{ "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 Ember scores 51.8 (D) on agent readiness against SiliconFlow's 42.9 (E), and leads in 5 of 7 scored categories. SiliconFlow leads on schema \u0026 documentation and payments \u0026 pricing.", "b": { "slug": "siliconflow", "name": "SiliconFlow", "vendor": "SiliconFlow Labs Pte. Ltd.", "vendorUrl": "https://www.siliconflow.com", "kind": "model", "category": "inference", "summary": "SiliconFlow is a hosted inference API for open-weight models, covering chat, vision, embeddings, reranking, image, video and speech. It answers OpenAI-style and Anthropic-style calls at api.siliconflow.com with a Bearer key.", "url": "https://www.anchorterminal.com/tools/siliconflow", "markdownUrl": "https://www.anchorterminal.com/tools/siliconflow.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/siliconflow.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/siliconflow.json", "dns": "siliconflow.grade.anchorterminal.com", "license": "Proprietary service under the SiliconFlow Terms of Use", "transports": [ "http" ], "remoteUrl": "https://api.siliconflow.com/v1", "packages": [], "auth": "api-key", "authNotes": "Self-serve Bearer key from the console at https://cloud.siliconflow.com/account/ak after a browser sign-up with a Google or GitHub account. The Anthropic-style `/messages` route also takes the key in `x-api-key`. No scopes, expiry or per-key limits are documented, and rate limits apply to the account, not the key. The error page says some models answer 403 until real-name authentication is done.", "pricing": "usage", "pricingNotes": "Pay per token, image, video or byte of speech text, with $1 in free credits for a new account. Whether a card is needed to claim the credit was not established. DeepSeek-V4.1-Flash costs $0.15 in, $0.003 cached and $0.60 out per 1M tokens. The FAQ says monthly spending limits can be set in the dashboard (https://www.siliconflow.com/pricing, checked 2026-10-09).", "priceSummary": "from $0.15 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI file or the pricing page (checked 2026-10-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-09" }, "docsUrl": "https://docs.siliconflow.com/en/userguide/quickstart", "rateLimitsUrl": "https://docs.siliconflow.com/cn/userguide/rate-limits/rate-limit-and-upgradation", "llmsTxt": "https://docs.siliconflow.com/llms.txt", "openapi": "https://docs.siliconflow.com/cn/api-reference/openapi.yaml", "capabilities": [ "inference.llm", "inference.open-weights", "embed.text", "rerank", "image.generate", "video.generate", "speech.tts", "speech.stt" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "free-credit", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "singapore" ], "lastRelease": "2026-09-14", "graded": true, "anchor": { "graded": true, "score": 42.9, "grade": "E", "agentReady": false, "rank": 1271, "ranked": true, "rankOf": 1515, "categoryRank": 40, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 37, "payments": 35, "reliability": 33, "schema": 65, "security": 39, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-05" }, "negative": -3, "negativeNotes": [ "2025-12-31 to 2026-06-11. SiliconFlow's release notes carry six model removal entries dated the day the models were removed, with no earlier entry announcing them, and on 11 June 2026 all traffic for `zai-org/GLM-5` and `moonshotai/Kimi-K2.5` was routed to successor models in an entry of the same date. Requests naming those models stopped working or changed model with notice only on the day. Read at the floor and folded into one term, as together-inference's and fireworks-inference's same-day serverless removals were, -3 (https://docs.siliconflow.com/en/release-notes/overview, per the 9 October reading)." ], "verdict": "Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.", "bestFor": "Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.", "strengths": [ "Per-token, per-image and per-video prices are public for every listed model, such as DeepSeek-V4.1-Flash at $0.15 in and $0.60 out per 1M tokens", "One Bearer key reaches 22 operations in a public OpenAPI 3.0 file, with OpenAI-style `/chat/completions` and Anthropic-style `/messages`", "`llms.txt` and a Markdown copy of every docs page, in English and Chinese", "Rate limits are published by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5", "The terms say SiliconFlow will not access Interaction Data, has no obligation to store it, and uses it only to supply the service" ], "weaknesses": [ "No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment", "Release notes stop at 11 June 2026. Six notices from December 2025 to June 2026 give a removal date equal to the notice date", "The Terms of Use of 9 September 2026 forbid use for any commercial purposes, scraping tools and benchmarking. This matters before any probe is run", "The OpenAPI model list and the function calling guide name models already removed, and omit every model added since July 2026", "No security.txt, disclosure policy, certification, DPA or sub-processor list was found, and no official SDK is published" ], "agentNotes": [ "Set the OpenAI client's base URL to `https://api.siliconflow.com/v1`, or the Anthropic client's to `https://api.siliconflow.com/`, and