{ "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 Spark Open's 53.4 (D), and leads in 6 of 7 scored categories. Muse Spark Open leads on payments \u0026 pricing.", "b": { "slug": "muse-spark-open", "name": "Muse Spark Open", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai/models/muse-spark-open", "kind": "model", "category": "inference", "summary": "Muse Spark Open is Meta's open-weight multimodal model for coding and agentic work, with 816B total and 42B active parameters and a 1M-token context, released on 15 October 2026 under the Muse Model Licence for self-hosting.", "url": "https://www.anchorterminal.com/tools/muse-spark-open", "markdownUrl": "https://www.anchorterminal.com/tools/muse-spark-open.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-spark-open.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json", "dns": "muse-spark-open.grade.anchorterminal.com", "repo": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B", "license": "Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "Running the model needs no account or key. Getting Meta's weights does. Both Hugging Face repositories (BF16 and NVFP4) are gated with manual review and ask for full legal name, date of birth, country, affiliation and job title, and downloads then use a Hugging Face access token. Meta's Get the model page also gives a download from its own site after signing in and accepting the licence, one file at a time. The GGUF quantisations from ggml-org and Unsloth that Meta's docs point to are not gated. A local server has whatever authentication the owner adds, such as llama.cpp's `--api-key`.", "pricing": "free", "pricingNotes": "Free to download and run under the Muse Model Licence, with the owner's GPUs as the cost. A licensee whose group has more than $20M gross revenue in any 12 months must notify Meta and sign a separate agreement before offering it as Model as a Service. Hosted copies cost $1.25 input, $0.15 cached input and $4.25 output per million tokens on Fireworks, Baseten and Modal (checked 2026-10-15). On 4 November Hugging Face lists Baseten and Together as live inference providers, Baseten at $4.25 output. Meta Model API does not serve it.", "priceSummary": "from $1.25 / 1M in", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Muse Spark Open is a model the owner runs, with no payment route from Meta, and Meta Model API does not serve it (checked 2026-10-15).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://dev.meta.ai/docs/muse-spark-open", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm", "inference.open-weights", "inference.local" ], "tags": [ "model", "open-weights", "self-hosted", "local", "free", "gated", "multimodal", "openai-compatible", "llms-txt", "gpu", "new" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 53.4, "grade": "D", "agentReady": false, "rank": 954, "ranked": true, "rankOf": 1517, "categoryRank": 34, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 60, "payments": 50, "reliability": 42, "schema": 61, "security": 44, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.", "bestFor": "A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.", "strengths": [ "816B total and 42B active parameters with a 1,048,576-token context, taking text, image, video and audio input", "Day-one support in vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, MLX and Unsloth, with commands for each in Meta's docs", "With the `muse` parsers, reasoning comes back in its own field and tool calls as a standard `tool_calls` array", "The licence grants use, fine-tuning, distillation and resale free of charge, with no user cap and no regional exclusion", "llms.txt and Markdown docs cover checkpoints, prompting, stop tokens, recommended settings and post-training" ], "weaknesses": [ "The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight", "Meta's Hugging Face repositories need manual approval with legal name and date of birth, which the Get the model page doesn't mention", "Products must display \"Built with Muse Spark Open\", and a company group with over $20M revenue needs Meta's agreement to offer it as Model as a Service", "The Usage Policy rules out military, nuclear, espionage, critical infrastructure and heavy machinery uses, and users under 18", "No changelog, deprecation policy or CI found, and none of the Hugging Face discussion replies counted on 4 November came from Meta" ], "agentNotes": [ "Start vLLM or SGLang with `--reasoning-parser muse --tool-call-parser muse` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as raw text", "Set `reasoning_effort` to `low` or `medium` for short tasks. The default is `xhigh`, reasoning can't be turned off, and other values return an error", "Stop only on `\u003c|eot|\u003e` and end of text. Stopping on `\u003c|eom|\u003e` cuts off tool calls and final answers", "Stream long generations and leave `max_tokens` room for reasoning, or `content` can come back empty with `finish_reason` `length`", "Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as `meta-models/muse-spark-open`" ], "metrics": { "kind": "local", "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": 53.4 } ], "editorialScores": { "ergonomics": 69, "maintenance": 60, "payments": 50, "reliability": 42, "schema": 61, "security": 44, "transparency": 50 }, "provenanceScore": 55, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-10-30", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "hf download meta-models/Muse-Spark-Open-816B-A42B-NVFP4 --local-dir ./muse-spark-open-nvfp4\n\nexport VLLM_USE_RUST_FRONTEND=1\nvllm serve meta-models/Muse-Spark-Open-816B-A42B-NVFP4 \\\n --tensor-parallel-size 4 \\\n --enable-expert-parallel \\\n --max-model-len 1048576 \\\n --reasoning-parser muse \\\n --tool-call-parser muse \\\n --enable-auto-tool-choice", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"meta-models/Muse-Spark-Open-816B-A42B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Refactor this module.