{ "data": { "a": { "slug": "muse-ember", "name": "Muse Ember", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.", "url": "https://www.anchorterminal.com/tools/muse-ember", "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json", "dns": "muse-ember.grade.anchorterminal.com", "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "transports": [], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://dev.meta.ai/docs/muse-ember", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "openai-compatible", "anthropic-compatible", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 51.8, "grade": "D", "agentReady": false, "rank": 1017, "ranked": true, "rankOf": 1515, "categoryRank": 36, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "strengths": [ "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login", "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens", "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints", "Tool calling, structured output, computer use and search grounding", "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to", "3,000 requests and 4,000,000 tokens a minute per team, published" ], "weaknesses": [ "No weights and no parameter count published, so it can't be run locally", "Requests over 200K input tokens are billed at three times the rate for the whole request", "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower", "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing", "Benchmark and cost figures are Meta's own, several on internal benchmarks" ], "agentNotes": [ "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY", "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing", "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate", "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises", "Use strict structured output for extraction and routing steps", "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.8 } ], "editorialScores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-09", "maintenance": "2026-11-09", "payments": "2026-11-09", "reliability": "2026-11-09", "schema": "2026-11-09", "security": "2026-11-09", "transparency": "2026-11-09" }, "last": "2026-11-09", "oldest": "2026-11-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"muse-ember-1.0\",\n \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n \"reasoning\": {\"effort\": \"low\"}\n}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-ember" }, "sameCompany": [ "muse-spark-open", "muse-code", "muse-glimmer-30b" ], "sameModel": [ "meta-model-api" ], "area": "models", "unitPrices": [ { "item": "Muse Ember input", "unit": "1m-tokens", "usd": 0.1, "note": "Up to 200K input tokens a request; $0.30 above, for the whole request" }, { "item": "Muse Ember cached input", "unit": "1m-tokens", "usd": 0.004, "note": "$0.012 above 200K input tokens" }, { "item": "Muse Ember output", "unit": "1m-tokens", "usd": 0.5, "note": "$1.50 above 200K input tokens" } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-09", "notes": [ "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.", "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json", "live": { "slug": "muse-ember", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-11-09T20:50:02.257490788Z", "lastOk": true, "lastStatus": 404, "lastMs": 46, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 72, "samples24h": 10, "samples30d": 10, "days": [ { "date": "2026-11-09", "probes": 10, "ok": 10 } ], "since": "2026-11-09T19:55:51.172718582Z" }, "updatedAt": "2026-11-09T20:50:02.257490788Z" }, "streak": { "days": 0, "from": "2026-11-09", "sinceWatching": true, "best": 0 } }, "answer": "Muse Spark Open scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "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": 952, "ranked": true, "rankOf": 1515, "categoryRank": 32, "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": "2026-11-09T16:04:43.357370024Z" }, "llmsTxt": { "url": "https://dev.meta.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:21.325776156Z" }, "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": "2026-11-09T15:43:17.110166162Z", "changedAt": "2026-11-09T15:43:17.110166162Z", "fingerprint": "a887f3a5762c" } ], "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": 943718, "checkedAt": "2026-11-08T23:49:27.027409601Z" } }, "updatedAt": "2026-11-09T16:04:43.357370024Z" } }, "edges": [ { "by": 30, "category": "payments", "leads": "muse-spark-open", "name": "Payments \u0026 pricing", "reasons": { "a": "Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.", "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": 20, "b": 50 } }, { "by": 20, "category": "security", "leads": "muse-ember", "name": "Security \u0026 auth", "reasons": { "a": "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.", "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": 64, "b": 44 } }, { "by": 14, "category": "maintenance", "leads": "muse-spark-open", "name": "Maintenance \u0026 community", "reasons": { "a": "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.", "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": 46, "b": 60 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "Meta", "name": "Vendor" }, { "a": "https://api.meta.ai/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 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", "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": "2026-10-02", "b": "2026-10-15", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "yes", "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": "Muse Spark Open scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "question": "Which is better for AI agents, Muse Ember or Muse Spark Open?" }, { "answer": "Muse Ember needs an API key. Muse Spark Open needs no key.", "question": "Do Muse Ember and Muse Spark Open need an API key?" }, { "answer": "Muse Ember has a hosted endpoint at https://api.meta.ai/v1. No hosted endpoint is listed for Muse Spark Open.", "question": "Can an agent call Muse Ember and Muse Spark Open without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 64 against 44" ], "also": [ "A hosted endpoint, with nothing to install" ], "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" }, { "aheadOn": [ "Payments \u0026 pricing, 50 against 20", "Maintenance \u0026 community, 60 against 46" ], "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": { "muse-ember": 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-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/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-muse-ember.json", "title": "Claude API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember" }, { "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-muse-ember.json", "title": "Antseed vs Muse Ember", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember" }, { "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-muse-ember.json", "title": "Azure OpenAI in Foundry Models vs Muse Ember", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember" }, { "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-muse-ember.json", "title": "BlockRun.AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember" }, { "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-muse-ember.json", "title": "Cerebras Inference vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember" }, { "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/cloudflare-ai-gateway-vs-muse-ember.json", "title": "Cloudflare AI Gateway vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember" }, { "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-ember.json", "title": "Cloudflare Workers AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember" }, { "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-ember.json", "title": "Cohere