:\n- Reliability, 60 against 40\n- Schema \u0026 documentation, 90 against 60\n- Agent ergonomics, 95 against 72\n- Security \u0026 auth, 92 against 64\n- Payments \u0026 pricing, 30 against 20\n- Maintenance \u0026 community, 91 against 46\n- Transparency \u0026 trust, 85 against 53\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: Adaptive thinking is always on and can't be turned off\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### Maintenance \u0026 community: Claude Fable 5.2 +45\n\n- Claude Fable 5.2 (91): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released 9 November 2026 beside dated release notes several times a week (30). 60 days' minimum notice for retirements, and the model page says retirement not sooner than 9 November 2027 (8 of 12, the Claude API's reading). Model shutdowns kept to notice (8). Support centre and dated notes (15). The sampled SDK issue without a reply, per the 1 October check (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 8 + 8 + 15 + 5 + 15 + 10 = 91.\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### Transparency \u0026 trust: Claude Fable 5.2 +32\n\n- Claude Fable 5.2 (85): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Commercial Terms with named contracting parties and Apache 2.0 SDKs (15). Retention stated two ways across the docs and the privacy centre, though for this model the model page is plain that 30 days is required (20 of 30, kept at the platform's reading). Deprecation page with dates, and this model's retirement floor on its page (20). Processing countries and subprocessors named (20). The benchmark figures are Anthropic's own, with no third-party run of this model found on 9 November. 15 + 20 + 20 + 20 = 75.\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### Schema \u0026 documentation: Claude Fable 5.2 +30\n\n- Claude Fable 5.2 (90): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. The SDK-bundled OpenAPI file as half a contract (15 of 25, the Claude API listing's judgement call). llms.txt with Markdown twins (10). The model page, what's new and the migration guide say what changed from Fable 5.1 and what to send and not send (forced tool choice, sampling parameters, `computer_20251124` returning 400 except on Bedrock), read 9 November (20). Structured outputs and strict tools GA (15). Documented error types with `request_id` (15). `anthropic-version` and dated release notes (15). 15 + 10 + 20 + 15 + 15 + 15 = 90.\n- Muse Ember (60): 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.\n\n### Security \u0026 auth: Claude Fable 5.2 +28\n\n- Claude Fable 5.2 (92): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Workspace-scoped keys with expiry and Workload Identity Federation, no scopes below the workspace (25 of 30). No training on API data without express permission (20). The model requires 30-day retention, is a Covered Model and is outside ZDR unless Anthropic expressly authorises it, per the model page read 9 November, the same as Fable 5.1 (12 of 15). Usage and cost analytics, Compliance API and OpenTelemetry export (15). security.txt, HackerOne and SOC 2, ISO 27001 and ISO 42001 (20). Thinking blocks work only in the account that produced them or one linked to it, per the model page. 25 + 20 + 12 + 15 + 20 = 92.\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: Claude Fable 5.2 +23\n\n- Claude Fable 5.2 (95): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Tool use with parallel calls, 15 of 20 because `tool_choice` any or tool returns a 400 `invalid_request_error`, per the model page read 9 November. Structured outputs (15). Prompt caching with reads at $0.20, 0.025x input, per the pricing page read 9 November (15). 1M context by default and at most, 128K output (15). Batch at half price (10). Official SDKs in seven languages (10). Documented errors, `retry-after` and retries (15). 15 + 15 + 15 + 15 + 10 + 10 + 15 = 95.\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### Reliability: Claude Fable 5.2 +20\n\n- Claude Fable 5.2 (60): Model reading for claude-fable-5-2 on the Claude API. Platform facts are the Claude API listing's (anthropic-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at status.claude.com with component history (20). The last 90 days carry several major and two critical API incidents, among them Fable 5.1 and Mythos 5.1 on 3 September and Fable 5.1 on 23 and 25 October, so 0 for incident history. Rate limits published per model, Fable 5.x at 4,000 requests a minute at the Start tier (15). 429s with `retry-after` and SDK retries with backoff (15). No SLA on sale (0). The model page lists it as a current model with no preview label (10). 20 + 0 + 15 + 15 + 0 + 10 = 60. Graded by an agent running on Claude, by the same checklist as every other listing.