send the key as a Bearer token", "Take model ids from the model pages or `GET /v1/models`, not from the OpenAPI enum or the function calling guide, which list removed models", "Check the `model` field of each response. On 11 June 2026 traffic for GLM-5 and Kimi-K2.5 was routed to successor models", "Use `response_format` of `json_object` only where the model page says JSON Mode is supported, and keep `max_tokens` about 10,000 below the context length", "Set the Claude Code environment variables by hand. The automated route pipes a script from an Amazon S3 bucket into bash" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "E", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 42.9 } ], "editorialScores": { "ergonomics": 60, "maintenance": 37, "payments": 35, "reliability": 33, "schema": 65, "security": 39, "transparency": 34 }, "provenanceScore": 70, "checked": { "categories": { "ergonomics": "2026-11-05", "maintenance": "2026-11-05", "payments": "2026-11-05", "reliability": "2026-11-05", "schema": "2026-11-05", "security": "2026-11-05", "transparency": "2026-11-05" }, "last": "2026-11-05", "lastMoved": "2026-11-05", "oldest": "2026-11-05", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install --upgrade openai", "http": "curl --request POST \\\n --url https://api.siliconflow.com/v1/chat/completions \\\n --header 'authorization: Bearer $SILICONFLOW_API_KEY' \\\n --header 'content-type: application/json' \\\n --data '{\"model\":\"deepseek-ai/DeepSeek-V4.1-Flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}],\"max_tokens\":128}'", "claudeCode": "export ANTHROPIC_BASE_URL=\"https://api.siliconflow.com/\"\nexport ANTHROPIC_MODEL=\"your-preferred-model\"\nexport ANTHROPIC_API_KEY=\"sk-your-api-key-here\"" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/siliconflow" }, "area": "models", "unitPrices": [ { "item": "Z-Image-Turbo, one image", "unit": "image", "usd": 0.005 }, { "item": "FLUX.1-dev, one image", "unit": "image", "usd": 0.014 }, { "item": "FLUX 1.1 [pro], one image", "unit": "image", "usd": 0.04 }, { "item": "Wan2.2-T2V-A14B, one video", "unit": "call", "usd": 0.29, "note": "Priced per video generated" } ], "provenance": { "legalEntity": "SiliconFlow Labs Pte. Ltd.", "domain": "siliconflow.com", "domainRegistered": "2019-02-14", "endpointOnVendorDomain": true, "terms": "https://docs.siliconflow.com/en/legals/terms-of-service", "privacy": "https://docs.siliconflow.com/en/legals/privacy-policy", "statusPage": "", "changelog": "https://docs.siliconflow.com/en/release-notes/overview", "securityTxt": "none", "checked": "2026-11-05", "notes": [ "The Terms of Use, updated 9 September 2026, name SILICONFLOW LABS PTE. LTD., apply Singapore law and set SIAC arbitration in Singapore. They govern the platform in its web, API and mobile versions, and no separate API agreement was found.", "The Privacy Policy, updated 9 September 2026, names the same entity and cites Singapore's Personal Data Protection Act 2012. It keeps square brackets around several values, such as [Stripe] and [Google and GitHub].", "No status page is linked from the site, the docs index or the pages read.", "https://www.siliconflow.com/.well-known/security.txt answered 404.", "The API answers at api.siliconflow.com, the docs at docs.siliconflow.com and the console at cloud.siliconflow.com. The terms point users in mainland China to siliconflow.cn.", "No SLA, DPA or sub-processor list was found.", "The Terms of Use (updated 9 September 2026) clause 3.4(i) bar any manual or automated software, including spiders, robots, scrapers and crawlers, to scrape, collect or download information from the Platform or Services, which they define to include the web, API and mobile versions (read 5 November 2026).", "The domain registration date was recorded from an RDAP lookup on 9 October 2026, a source this check doesn't use. It wasn't re-read." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/siliconflow.json", "live": { "slug": "siliconflow", "probe": { "target": "https://api.siliconflow.com/v1", "method": "get", "lastAt": "2026-11-09T20:50:14.340067296Z", "lastOk": true, "lastStatus": 404, "lastMs": 265, "authRequired": false, "uptime24h": 100, "uptime30d": 99.99, "p50ms24h": 280, "p95ms24h": 375, "samples24h": 244, "samples30d": 7716, "days": [ { "date": "2026-10-09", "probes": 85, "ok": 85 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 252 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 212, "ok": 212 } ], "since": "2026-10-09T00:00:00Z" }, "securityTxt": { "url": "https://siliconflow.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:05:24.584032287Z" }, "llmsTxt": { "url": "https://docs.siliconflow.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:57.658463318Z" }, "domain": { "domain": "siliconflow.com", "registered": "2019-02-14", "source": "https://rdap.verisign.com/com/v1/domain/siliconflow.com", "checkedAt": "2026-11-08T14:52:35.114498363Z" }, "pages": [ { "url": "https://docs.siliconflow.com/en/release-notes/overview", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:39:06.697753959Z", "changedAt": "2026-10-26T03:15:35.683654862Z", "fingerprint": "174b36f1f0c8" }, { "url": "https://www.siliconflow.