Chat API (Command models) vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember" }, { "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-ember.json", "title": "DeepInfra vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember" }, { "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-ember.json", "title": "DeepSeek API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember" }, { "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-ember.json", "title": "Eden AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember" }, { "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-ember.json", "title": "Glimpse vs 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"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-ember.json", "title": "GroqCloud vs Muse Ember", "url": "https://www.anchorterminal.com/compare/groq-vs-muse-ember" }, { "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-ember.json", "title": "Hugging Face Inference Providers vs Muse Ember", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember" }, { "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-ember.json", "title": "Inkling 2 Small vs Muse Ember", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember" }, { "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-ember.json", "title": "Jais 3 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember" }, { "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-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "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-ember.json", "title": "MiniMax API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember" }, { "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-ember.json", "title": "Mistral AI API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember" }, { "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-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", 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"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", 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"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": 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"panel": null, "scores": [ { "by": 2, "edge": "muse-spark-open", "key": "reliability", "muse-ember": 40, "muse-spark-open": 42, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 1, "edge": "muse-spark-open", "key": "schema", "muse-ember": 60, "muse-spark-open": 61, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 3, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "muse-spark-open": 69, "name": "Agent ergonomics", "weight": 13 }, { "by": 20, "edge": "muse-ember", "key": "security", "muse-ember": 64, "muse-spark-open": 44, "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "edge": "muse-spark-open", "key": "payments", "muse-ember": 20, "muse-spark-open": 50, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 14, "edge": "muse-spark-open", "key": "maintenance", "muse-ember": 46, "muse-spark-open": 60, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 0, "edge": "", "key": "transparency", "muse-ember": 53, "muse-spark-open": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Muse Spark Open scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "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.", "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/muse-ember-vs-muse-spark-open", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.min.md" }, "markdown": "Muse Spark Open scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on security \u0026 auth. Both do llm inference.\n\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Muse Spark Open: grade D, 53.4/100, rank #952 of 1515. 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 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\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\nAhead on:\n- Security \u0026 auth, 64 against 44\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: No weights and no parameter count published, so it can't be run locally\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 20\n- Maintenance \u0026 community, 60 against 46\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### Payments \u0026 pricing: Muse Spark Open +30\n\n- Muse Ember (20): Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.\n- 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### Security \u0026 auth: Muse Ember +20\n\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- 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### Maintenance \u0026 community: Muse Spark Open +14\n\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- 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## Score by category\n\n| Category | Weight | Muse Ember | Muse Spark Open | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 42 | Muse Spark Open +2 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 61 | Muse Spark Open +1 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 69 | Muse Ember +3 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 44 | Muse Ember +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 50 | Muse Spark Open +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 60 | Muse Spark Open +14 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 53 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.8 · D** | **53.4 · D** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | Muse Spark Open |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | Meta |\n| Hosted endpoint | `https://api.meta.ai/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 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 | 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 | 2026-10-02 | 2026-10-15 |\n| Privacy policy last updated | couldn't be read | no document linked |\n| Customer content may train models | yes | 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### Muse Ember\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**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**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### 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### 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, Muse Ember or Muse Spark Open?\n\nMuse Spark Open scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on security \u0026 auth.\n\n### Do Muse Ember and Muse Spark Open need an API key?\n\nMuse Ember needs an API key. Muse Spark Open needs no key.\n\n### Can an agent call Muse Ember and Muse Spark Open without installing anything?\n\nMuse Ember has a hosted endpoint at https://api.meta.ai/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/muse-ember-vs-muse-spark-open.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"muse-spark-open\"}`. From a terminal: `anchor compare muse-ember muse-spark-open`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-ember.json and https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json\n\n## Other comparisons with Muse Ember or Muse Spark Open\n\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Amazon Bedrock vs Muse Spark Open](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-spark-open.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Claude API vs Muse Spark Open](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.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 Muse 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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", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Muse Ember vs Muse Spark Open", "url": "" } ], "description": "Muse Spark Open scores 53.4 (D) to Muse Ember's 51.8 (D) for llm inference. 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