\"}],\n \"reasoning_effort\": \"xhigh\"\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-spark-open" }, "sameCompany": [ "meta-model-api", "muse-ember", "muse-code", "muse-glimmer-30b" ], "family": { "lab": "Meta", "family": "Muse", "url": "https://www.anchorterminal.com/open-weights/#meta", "others": [ { "name": "Muse Glimmer 30B", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B", "params": "30B (dense, multimodal)", "listing": "https://www.anchorterminal.com/tools/muse-glimmer-30b" }, { "name": "Muse Glimmer 30B assistant (DFlash drafter)", "url": "https://huggingface.co/meta-models/Muse-Glimmer-30B-assistant", "params": "2.56B (Hub safetensors count; the card states no figure)" } ], "olderCount": 50, "unrestrictedCopies": 1 }, "area": "models", "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-04", "notes": [ "The licence names Meta Platforms, Inc. as copyright holder, and defines Meta as Meta Platforms Ireland Limited for licensees in the European region and Meta Platforms, Inc. elsewhere. The model card names the same two as model provider.", "The licence is the document that governs use of the weights, so it is recorded as the terms. LICENSE.md matches the text shown on the gated access form word for word. The Usage Policy is its Exhibit A. The separate USE_POLICY.md that Meta's docs link answered 401 without access and wasn't read.", "No privacy policy governs the model, because the owner runs it and no input reaches Meta. The access form says its details are handled under the Meta Privacy Policy on www.facebook.com, whose robots.txt asks readers like ours not to fetch it, so it wasn't read.", "The domain is meta.ai, where Meta's developer docs (dev.meta.ai) and research post (research.meta.ai) sit, the same as the meta-model-api and muse-code listings. RDAP gives a registration date of 16 December 2017.", "No hosted endpoint from Meta, so no status page. No changelog for the model was found, and the Hugging Face commit history needs access to read.", "dev.meta.ai/.well-known/security.txt and research.meta.ai/.well-known/security.txt returned 404 on 15 October 2026.", "WebFetch read the research post. It refused the Hugging Face model card as outside its allowed set, so the card, LICENSE.md, the repository and collection metadata, the discussions, the evaluation methodology PDF, Meta's docs pages and the providers' model pages were read with curl.", "Re-read on 4 November 2026 from the Hugging Face model pages, which show both Meta repositories gated with manual review and last changed on 15 October 2026. Meta's Get the model page now also lists a signed-in download from dev.meta.ai." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-spark-open.json", "live": { "slug": "muse-spark-open", "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": "2026-11-10T15:43:56.741326494Z" }, "domain": { "domain": "meta.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/meta.ai", "checkedAt": "2026-11-08T14:51:01.706547979Z" }, "pages": [ { "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md", "kind": "terms", "status": 200, "checkedAt": "2026-11-10T16:23:06.756199061Z", "changedAt": "2026-11-10T16:23:06.756199061Z", "fingerprint": "5d05f8810a6b" } ], "models": { "meta/muse-spark-open": { "openrouterId": "meta/muse-spark-open", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 131072, "checkedAt": "2026-11-09T23:49:27.454762861Z" } }, "updatedAt": "2026-11-10T16:23:06.756199061Z" } }, "edges": [ { "by": 48, "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": "Read as software the owner runs. No account or key to run it. Meta's weights need manual approval on Hugging Face and an access token, and server authentication is the owner's to add (12 of 30). An agent model that calls whatever tools the operator gives it. The card recommends human-in-the-loop confirmation for irreversible actions and Meta reports agentic-risk evaluations, but nothing in the model or template enforces an approval (8 of 20). The chat template neutralises special-token sequences and media placeholders in caller text and wraps tool results in `\u003ctool_output\u003e`, and the card advises protecting internal assistant messages and layering defences against adversarial input. No indirect prompt-injection results are published (10 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Weights are safetensors that load through Transformers' own classes from 5.19, and Meta published cyber, chemical, biological and misalignment evaluations. dev.meta.ai/.well-known/security.txt answers 404 (re-read on 4 November), research.meta.ai per the 15 October check, and no security route is on the card or in the docs, only museoss@meta.com and the Hugging Face discussions (9 of 20). 12 + 8 + 10 + 5 + 9 = 44." }, "scores": { "a": 92, "b": 44 } }, { "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": "Weights are downloadable under the Muse Model Licence, which is clear and broad in its grant but not an OSI licence. It adds a display requirement, a separate agreement before Model as a Service by a licensee group above $20M gross revenue, and a Usage Policy with field-of-use limits (military, nuclear, espionage, critical infrastructure, heavy machinery) and an age limit of 18. It has no regional exclusion and no governing-law clause. Training code and data aren't released (18 of 30). Inputs stay on the owner's hardware, and Meta's docs say self-hosting keeps 'every prompt and response' inside the owner's infrastructure. The access form's details, and a download from Meta's own page after signing in, go to Meta under its general privacy policy, which we didn't read, and the training data is described in outline only (20 of 30). No deprecation policy, dated notices or versioning found (0 of 20). The Get the model page says everything runs offline once the weights land, and safetensors weights carry no code. No other telemetry statement, and Meta's download form records name, date of birth, country and IP location (12 of 20). 