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-09T16:05:29.718386275Z", "changedAt": "2026-10-22T01:36:57.039692841Z", "fingerprint": "e1314294f4a8" }, { "url": "https://docs.siliconflow.com/en/legals/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-09T15:39:02.567489699Z", "changedAt": "2026-10-26T03:15:31.436126122Z", "fingerprint": "6533065904d9" }, { "url": "https://docs.siliconflow.com/en/legals/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:39:04.729607623Z", "changedAt": "2026-10-26T03:15:33.589043532Z", "fingerprint": "9b70022b01e3" } ], "updatedAt": "2026-11-09T20:50:14.340067296Z" }, "streak": { "days": 31, "from": "2026-10-09", "sinceWatching": true, "last": { "kind": "shutdown", "date": "2026-06-11", "what": "`zai-org/GLM-4.7` removed, and traffic for `zai-org/GLM-5` and `moonshotai/Kimi-K2.5` routed to GLM-5.1 and Kimi-K2.6", "source": "https://docs.siliconflow.com/en/release-notes/overview" }, "best": 31 } }, "edges": [ { "by": 25, "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": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Bearer keys created in the console. No scopes, expiry, per-key limits or rotation are documented, and deletion of a key was not confirmed because the console was not read (15 of 30). The terms say Interaction Data is used only to supply the service and is not used or disclosed without authorisation. No statement on model training was found (12 of 20). The terms say there is no obligation to store Interaction Data. No retention period or zero-retention option is stated, and clause 5.11 allows communications to be recorded and passed to the authorities (6 of 15). The release note of 26 March 2026 describes usage and cost breakdowns and invoice export, and the pricing FAQ says monthly spending limits can be set. No audit log was found (6 of 15). No security.txt (404), disclosure policy, bug bounty or named certification was found on the pages read. The product introduction says the service complies with industry standards and names none (0 of 20). 15+12+6+6+0 = 39." }, "scores": { "a": 64, "b": 39 } }, { "by": 15, "category": "payments", "leads": "siliconflow", "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": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. No machine payment protocol (0). Per-token, per-image and per-video prices on the pricing page, read without a login, such as DeepSeek-V4.1-Flash at $0.15 in and $0.60 out per 1M tokens (20). $1 in free credits for a new account. Whether a card is needed was not established, so this line takes 15 of 20. A person signs up in a browser with a Google or GitHub account, and no key API was found (0). 0+20+15+0 = 35." }, "scores": { "a": 20, "b": 35 } }, { "by": 12, "category": "ergonomics", "leads": "muse-ember", "name": "Agent ergonomics", "reasons": { "a": "Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.", "b": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-style `tools` with up to 128 functions, and each model page says whether Tools are supported. No `tool_choice` field is in the chat schema and the guide is out of date (12 of 20). `response_format` of `json_object`. The DeepSeek-V4.1-Flash page says Structured Outputs are not supported (7 of 15). Cached input prices are published per model. No caching guide was found (8 of 15). Context listed as 1049K tokens on eleven chat models on the home page, with output up to 393K (14 of 15). `/files` and `/batches` are in the OpenAPI file for chat completions only, with no guide in the English index, no batch price and a window description that contradicts itself (5 of 10). No official SDK. The OpenAI and Anthropic clients work with a changed base URL (6 of 10). Errors carry an HTTP status, a numeric code and a message. No list of codes was found (8 of 15). 12+7+8+14+5+6+8 = 60." }, "scores": { "a": 72, "b": 60 } }, { "by": 9, "category": "maintenance", "leads": "muse-ember", "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": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Model reading, per the 9 October check, not re-read on 5 November. Hy4-preview is listed as released on 14 September 2026, 52 days before this check, and no later model or API change was recorded; blog post dates record no API change (20). No notice period is stated. Six removal notices from 31 December 2025 to 11 June 2026 are dated the day of removal, and two in March 2026 gave six and seven days (2 of 12). The release notes record about 60 model ids removed in ten notices over the last twelve months (2 of 8). The release notes stop at 11 June 2026 while nine chat models were added afterwards. Support is by email and Discord (6 of 15). Replies in the support channels were not examined (2 of 10). No official SDK. The `langchain-siliconflow` repository in the vendor's GitHub organisation was last changed on 12 December 2025 (version 0.2.3), confirmed from a clone on 5 November (3 of 15). The API description and two guides are out of date (2 of 10). 