\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## Score by category\n\n| Category | Weight | Claude Fable 5.2 | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 60 | 40 | Claude Fable 5.2 +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 90 | 60 | Claude Fable 5.2 +30 |\n| Agent ergonomics | 13% (16.2 this run) | 95 | 72 | Claude Fable 5.2 +23 |\n| Security \u0026 auth | 14% (17.5 this run) | 92 | 64 | Claude Fable 5.2 +28 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 20 | Claude Fable 5.2 +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 91 | 46 | Claude Fable 5.2 +45 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 85 | 53 | Claude Fable 5.2 +32 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **77.3 · BB** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | Claude Fable 5.2 | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Anthropic | Meta |\n| Hosted endpoint | `https://api.anthropic.com/v1` | `https://api.meta.ai/v1` |\n| Transports | | |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service | 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 | 2026-11-09 | 2026-11-09 |\n| Terms last updated | no date given | 2026-10-02 |\n| Privacy policy last updated | no date given | couldn't be read |\n| Customer content may train models | yes, with an opt-out | 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 | yes | yes |\n\n## Verdicts\n\n**Claude Fable 5.2.** Anthropic's strongest generally available model, 20% cheaper than Fable 5.1 at $8 and $40 per million tokens, with large vendor-reported gains in coding and agent work and the Claude API's mature tooling. Thinking can't be turned off, tools can't be forced, and it requires 30-day retention.\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### Claude Fable 5.2\n\n1. Call https://api.anthropic.com/v1/messages with model claude-fable-5-2 and your key\n2. Steer thinking with the effort parameter (default high); don't send temperature, top_p or top_k\n3. Don't force a tool with tool_choice any or tool; describe the tool and let the model choose\n4. Thinking blocks from it are read only by Fable 5.2 and Mythos 5.2, so keep them out of other models' context\n5. Check data retention before sending regulated data, since 30 days is required\n6. Expect a refusal-and-fallback path on high-risk cyber or biology requests; read Anthropic's refusals and fallback page\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, Claude Fable 5.2 or Muse Ember?\n\nClaude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.\n\n### Do Claude Fable 5.2 and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call Claude Fable 5.2 and Muse Ember without installing anything?\n\nYes. Claude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/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/claude-fable-5-2-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"claude-fable-5-2\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare claude-fable-5-2 muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/claude-fable-5-2.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with Claude Fable 5.2 or Muse Ember\n\n- [Amazon Bedrock vs Claude Fable 5.2](https://www.anchorterminal.com/compare/amazon-bedrock-vs-claude-fable-5-2.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs Claude Fable 5.2](https://www.anchorterminal.com/compare/anthropic-api-vs-claude-fable-5-2.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs Claude Fable 5.2](https://www.anchorterminal.com/compare/antseed-vs-claude-fable-5-2.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs Claude Fable 5.2](https://www.anchorterminal.com/compare/azure-openai-vs-claude-fable-5-2.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- [BlockRun.AI vs Claude Fable 5.2](https://www.anchorterminal.com/compare/blockrun-ai-vs-claude-fable-5-2.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [Cerebras Inference vs Claude Fable 5.2](https://www.anchorterminal.com/compare/cerebras-inference-vs-claude-fable-5-2.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Claude Fable 5.2 vs Claude Mythos 5.2](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-claude-mythos-5-2.md)\n- [Claude Fable 5.2 vs Cloudflare AI Gateway](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-ai-gateway.md)\n- [Claude Fable 5.2 vs Cloudflare Workers AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-workers-ai.md)\n- [Claude Fable 5.2 vs Cohere Chat API (Command models)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cohere-chat.md)\n- [Claude Fable 5.2 vs DeepInfra](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepinfra.md)\n- [Claude Fable 5.2 vs DeepSeek API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepseek-api.md)\n- [Claude Fable 5.2 vs Eden AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-eden-ai.md)\n- [Claude Fable 5.2 vs Glimpse](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-elorian-glimpse.md)\n- [Claude Fable 5.2 vs Fireworks AI Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-fireworks-inference.md)\n- [Claude Fable 5.2 vs Gemini Developer API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-api.md)\n- [Claude Fable 5.2 vs GroqCloud](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-groq.md)\n- [Claude Fable 5.2 vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hugging-face-inference-providers.md)\n- [Claude Fable 5.2 vs Inkling 2 Small](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-inkling-2-small.md)\n- [Claude Fable 5.2 vs Jais 3](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.md)\n- [Claude Fable 5.2 vs Meta Model API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-meta-model-api.md)\n- [Claude Fable 5.2 vs MiniMax API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-minimax-api.md)\n- [Claude Fable 5.2 vs Mistral AI API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-mistral-api.md)\n- [Claude Fable 5.2 vs Muse Spark Open](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.md)\n- [Claude Fable 5.2 vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-nemotron-3-5-super-vl.md)\n- [Claude