18 + 20 + 0 + 12 = 50." }, "scores": { "a": 85, "b": 53 } }, { "by": 31, "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": "Read for an open-weight model released three weeks ago. Released on 15 October 2026 (30). One release, BF16 and NVFP4 on the same day, with no history of revisions, successors or notice periods, and Meta Model API has no deprecation policy either (6 of 20). None of the 29 replies counted by script on 4 November across the BF16 repository's discussions came from Meta. Feedback goes to the discussions or museoss@meta.com, and Meta's docs and developer blog carry dated posts (8 of 25). Day-one support in vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, MLX and Unsloth with Meta's own recipes, though SGLang's is a dev image and TensorRT-LLM's a feature branch (12 of 15). Current dependencies (Transformers 5.19), but no CI visible to us (4 of 10). 30 + 6 + 8 + 12 + 4 = 60." }, "scores": { "a": 91, "b": 60 } }, { "by": 29, "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": "Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which renders OpenAI-format tools with `additionalProperties: false` added and rejects bad values, plus the OpenAI-compatible servers in vLLM and SGLang with the `muse` parsers (10 of 25). dev.meta.ai/llms.txt lists the Muse Spark Open overview and seven pages under it, plus three cookbook pages, each with a Markdown copy (10). The card lists seven intended uses and six limitations, the docs say when to pick BF16 or NVFP4 and which reasoning strength suits which task, and the Usage Policy states what is out of scope (17 of 20). `reasoning_effort` is an enum of four values and anything else returns an error, and tools take JSON Schema. Structured output is documented only for TensorRT-LLM's `response_format`, not in the prompting guide or for vLLM and SGLang (11 of 15). curl and Python examples for every runtime and hosted provider, the rendered prompt for each case, and the template's own error messages, but no error reference for a served model (11 of 15). One release, with no changelog or version scheme beyond the licence's version line, Muse Spark Open (2 of 15). 10 + 10 + 17 + 11 + 11 + 2 = 61." }, "scores": { "a": 90, "b": 61 } }, { "by": 26, "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 of the checklist (tool use, structured output, caching, context, throughput, defaults and SDKs, errors). OpenAI-format tools and parallel calls come back as `tool_calls`, and the template supports namespaced tools and deferred loading through a `tool_search` tool. A forced `tool_choice` is not documented (17 of 20). JSON mode and JSON Schema come from each runtime's guided decoding, which Meta's docs show for TensorRT-LLM only (8 of 15). Prefix caching is the runtime's. Meta's SGLang command turns on hierarchical caching, and Dynamo routes each request to the worker holding its cached context (8 of 10). 1,048,576-token context, with sliding-window attention on 46 of 62 layers holding KV memory down (15 of 15). Draft models for speculative decoding in SGLang and vLLM, and no batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, but reasoning can't be turned off, defaults to the most expensive level, and needs streaming, a `max_tokens` budget for reasoning and the right stop tokens (6 of 15). The template raises named errors for an unsupported reasoning effort, tool arguments passed as a string and a tool message with no name. Server errors are the runtime's (8 of 15). 17 + 8 + 8 + 15 + 7 + 6 + 8 = 69." }, "scores": { "a": 95, "b": 69 } }, { "by": 20, "category": "payments", "leads": "muse-spark-open", "name": "Payments \u0026 pricing", "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. No machine payment protocol on the pricing page, read 9 November (0). Per-token prices published without a login, $8 input and $40 output (20). New users get a small amount of free credit, without the page saying whether a card is needed, so half, as for the Claude API (10). Keys from the browser Console only (0). 0 + 20 + 10 + 0 = 30.", "b": "Self-hosted rule. Nothing to buy from Meta, and the licence grants use free of charge. The Model as a Service clause asks for a separate agreement only from licensee groups above $20M gross revenue in 12 months (20). Free to run with no card (20). Meta's own weights need a manually reviewed Hugging Face form with legal name and date of birth, or, since the 4 November re-read of Get the model, a signed-in download from Meta's own page, so an agent can't get them alone. The ungated GGUF quantisations from ggml-org and Unsloth that Meta's docs point to download without approval (the Unsloth repository is ungated on 4 November), so part of the line (10 of 20). No payment protocol (0). Hosted copies at $1.25 input and $4.25 output per million tokens need each provider's own account and are not graded. 