20+2+2+6+2+3+2 = 37." }, "scores": { "a": 46, "b": 37 } }, { "by": 7, "category": "reliability", "leads": "muse-ember", "name": "Reliability", "reasons": { "a": "Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.", "b": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Hosted reading. No status page is linked from the site, the docs index or any page read (0). With no page there is no incident record to read (5 of 30). Rate limits are published with numbers by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5, on a page served only in Chinese, with per-model figures left to the console (12 of 15). The 429 body is documented and names the limit reached, and the docs say to wait and retry. No `Retry-After` header, backoff figures or idempotency guidance were found (6 of 15). No SLA found. Clause 4.2 of the terms disclaims any uptime or availability commitment (0). The serverless API is generally available, and `/systemone` is marked alpha (10). 0+5+12+6+0+10 = 33." }, "scores": { "a": 40, "b": 33 } }, { "by": 5, "category": "schema", "leads": "siliconflow", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.", "b": "SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Public OpenAPI 3.0 file with 22 operations and 102 schemas, linked from `llms.txt` under the Chinese path only. Its chat model enum of 71 ids includes removed models and omits every model added since July 2026 (19 of 25). `llms.txt` and a Markdown copy of each docs page (10). Guides for function calling, JSON mode, reasoning, prefix and FIM completion. The function calling guide lists only models the release notes show as removed, and its examples use DeepSeek-V2.5 (10 of 20). Typed parameters with 104 enums and required fields. `response_format` is typed as a bare string with an example (10 of 15). Curl and Python examples, an error page with seven HTTP codes and one body code, and 400, 401, 404, 429, 503 and 504 responses in the file (9 of 15). The file's version reads 1.0.0. The release notes are dated, stop at 11 June 2026 and since March 2026 record only model removals (7 of 15). 19+10+10+10+9+7 = 65." }, "scores": { "a": 60, "b": 65 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "SiliconFlow Labs Pte. Ltd.", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.siliconflow.com/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through 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": "Proprietary service under the SiliconFlow Terms of Use", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-09-14", "name": "Last release" }, { "a": "2026-10-02", "b": "no date given", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "no date given", "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": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "yes", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Muse Ember scores 51.8 (D) on agent readiness against SiliconFlow's 42.9 (E), and leads in 5 of 7 scored categories. SiliconFlow leads on schema \u0026 documentation and payments \u0026 pricing.", "question": "Which is better for AI agents, Muse Ember or SiliconFlow?" }, { "answer": "Both need an API key.", "question": "Do Muse Ember and SiliconFlow need an API key?" }, { "answer": "Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and SiliconFlow at https://api.siliconflow.com/v1.", "question": "Can an agent call Muse Ember and SiliconFlow without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 40 against 33", "Agent ergonomics, 72 against 60", "Security \u0026 auth, 64 against 39", "Maintenance \u0026 community, 46 against 37" ], "also": [ "No incidents deducted, where SiliconFlow loses 3 points for them" ], "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": [ "Schema \u0026 documentation, 65 against 60", "Payments \u0026 pricing, 35 against 20" ], "also": null, "goodFor": "Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.", "slug": "siliconflow", "watchFor": "No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment" } ], "history": { "muse-ember": null, "siliconflow": [ { "causes": [ "correction", "time" ], "date": "2026-11-05", "delta": -3.9, "from": 46.8, "fromGrade": "D", "to": 42.9, "toGrade": "E" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.2, "from": 47, "fromGrade": "D", "to": 46.8, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.3, "from": 46.7, "fromGrade": "D", "to": 47, "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" }, { 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"https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" }, { "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/novita-ai-vs-siliconflow.json", "title": "Novita AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-siliconflow.json", "title": "OpenAI API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/openai-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-siliconflow.json", "title": "OpenRouter vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/openrouter-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow.json", "title": "Prism Inference vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-siliconflow.json", "title": "Router.com (Ramp Router) vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-siliconflow.json", "title": "Beam 1.0 vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-siliconflow.json", "title": "RouterPlus vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/routerplus-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-siliconflow.json", "title": "SambaCloud vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/sambanova-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.json", "title": "SiliconFlow vs Simplismart", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api.json", "title": "SiliconFlow vs StepFun API", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-swe-2-flash.json", "title": "SiliconFlow vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-tencent-tokenhub.json", "title": "SiliconFlow vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-together-inference.json", "title": "SiliconFlow vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-vercel-ai-gateway.json", "title": "SiliconFlow vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.json", "title": "SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/siliconflow-vs-xai-api.json", "title": "SiliconFlow vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/siliconflow-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow.json", "title": "Nemotron 3.5 Super VL vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow" } ], "panel": null, "scores": [ { "by": 7, "edge": "muse-ember", "key": "reliability", "muse-ember": 40, "name": "Reliability", "siliconflow": 33, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 5, "edge": "siliconflow", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "siliconflow": 65, "weight": 13 }, { "by": 12, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "siliconflow": 60, "weight": 13 }, { "by": 25, "edge": "muse-ember", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "siliconflow": 39, "weight": 14 }, { "by": 15, "edge": "siliconflow", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "siliconflow": 35, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 9, "edge": "muse-ember", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "siliconflow": 37, "weight": 7 }, { "by": 1, "edge": "muse-ember", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "siliconflow": 52, "weight": 7 } ], "summary": "Muse Ember scores 51.8 (D) on agent readiness against SiliconFlow's 42.9 (E), and leads in 5 of 7 scored categories. SiliconFlow leads on schema \u0026 documentation and payments \u0026 pricing. 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.", "siliconflow": "Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.min.md" }, "markdown": "Muse Ember scores 51.8 (D) on agent readiness against SiliconFlow's 42.9 (E), and leads in 5 of 7 scored categories. SiliconFlow leads on schema \u0026 documentation and payments \u0026 pricing. 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- SiliconFlow: grade E, 42.9/100, rank #1271 of 1515. Markdown https://www.anchorterminal.com/tools/siliconflow.md · JSON https://www.anchorterminal.com/api/v1/tools/siliconflow.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- Reliability, 40 against 33\n- Agent ergonomics, 72 against 60\n- Security \u0026 auth, 64 against 39\n- Maintenance \u0026 community, 46 against 37\n\nAlso in its favour:\n- No incidents deducted, where SiliconFlow loses 3 points for them\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n### SiliconFlow (E)\n\nGood for: Agents that want recent open-weight chat models, plus image, video and speech, behind one OpenAI-style key at low per-token prices, and can live without a status page or SLA.\n\nAhead on:\n- Schema \u0026 documentation, 65 against 60\n- Payments \u0026 pricing, 35 against 20\n\nWatch for: No status page, incident history or SLA was found, and the terms disclaim any uptime or availability commitment\n\n\n## Why the edge\n\n### Security \u0026 auth: Muse Ember +25\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- SiliconFlow (39): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Bearer keys created in the console. No scopes, expiry, per-key limits or rotation are documented, and deletion of a key was not confirmed because the console was not read (15 of 30). The terms say Interaction Data is used only to supply the service and is not used or disclosed without authorisation. No statement on model training was found (12 of 20). The terms say there is no obligation to store Interaction Data. No retention period or zero-retention option is stated, and clause 5.11 allows communications to be recorded and passed to the authorities (6 of 15). The release note of 26 March 2026 describes usage and cost breakdowns and invoice export, and the pricing FAQ says monthly spending limits can be set. No audit log was found (6 of 15). No security.txt (404), disclosure policy, bug bounty or named certification was found on the pages read. The product introduction says the service complies with industry standards and names none (0 of 20). 15+12+6+6+0 = 39.\n\n### Payments \u0026 pricing: SiliconFlow +15\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- SiliconFlow (35): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. No machine payment protocol (0). Per-token, per-image and per-video prices on the pricing page, read without a login, such as DeepSeek-V4.1-Flash at $0.15 in and $0.60 out per 1M tokens (20). $1 in free credits for a new account. Whether a card is needed was not established, so this line takes 15 of 20. A person signs up in a browser with a Google or GitHub account, and no key API was found (0). 