Fable 5.2 vs Novita AI](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-novita-ai.md)\n- [Claude Fable 5.2 vs OpenAI API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openai-api.md)\n- [Claude Fable 5.2 vs OpenRouter](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openrouter.md)\n- [Claude Fable 5.2 vs Prism Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-prism-inference.md)\n- [Claude Fable 5.2 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-ramp-router.md)\n- [Claude Fable 5.2 vs Beam 1.0](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-reflection-beam.md)\n- [Claude Fable 5.2 vs RouterPlus](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-routerplus.md)\n- [Claude Fable 5.2 vs SambaCloud](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-sambanova.md)\n- [Claude Fable 5.2 vs SiliconFlow](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-siliconflow.md)\n- [Claude Fable 5.2 vs Simplismart](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-simplismart.md)\n- [Claude Fable 5.2 vs StepFun API](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-stepfun-api.md)\n- [Claude Fable 5.2 vs SWE-2 Flash](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-swe-2-flash.md)\n- [Claude Fable 5.2 vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-tencent-tokenhub.md)\n- [Claude Fable 5.2 vs Together AI Inference](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-together-inference.md)\n- [Claude Fable 5.2 vs Vercel AI Gateway](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vercel-ai-gateway.md)\n- [Claude Fable 5.2 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vertex-ai.md)\n- [Claude Fable 5.2 vs xAI API (Grok)](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-xai-api.md)\n- [Claude Mythos 5.2 vs Muse Ember](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-ember.md)\n- [Cloudflare AI Gateway vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.md)\n- [Cloudflare Workers AI vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.md)\n- [Cohere Chat API (Command models) vs Muse Ember](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.md)\n- [DeepInfra vs Muse Ember](https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md)\n- [DeepSeek API vs Muse Ember](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.md)\n- [Eden AI vs Muse Ember](https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.md)\n- [Glimpse vs Muse Ember](https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.md)\n- [Fireworks AI Inference vs Muse Ember](https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.md)\n- [Gemini Developer API vs Muse Ember](https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.md)\n- [GroqCloud vs Muse Ember](https://www.anchorterminal.com/compare/groq-vs-muse-ember.md)\n- [Hugging Face Inference Providers vs Muse Ember](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.md)\n- [Inkling 2 Small vs Muse Ember](https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Meta Model API vs Muse Ember](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Ember vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.md)\n- [Muse Ember vs Novita AI](https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.md)\n- [Muse Ember vs OpenAI API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism Inference](https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.md)\n- [Muse Ember vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.md)\n- [Muse Ember vs Beam 1.0](https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md)\n- [Muse Ember vs RouterPlus](https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.md)\n- [Muse Ember vs SambaCloud](https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md)\n- [Muse Ember vs SiliconFlow](https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.md)\n- [Muse Ember vs Simplismart](https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md)\n- [Muse Ember vs StepFun API](https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.md)\n- [Muse 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\n## Disclosure\n\n- Anthropic makes the models this research run and the review panel run on. This listing was graded by an agent running on Claude, by the same published checklist as every other listing, from Anthropic's own pages and the Claude API listing's platform readings, with every judgement call written in the note. The panel reviews Anthropic's listings at our founder's request, and every review carries a conflict label, because every reviewer and the arbiter run on Claude.\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": "2026-11-10", "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" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Claude Fable 5.2 vs Muse Ember", "url": "" } ], "description": "Claude Fable 5.2 scores 77.3 (BB) to Muse Ember's 51.8 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Claude Fable 5.2 BB 77.3", "Muse Ember D 51.8", "scores" ], "h1": "Claude Fable 5.2 vs Muse Ember", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/claude-fable-5-2-vs-muse-ember", "published": "2026-10-01", "section": "tools", "title": "Claude Fable 5.2 vs Muse Ember for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-11-09", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember" }, "tokens": { "markdown": 6400, "slim": 930 }, "version": 1 }