20 + 20 + 10 + 0 = 50." }, "scores": { "a": 30, "b": 50 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Anthropic", "b": "Meta", "name": "Vendor" }, { "a": "https://api.anthropic.com/v1", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "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": "Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-15", "name": "Last release" }, { "a": "no date given", "b": "2026-10-15", "name": "Terms last updated" }, { "a": "no date given", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "not found in the text", "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": "not found in the text", "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": "not found in the text", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in 6 of 7 scored categories. Muse Spark Open leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Claude Fable 5.2 or Muse Spark Open?" }, { "answer": "Claude Fable 5.2 needs an API key. Muse Spark Open needs no key.", "question": "Do Claude Fable 5.2 and Muse Spark Open need an API key?" }, { "answer": "Claude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/v1. No hosted endpoint is listed for Muse Spark Open.", "question": "Can an agent call Claude Fable 5.2 and Muse Spark Open without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 60 against 42", "Schema \u0026 documentation, 90 against 61", "Agent ergonomics, 95 against 69", "Security \u0026 auth, 92 against 44", "Maintenance \u0026 community, 91 against 60", "Transparency \u0026 trust, 85 against 53" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install" ], "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": [ "Payments \u0026 pricing, 50 against 30" ], "also": [ "No key needed to call it" ], "goodFor": "A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.", "slug": "muse-spark-open", "watchFor": "The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight" } ], "history": { "claude-fable-5-2": null, "muse-spark-open": [ { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.1, "from": 53.5, "fromGrade": "D", "to": 53.4, "toGrade": "D" }, { "causes": [ "evidence" ], "date": "2026-10-28", "delta": 0, "from": 53.5, "fromGrade": "D", "to": 53.5, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 53.4, "fromGrade": "D", "to": 53.5, "toGrade": "D" } ] }, "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-spark-open.json", "title": "Amazon Bedrock vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-spark-open" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open.json", "title": "Claude API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-claude-fable-5-2.json", "title": "Antseed vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/antseed-vs-claude-fable-5-2" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-muse-spark-open.json", "title": "Antseed vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-claude-fable-5-2.json", "title": "Azure OpenAI in Foundry Models vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-claude-fable-5-2" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open.json", "title": "Azure OpenAI in Foundry Models vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-claude-fable-5-2.json", "title": "BlockRun.AI vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-claude-fable-5-2" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.json", "title": "BlockRun.AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-claude-fable-5-2.json", "title": "Cerebras Inference vs Claude Fable 5.2", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-claude-fable-5-2" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-spark-open.json", "title": "Cerebras Inference vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-claude-mythos-5-2.json", "title": "Claude Fable 5.2 vs Claude Mythos 5.2", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-claude-mythos-5-2" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-ai-gateway.json", "title": "Claude Fable 5.2 vs Cloudflare AI Gateway", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-workers-ai.json", "title": "Claude Fable 5.2 vs Cloudflare Workers AI", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cloudflare-workers-ai" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cohere-chat.json", "title": "Claude Fable 5.2 vs Cohere Chat API (Command models)", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-cohere-chat" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepinfra.json", "title": "Claude Fable 5.2 vs DeepInfra", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepinfra" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepseek-api.json", "title": "Claude Fable 5.2 vs DeepSeek API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepseek-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-eden-ai.json", "title": "Claude Fable 5.2 vs Eden AI", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-elorian-glimpse.json", "title": "Claude Fable 5.2 vs Glimpse", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-elorian-glimpse" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-fireworks-inference.json", "title": "Claude Fable 5.2 vs Fireworks AI Inference", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-fireworks-inference" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-api.json", "title": "Claude Fable 5.2 vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-groq.json", "title": "Claude Fable 5.2 vs GroqCloud", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hugging-face-inference-providers.json", "title": "Claude Fable 5.2 vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-inkling-2-small.json", "title": "Claude Fable 5.2 vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3.json", "title": "Claude Fable 5.2 vs Jais 3", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-meta-model-api.json", "title": "Claude Fable 5.2 vs Meta Model API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-minimax-api.json", "title": "Claude Fable 5.2 vs MiniMax API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-mistral-api.json", "title": "Claude Fable 5.2 vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.json", "title": "Claude Fable 5.2 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-nemotron-3-5-super-vl.json", "title": "Claude Fable 5.2 vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-novita-ai.json", "title": "Claude Fable 5.2 vs Novita AI", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openai-api.json", "title": "Claude Fable 5.2 vs OpenAI API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openrouter.json", "title": "Claude Fable 5.2 vs OpenRouter", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-prism-inference.json", "title": "Claude