0+20+15+0 = 35.\n\n### Agent ergonomics: Muse Ember +12\n\n- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n- SiliconFlow (60): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). OpenAI-style `tools` with up to 128 functions, and each model page says whether Tools are supported. No `tool_choice` field is in the chat schema and the guide is out of date (12 of 20). `response_format` of `json_object`. The DeepSeek-V4.1-Flash page says Structured Outputs are not supported (7 of 15). Cached input prices are published per model. No caching guide was found (8 of 15). Context listed as 1049K tokens on eleven chat models on the home page, with output up to 393K (14 of 15). `/files` and `/batches` are in the OpenAPI file for chat completions only, with no guide in the English index, no batch price and a window description that contradicts itself (5 of 10). No official SDK. The OpenAI and Anthropic clients work with a changed base URL (6 of 10). Errors carry an HTTP status, a numeric code and a message. No list of codes was found (8 of 15). 12+7+8+14+5+6+8 = 60.\n\n### Maintenance \u0026 community: Muse Ember +9\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- SiliconFlow (37): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Model reading, per the 9 October check, not re-read on 5 November. Hy4-preview is listed as released on 14 September 2026, 52 days before this check, and no later model or API change was recorded; blog post dates record no API change (20). No notice period is stated. Six removal notices from 31 December 2025 to 11 June 2026 are dated the day of removal, and two in March 2026 gave six and seven days (2 of 12). The release notes record about 60 model ids removed in ten notices over the last twelve months (2 of 8). The release notes stop at 11 June 2026 while nine chat models were added afterwards. Support is by email and Discord (6 of 15). Replies in the support channels were not examined (2 of 10). No official SDK. The `langchain-siliconflow` repository in the vendor's GitHub organisation was last changed on 12 December 2025 (version 0.2.3), confirmed from a clone on 5 November (3 of 15). The API description and two guides are out of date (2 of 10). 20+2+2+6+2+3+2 = 37.\n\n### Reliability: Muse Ember +7\n\n- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.\n- SiliconFlow (33): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Hosted reading. No status page is linked from the site, the docs index or any page read (0). With no page there is no incident record to read (5 of 30). Rate limits are published with numbers by spend tier, from 1,000 requests and 40,000 tokens a minute at L0 to 10,000 and 2,000,000 at L5, on a page served only in Chinese, with per-model figures left to the console (12 of 15). The 429 body is documented and names the limit reached, and the docs say to wait and retry. No `Retry-After` header, backoff figures or idempotency guidance were found (6 of 15). No SLA found. Clause 4.2 of the terms disclaims any uptime or availability commitment (0). The serverless API is generally available, and `/systemone` is marked alpha (10). 0+5+12+6+0+10 = 33.\n\n### Schema \u0026 documentation: SiliconFlow +5\n\n- Muse Ember (60): Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.\n- SiliconFlow (65): SiliconFlow's Terms of Use bar spiders, robots, scrapers and crawlers on the Platform, so its sites were not re-read on 5 November. Per the 9 October check, not re-read on 5 November. Model reading. Public OpenAPI 3.0 file with 22 operations and 102 schemas, linked from `llms.txt` under the Chinese path only. Its chat model enum of 71 ids includes removed models and omits every model added since July 2026 (19 of 25). `llms.txt` and a Markdown copy of each docs page (10). Guides for function calling, JSON mode, reasoning, prefix and FIM completion. The function calling guide lists only models the release notes show as removed, and its examples use DeepSeek-V2.5 (10 of 20). Typed parameters with 104 enums and required fields. `response_format` is typed as a bare string with an example (10 of 15). Curl and Python examples, an error page with seven HTTP codes and one body code, and 400, 401, 404, 429, 503 and 504 responses in the file (9 of 15). The file's version reads 1.0.0. The release notes are dated, stop at 11 June 2026 and since March 2026 record only model removals (7 of 15). 19+10+10+10+9+7 = 65.\n\n## Score by category\n\n| Category | Weight | Muse Ember | SiliconFlow | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 33 | Muse Ember +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 65 | SiliconFlow +5 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 60 | Muse Ember +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 39 | Muse Ember +25 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 35 | SiliconFlow +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 37 | Muse Ember +9 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 52 | Muse Ember +1 |\n| Negative events | ≤15 | 0 | -3 | |\n| **Total** | | **51.8 · D** | **42.9 · E** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | SiliconFlow |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | SiliconFlow Labs Pte. Ltd. |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.siliconflow.com/v1` |\n| Transports | | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\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 | Proprietary service under the SiliconFlow Terms of Use |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-09-14 |\n| Terms last updated | 2026-10-02 | no date given |\n| Privacy policy last updated | couldn't be read | no date given |\n| Customer content may train models | yes | not found in the text |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | yes |\n| Arbitration or class-action waiver | yes | yes |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Muse Ember\n\nNo score change since it was listed.\n\n### SiliconFlow\n\n- -3.9 on 5 November 2026 (D to E): correction, time\n- -0.2 on 28 October 2026: correction\n- +0.3 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**SiliconFlow.** Per-token prices for every listed model are public, and one key reaches chat, embeddings, reranking, image, video and speech through OpenAI-style and Anthropic-style routes. No status page, SLA, security page or official SDK was found, release notes stop at 11 June 2026, and several model removals are dated the same day as their notice.\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### SiliconFlow\n\n1. Set the OpenAI client's base URL to `https://api.siliconflow.com/v1`, or the Anthropic client's to `https://api.siliconflow.com/`, and send the key as a Bearer token\n2. Take model ids from the model pages or `GET /v1/models`, not from the OpenAPI enum or the function calling guide, which list removed models\n3. Check the `model` field of each response. On 11 June 2026 traffic for GLM-5 and Kimi-K2.5 was routed to successor models\n4. Use `response_format` of `json_object` only where the model page says JSON Mode is supported, and keep `max_tokens` about 10,000 below the context length\n5. Set the Claude Code environment variables by hand. The automated route pipes a script from an Amazon S3 bucket into bash\n\n## Questions\n\n### Which is better for AI agents, Muse Ember or SiliconFlow?\n\nMuse Ember scores 51.8 (D) on agent readiness against SiliconFlow's 42.9 (E), and leads in 5 of 7 scored categories. SiliconFlow leads on schema \u0026 documentation and payments \u0026 pricing.\n\n### Do Muse Ember and SiliconFlow need an API key?\n\nBoth need an API key.\n\n### Can an agent call Muse Ember and SiliconFlow without installing anything?\n\nYes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and SiliconFlow at https://api.siliconflow.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"siliconflow\"}`. From a terminal: `anchor compare muse-ember siliconflow`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-ember.json and https://www.anchorterminal.com/api/v1/tools/siliconflow.json\n\n## Other comparisons with Muse Ember or SiliconFlow\n\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Amazon Bedrock vs SiliconFlow](https://www.anchorterminal.com/compare/amazon-bedrock-vs-siliconflow.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Claude API vs SiliconFlow](https://www.anchorterminal.com/compare/anthropic-api-vs-siliconflow.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Antseed vs SiliconFlow](https://www.anchorterminal.com/compare/antseed-vs-siliconflow.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs SiliconFlow](https://www.anchorterminal.com/compare/azure-openai-vs-siliconflow.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [BlockRun.AI vs SiliconFlow](https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Cerebras Inference vs SiliconFlow](https://www.anchorterminal.com/compare/cerebras-inference-vs-siliconflow.md)\n- [Cloudflare AI Gateway vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.md)\n- [Cloudflare AI Gateway vs SiliconFlow](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-siliconflow.md)\n- [Cloudflare Workers AI vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.md)\n- [Cloudflare Workers AI vs SiliconFlow](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-siliconflow.md)\n- [Cohere Chat API (Command models) vs Muse Ember](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.md)\n- [Cohere Chat API (Command models) vs SiliconFlow](https://www.anchorterminal.com/compare/cohere-chat-vs-siliconflow.md)\n- [DeepInfra vs Muse Ember](https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md)\n- [DeepInfra vs SiliconFlow](https://www.anchorterminal.com/compare/deepinfra-vs-siliconflow.md)\n- [DeepSeek API vs Muse Ember](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.md)\n- [DeepSeek API vs SiliconFlow](https://www.anchorterminal.com/compare/deepseek-api-vs-siliconflow.md)\n- [Eden AI vs Muse Ember](https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.md)\n- [Eden AI vs SiliconFlow](https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.md)\n- [Glimpse vs Muse Ember](https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.md)\n- [Glimpse vs SiliconFlow](https://www.anchorterminal.com/compare/elorian-glimpse-vs-siliconflow.md)\n- [Fireworks AI Inference vs Muse Ember](https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.md)\n- [Fireworks