Fable 5.2 vs Prism Inference", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-ramp-router.json", "title": "Claude Fable 5.2 vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-reflection-beam.json", "title": "Claude Fable 5.2 vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-routerplus.json", "title": "Claude Fable 5.2 vs RouterPlus", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-sambanova.json", "title": "Claude Fable 5.2 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-siliconflow.json", "title": "Claude Fable 5.2 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-simplismart.json", "title": "Claude Fable 5.2 vs Simplismart", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-stepfun-api.json", "title": "Claude Fable 5.2 vs StepFun API", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-swe-2-flash.json", "title": "Claude Fable 5.2 vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-tencent-tokenhub.json", "title": "Claude Fable 5.2 vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-together-inference.json", "title": "Claude Fable 5.2 vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vercel-ai-gateway.json", "title": "Claude Fable 5.2 vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vertex-ai.json", "title": "Claude Fable 5.2 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-xai-api.json", "title": "Claude Fable 5.2 vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-spark-open.json", "title": "Claude Mythos 5.2 vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-spark-open.json", "title": "Cloudflare AI Gateway vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open.json", "title": "Cloudflare Workers AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open.json", "title": "Cohere Chat API (Command models) vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open.json", "title": "DeepInfra vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open.json", "title": "DeepSeek API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.json", "title": "Eden AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-spark-open.json", "title": "Glimpse vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-spark-open.json", "title": "Fireworks AI Inference vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.json", "title": "Gemini Developer API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open.json", "title": "GroqCloud vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-spark-open.json", "title": "Hugging Face Inference Providers vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-spark-open.json", "title": "Inkling 2 Small vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.json", "title": "Jais 3 vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open.json", "title": "Meta Model API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open.json", "title": "MiniMax API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open.json", "title": "Mistral AI API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-nemotron-3-5-super-vl.json", "title": "Muse Spark Open vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai.json", "title": "Muse Spark Open vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api.json", "title": "Muse Spark Open vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter.json", "title": "Muse Spark Open vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference.json", "title": "Muse Spark Open vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-ramp-router.json", "title": "Muse Spark Open vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-reflection-beam.json", "title": "Muse Spark Open vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus.json", "title": "Muse Spark Open vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.json", "title": "Muse Spark Open vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.json", "title": "Muse Spark Open vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-simplismart.json", "title": "Muse Spark Open vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.json", "title": "Muse Spark Open vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-swe-2-flash.json", "title": "Muse Spark Open vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-tencent-tokenhub.json", "title": "Muse Spark Open vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-together-inference.json", "title": "Muse Spark Open vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vercel-ai-gateway.json", "title": "Muse Spark Open vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.json", "title": "Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-xai-api.json", "title": "Muse Spark Open vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-xai-api" } ], "panel": null, "scores": [ { "by": 18, "claude-fable-5-2": 60, "edge": "claude-fable-5-2", "key": "reliability", "muse-spark-open": 42, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 29, "claude-fable-5-2": 90, "edge": "claude-fable-5-2", "key": "schema", "muse-spark-open": 61, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 26, "claude-fable-5-2": 95, "edge": "claude-fable-5-2", "key": "ergonomics", "muse-spark-open": 69, "name": "Agent ergonomics", "weight": 13 }, { "by": 48, "claude-fable-5-2": 92, "edge": "claude-fable-5-2", "key": "security", "muse-spark-open": 44, "name": "Security \u0026 auth", "weight": 14 }, { "by": 20, "claude-fable-5-2": 30, "edge": "muse-spark-open", "key": "payments", "muse-spark-open": 50, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 31, "claude-fable-5-2": 91, "edge": "claude-fable-5-2", "key": "maintenance", "muse-spark-open": 60, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 32, "claude-fable-5-2": 85, "edge": "claude-fable-5-2", "key": "transparency", "muse-spark-open": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in 6 of 7 scored categories. Muse Spark Open leads on payments \u0026 pricing. 