AI Inference vs SiliconFlow](https://www.anchorterminal.com/compare/fireworks-inference-vs-siliconflow.md)\n- [Gemini Developer API vs Muse Ember](https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.md)\n- [Gemini Developer API vs SiliconFlow](https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.md)\n- [GroqCloud vs Muse Ember](https://www.anchorterminal.com/compare/groq-vs-muse-ember.md)\n- [GroqCloud vs SiliconFlow](https://www.anchorterminal.com/compare/groq-vs-siliconflow.md)\n- [Hugging Face Inference Providers vs Muse Ember](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.md)\n- [Hugging Face Inference Providers vs SiliconFlow](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-siliconflow.md)\n- [Inkling 2 Small vs Muse Ember](https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.md)\n- [Inkling 2 Small vs SiliconFlow](https://www.anchorterminal.com/compare/inkling-2-small-vs-siliconflow.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Jais 3 vs SiliconFlow](https://www.anchorterminal.com/compare/jais-3-vs-siliconflow.md)\n- [Meta Model API vs Muse Ember](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md)\n- [Meta Model API vs SiliconFlow](https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [MiniMax API vs SiliconFlow](https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Mistral AI API vs SiliconFlow](https://www.anchorterminal.com/compare/mistral-api-vs-siliconflow.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Ember vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.md)\n- [Muse Ember vs Novita AI](https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.md)\n- [Muse Ember vs OpenAI API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism Inference](https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.md)\n- [Muse Ember vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.md)\n- [Muse Ember vs Beam 1.0](https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md)\n- [Muse Ember vs RouterPlus](https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.md)\n- [Muse Ember vs SambaCloud](https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md)\n- [Muse Ember vs Simplismart](https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md)\n- [Muse Ember vs StepFun API](https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.md)\n- [Muse Ember vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.md)\n- [Muse Ember vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.md)\n- [Muse Ember vs Together AI Inference](https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.md)\n- [Muse Ember vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Ember vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.md)\n- [Muse Spark Open vs SiliconFlow](https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.md)\n- [Novita AI vs SiliconFlow](https://www.anchorterminal.com/compare/novita-ai-vs-siliconflow.md)\n- [OpenAI API vs SiliconFlow](https://www.anchorterminal.com/compare/openai-api-vs-siliconflow.md)\n- [OpenRouter vs SiliconFlow](https://www.anchorterminal.com/compare/openrouter-vs-siliconflow.md)\n- [Prism Inference vs SiliconFlow](https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow.md)\n- [Router.com (Ramp Router) vs SiliconFlow](https://www.anchorterminal.com/compare/ramp-router-vs-siliconflow.md)\n- [Beam 1.0 vs SiliconFlow](https://www.anchorterminal.com/compare/reflection-beam-vs-siliconflow.md)\n- [RouterPlus vs SiliconFlow](https://www.anchorterminal.com/compare/routerplus-vs-siliconflow.md)\n- [SambaCloud vs SiliconFlow](https://www.anchorterminal.com/compare/sambanova-vs-siliconflow.md)\n- [SiliconFlow vs Simplismart](https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.md)\n- [SiliconFlow vs StepFun API](https://www.anchorterminal.com/compare/siliconflow-vs-stepfun-api.md)\n- [SiliconFlow vs SWE-2 Flash](https://www.anchorterminal.com/compare/siliconflow-vs-swe-2-flash.md)\n- [SiliconFlow vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/siliconflow-vs-tencent-tokenhub.md)\n- [SiliconFlow vs Together AI Inference](https://www.anchorterminal.com/compare/siliconflow-vs-together-inference.md)\n- [SiliconFlow vs Vercel AI Gateway](https://www.anchorterminal.com/compare/siliconflow-vs-vercel-ai-gateway.md)\n- [SiliconFlow vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/siliconflow-vs-vertex-ai.md)\n- [SiliconFlow vs xAI API (Grok)](https://www.anchorterminal.com/compare/siliconflow-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs SiliconFlow](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-siliconflow.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 SiliconFlow", "url": "" } ], "description": "Muse Ember scores 51.8 (D) to SiliconFlow's 42.9 (E) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Muse Ember D 51.8", "SiliconFlow E 42.9", "scores" ], "h1": "Muse Ember vs SiliconFlow", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/muse-ember-vs-siliconflow", "published": "2026-10-01", "section": "tools", "title": "Muse Ember vs SiliconFlow for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-09", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, "tokens": { "markdown": 6550, "slim": 1080 }, "version": 1 }