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-spark-open": "Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers." } }, "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-spark-open", "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.min.md" }, "markdown": "Claude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in 6 of 7 scored categories. Muse Spark Open leads on payments \u0026 pricing. 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 Spark Open: grade D, 53.4/100, rank #954 of 1517. Markdown https://www.anchorterminal.com/tools/muse-spark-open.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-spark-open.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 42\n- Schema \u0026 documentation, 90 against 61\n- Agent ergonomics, 95 against 69\n- Security \u0026 auth, 92 against 44\n- Maintenance \u0026 community, 91 against 60\n- Transparency \u0026 trust, 85 against 53\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n\nWatch for: Adaptive thinking is always on and can't be turned off\n\n### Muse Spark Open (D)\n\nGood for: A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.\n\nAhead on:\n- Payments \u0026 pricing, 50 against 30\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight\n\n\n## Why the edge\n\n### Security \u0026 auth: Claude Fable 5.2 +48\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 Spark Open (44): Read as software the owner runs. No account or key to run it. Meta's weights need manual approval on Hugging Face and an access token, and server authentication is the owner's to add (12 of 30). An agent model that calls whatever tools the operator gives it. The card recommends human-in-the-loop confirmation for irreversible actions and Meta reports agentic-risk evaluations, but nothing in the model or template enforces an approval (8 of 20). The chat template neutralises special-token sequences and media placeholders in caller text and wraps tool results in `\u003ctool_output\u003e`, and the card advises protecting internal assistant messages and layering defences against adversarial input. No indirect prompt-injection results are published (10 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Weights are safetensors that load through Transformers' own classes from 5.19, and Meta published cyber, chemical, biological and misalignment evaluations. dev.meta.ai/.well-known/security.txt answers 404 (re-read on 4 November), research.meta.ai per the 15 October check, and no security route is on the card or in the docs, only museoss@meta.com and the Hugging Face discussions (9 of 20). 12 + 8 + 10 + 5 + 9 = 44.\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 Spark Open (53): Weights are downloadable under the Muse Model Licence, which is clear and broad in its grant but not an OSI licence. It adds a display requirement, a separate agreement before Model as a Service by a licensee group above $20M gross revenue, and a Usage Policy with field-of-use limits (military, nuclear, espionage, critical infrastructure, heavy machinery) and an age limit of 18. It has no regional exclusion and no governing-law clause. Training code and data aren't released (18 of 30). Inputs stay on the owner's hardware, and Meta's docs say self-hosting keeps 'every prompt and response' inside the owner's infrastructure. The access form's details, and a download from Meta's own page after signing in, go to Meta under its general privacy policy, which we didn't read, and the training data is described in outline only (20 of 30). No deprecation policy, dated notices or versioning found (0 of 20). The Get the model page says everything runs offline once the weights land, and safetensors weights carry no code. No other telemetry statement, and Meta's download form records name, date of birth, country and IP location (12 of 20). 18 + 20 + 0 + 12 = 50.\n\n### Maintenance \u0026 community: Claude Fable 5.2 +31\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 Spark Open (60): Read for an open-weight model released three weeks ago. Released on 15 October 2026 (30). One release, BF16 and NVFP4 on the same day, with no history of revisions, successors or notice periods, and Meta Model API has no deprecation policy either (6 of 20). None of the 29 replies counted by script on 4 November across the BF16 repository's discussions came from Meta. Feedback goes to the discussions or museoss@meta.com, and Meta's docs and developer blog carry dated posts (8 of 25). Day-one support in vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, MLX and Unsloth with Meta's own recipes, though SGLang's is a dev image and TensorRT-LLM's a feature branch (12 of 15). Current dependencies (Transformers 5.19), but no CI visible to us (4 of 10). 30 + 6 + 8 + 12 + 4 = 60.\n\n### Schema \u0026 documentation: Claude Fable 5.2 +29\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 Spark Open (61): Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which renders OpenAI-format tools with `additionalProperties: false` added and rejects bad values, plus the OpenAI-compatible servers in vLLM and SGLang with the `muse` parsers (10 of 25). dev.meta.ai/llms.txt lists the Muse Spark Open overview and seven pages under it, plus three cookbook pages, each with a Markdown copy (10). The card lists seven intended uses and six limitations, the docs say when to pick BF16 or NVFP4 and which reasoning strength suits which task, and the Usage Policy states what is out of scope (17 of 20). `reasoning_effort` is an enum of four values and anything else returns an error, and tools take JSON Schema. Structured output is documented only for TensorRT-LLM's `response_format`, not in the prompting guide or for vLLM and SGLang (11 of 15). curl and Python examples for every runtime and hosted provider, the rendered prompt for each case, and the template's own error messages, but no error reference for a served model (11 of 15). One release, with no changelog or version scheme beyond the licence's version line, Muse Spark Open (2 of 15). 10 + 10 + 17 + 11 + 11 + 2 = 61.\n\n### Agent ergonomics: Claude Fable 5.2 +26\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 Spark Open (69): Model reading of the checklist (tool use, structured output, caching, context, throughput, defaults and SDKs, errors). OpenAI-format tools and parallel calls come back as `tool_calls`, and the template supports namespaced tools and deferred loading through a `tool_search` tool. A forced `tool_choice` is not documented (17 of 20). JSON mode and JSON Schema come from each runtime's guided decoding, which Meta's docs show for TensorRT-LLM only (8 of 15). Prefix caching is the runtime's. Meta's SGLang command turns on hierarchical caching, and Dynamo routes each request to the worker holding its cached context (8 of 10). 1,048,576-token context, with sliding-window attention on 46 of 62 layers holding KV memory down (15 of 15). Draft models for speculative decoding in SGLang and vLLM, and no batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, but reasoning can't be turned off, defaults to the most expensive level, and needs streaming, a `max_tokens` budget for reasoning and the right stop tokens (6 of 15). The template raises named errors for an unsupported reasoning effort, tool arguments passed as a string and a tool message with no name. Server errors are the runtime's (8 of 15). 17 + 8 + 8 + 15 + 7 + 6 + 8 = 69.\n\n### Payments \u0026 pricing: Muse Spark Open +20\n\n- Claude Fable 5.2 (30): 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. No machine payment protocol on the pricing page, read 9 November (0). Per-token prices published without a login, $8 input and $40 output (20). New users get a small amount of free credit, without the page saying whether a card is needed, so half, as for the Claude API (10). Keys from the browser Console only (0). 0 + 20 + 10 + 0 = 30.\n- Muse Spark Open (50): Self-hosted rule. Nothing to buy from Meta, and the licence grants use free of charge. The Model as a Service clause asks for a separate agreement only from licensee groups above $20M gross revenue in 12 months (20). Free to run with no card (20). Meta's own weights need a manually reviewed Hugging Face form with legal name and date of birth, or, since the 4 November re-read of Get the model, a signed-in download from Meta's own page, so an agent can't get them alone. The ungated GGUF quantisations from ggml-org and Unsloth that Meta's docs point to download without approval (the Unsloth repository is ungated on 4 November), so part of the line (10 of 20). No payment protocol (0). Hosted copies at $1.25 input and $4.25 output per million tokens need each provider's own account and are not graded. 20 + 20 + 10 + 0 = 50.\n\n## Score by category\n\n| Category | Weight | Claude Fable 5.2 | Muse Spark Open | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 60 | 42 | Claude Fable 5.2 +18 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 90 | 61 | Claude Fable 5.2 +29 |\n| Agent ergonomics | 13% (16.2 this run) | 95 | 69 | Claude Fable 5.2 +26 |\n| Security \u0026 auth | 14% (17.5 this run) | 92 | 44 | Claude Fable 5.2 +48 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 50 | Muse Spark Open +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 91 | 60 | Claude Fable 5.2 +31 |\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** | **53.4 · D** | |\n\n## Facts side by side\n\n| Fact | Claude Fable 5.2 | Muse Spark Open |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Anthropic | Meta |\n| Hosted endpoint | `https://api.anthropic.com/v1` | no (local only) |\n| Transports | | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | Closed model, used through the Claude API and cloud platforms under Anthropic's Commercial Terms of Service | Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-15 |\n| Terms last updated | no date given | 2026-10-15 |\n| Privacy policy last updated | no date given | no document linked |\n| Customer content may train models | yes, with an opt-out | not found in the text |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\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 | not found in the text |\n\n## How the scores have moved\n\n### Claude Fable 5.2\n\nNo score change since it was listed.\n\n### Muse Spark Open\n\n- -0.1 on 30 October 2026: checklist\n- no change on 28 October 2026: new evidence\n- +0.1 on 27 October 2026: checklist\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 Spark Open.** Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.\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 Spark Open\n\n1. Start vLLM or SGLang with `--reasoning-parser muse --tool-call-parser muse` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as raw text\n2. Set `reasoning_effort` to `low` or `medium` for short tasks. The default is `xhigh`, reasoning can't be turned off, and other values return an error\n3. Stop only on `\u003c|eot|\u003e` and end of text. Stopping on `\u003c|eom|\u003e` cuts off tool calls and final answers\n4. Stream long generations and leave `max_tokens` room for reasoning, or `content` can come back empty with `finish_reason` `length`\n5. Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as `meta-models/muse-spark-open`\n\n## Questions\n\n### Which is better for AI agents, Claude Fable 5.2 or Muse Spark Open?\n\nClaude Fable 5.2 scores 77.3 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in 6 of 7 scored categories. Muse Spark Open leads on payments \u0026 pricing.\n\n### Do Claude Fable 5.2 and Muse Spark Open need an API key?\n\nClaude Fable 5.2 needs an API key. Muse Spark Open needs no key.\n\n### Can an agent call Claude Fable 5.2 and Muse Spark Open without installing anything?\n\nClaude Fable 5.2 has a hosted endpoint at https://api.anthropic.com/v1. No hosted endpoint is listed for Muse Spark Open.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.json, and with the fewest tokens: https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"claude-fable-5-2\", \"b\": \"muse-spark-open\"}`. From a terminal: `anchor compare claude-fable-5-2 muse-spark-open`\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-spark-open.json\n\n## Other comparisons with Claude Fable 5.2 or Muse Spark Open\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 Spark Open](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-spark-open.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 Spark Open](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open.md)\n- [Antseed vs Claude Fable 5.2](https://www.anchorterminal.com/compare/antseed-vs-claude-fable-5-2.md)\n- [Antseed vs Muse Spark Open](https://www.anchorterminal.com/compare/antseed-vs-muse-spark-open.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 Spark Open](https://www.anchorterminal.com/compare/azure-openai-vs-muse-spark-open.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 Spark Open](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.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 Spark Open](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-spark-open.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 Ember](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.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 Spark Open](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-spark-open.md)\n- [Cloudflare AI Gateway vs Muse Spark Open](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-spark-open.md)\n- [Cloudflare Workers AI vs Muse Spark Open](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open.md)\n- [Cohere Chat API (Command models) vs Muse Spark Open](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open.md)\n- [DeepInfra vs Muse Spark Open](https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open.md)\n- [DeepSeek API vs Muse Spark Open](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open.md)\n- [Eden AI vs Muse Spark Open](https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.md)\n- [Glimpse vs Muse Spark Open](https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-spark-open.md)\n- [Fireworks AI Inference vs Muse Spark Open](https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-spark-open.md)\n- [Gemini Developer API vs Muse Spark Open](https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.md)\n- [GroqCloud vs Muse Spark Open](https://www.anchorterminal.com/compare/groq-vs-muse-spark-open.md)\n- [Hugging Face Inference Providers vs Muse Spark Open](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-spark-open.md)\n- [Inkling 2 Small vs Muse Spark Open](https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-spark-open.md)\n- [Jais 3 vs Muse Spark Open](https://www.anchorterminal.com/compare/jais-3-vs-muse-spark-open.md)\n- [Meta Model API vs Muse Spark Open](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open.md)\n- [MiniMax API vs Muse Spark Open](https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open.md)\n- [Mistral AI API vs Muse Spark Open](https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Spark Open vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-spark-open-vs-nemotron-3-5-super-vl.md)\n- [Muse Spark Open vs Novita AI](https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai.md)\n- [Muse Spark Open vs OpenAI API](https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api.md)\n- [Muse Spark Open vs OpenRouter](https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter.md)\n- [Muse Spark Open vs Prism Inference](https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference.md)\n- [Muse Spark Open vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-spark-open-vs-ramp-router.md)\n- [Muse Spark Open vs Beam 1.0](https://www.anchorterminal.com/compare/muse-spark-open-vs-reflection-beam.md)\n- [Muse Spark Open vs RouterPlus](https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus.md)\n- [Muse Spark Open vs SambaCloud](https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.md)\n- [Muse Spark Open vs SiliconFlow](https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.md)\n- [Muse Spark Open vs Simplismart](https://www.anchorterminal.com/compare/muse-spark-open-vs-simplismart.md)\n- [Muse Spark Open vs StepFun API](https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.md)\n- [Muse Spark Open vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-spark-open-vs-swe-2-flash.md)\n- [Muse Spark Open vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-spark-open-vs-tencent-tokenhub.md)\n- [Muse Spark Open vs Together AI Inference](https://www.anchorterminal.com/compare/muse-spark-open-vs-together-inference.md)\n- [Muse Spark Open vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-spark-open-vs-vercel-ai-gateway.md)\n- [Muse Spark Open vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-spark-open-vs-vertex-ai.md)\n- [Muse Spark Open vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-spark-open-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 Spark Open", "url": "" } ], "description": "Claude Fable 5.2 scores 77.3 (BB) to Muse Spark Open's 53.4 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Claude Fable 5.2 BB 77.3", "Muse Spark Open D 53.4", "scores" ], "h1": "Claude Fable 5.2 vs Muse Spark Open", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/claude-fable-5-2-vs-muse-spark-open", "published": "2026-10-01", "section": "tools", "title": "Claude Fable 5.2 vs Muse Spark Open for AI agents (2026)", "toc": null, "updated": "2026-11-09", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-spark-open" }, "tokens": { "markdown": 7450, "slim": 1080 }, "version": 1 }