{ "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": "SambaCloud scores 64.9 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics.", "b": { "slug": "sambanova", "name": "SambaCloud", "vendor": "SambaNova Systems, Inc.", "vendorUrl": "https://sambanova.ai", "kind": "model", "category": "inference", "summary": "SambaCloud is SambaNova's hosted inference API for open-weight models running on its own RDU processors. It answers OpenAI-style chat, completions and Responses calls and Anthropic-style Messages calls, with Python and TypeScript SDKs.", "url": "https://www.anchorterminal.com/tools/sambanova", "markdownUrl": "https://www.anchorterminal.com/tools/sambanova.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/sambanova.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/sambanova.json", "dns": "sambanova.grade.anchorterminal.com", "repo": "https://github.com/sambanova/sambanova-python", "license": "Proprietary service under the SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0", "transports": [ "http" ], "remoteUrl": "https://api.sambanova.ai/v1", "packages": [ { "registry": "pypi", "name": "sambanova" }, { "registry": "npm", "name": "sambanova" } ], "auth": "api-key", "authNotes": "Self-serve Bearer key from the SambaCloud console at https://cloud.sambanova.ai/apis after a browser sign-up. The Messages routes also take the same key in `x-api-key`. Up to 25 keys per user, shown once, with no scopes.", "pricing": "freemium", "pricingNotes": "Free tier with no payment method, at 20 requests a minute, 20 requests a day and 200,000 tokens a day per model. Linking a card moves the account to the Developer tier, billed per token through Stripe, from $0.22 in and $0.59 out per 1M tokens on gpt-oss-120b (https://cloud.sambanova.ai/plans/pricing, checked 2026-10-08).", "priceSummary": "from $0.22 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI document or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 2, "npmWeekly": 76, "pypiWeekly": 6129, "asOf": "2026-10-08" }, "docsUrl": "https://docs.sambanova.ai/docs/en/get-started/overview", "rateLimitsUrl": "https://docs.sambanova.ai/docs/en/models/rate-limits", "llmsTxt": "https://docs.sambanova.ai/docs/llms.txt", "openapi": "https://raw.githubusercontent.com/sambanova/sambanova-inference-api-spec/refs/heads/main/openapi.documented.json", "capabilities": [ "inference.llm", "inference.open-weights", "inference.fast" ], "tags": [ "hosted", "model", "open-weights", "free-tier", "no-card", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "python", "typescript", "status-page", "soc2" ], "lastRelease": "2026-10-27", "graded": true, "anchor": { "graded": true, "score": 64.9, "grade": "B", "agentReady": false, "rank": 446, "ranked": true, "rankOf": 1515, "categoryRank": 18, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 77, "payments": 40, "reliability": 85, "schema": 81, "security": 61, "transparency": 73 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -5, "negativeNotes": [ "2026-10-08. The models and rate-limit pages listed `MiniMax-M2.7` as a production model a week after its 1 October removal from SambaCloud, and the July 2026 release notes say `Mistral-Large-3-675B-Instruct-2512` was promoted to production. Neither was on the pricing page or in `/v1/models`, and the deprecations page has no entry for Mistral Large 3. Two points, since a keyed call was not made to confirm the ids fail; the docs couldn't be re-read on 1 November (https://docs.sambanova.ai/docs/en/models/sambacloud-models, https://api.sambanova.ai/v1/models).", "2026-10-27, -3. Python SDK 1.14.0 and TypeScript SDK 1.11.0, 1.x minor releases filed under `Features`, removed the documented `do_sample` parameter from the create methods and deleted the audio transcription and translation streaming types, so existing Python callers passing either now get a TypeError. Neither changelog, README nor commit gives notice, and 1.13.0 carried no deprecation warning. Read as box-api's unannounced breaking changes in minor SDK releases, the two packages folded into one term. The docs' release notes and deprecations page couldn't be read on 1 November (bot check); a dated notice there would lift it (https://github.com/sambanova/sambanova-python/commit/8b7cc8b44a82726ff1e759c65f4541adbebb464e, https://github.com/sambanova/sambanova-typescript/commit/128023023eb6ec3b63cabe1c45f0941eb9ba4b8d)" ], "verdict": "Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms.", "bestFor": "Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.", "strengths": [ "Public OpenAPI 3.1.1 document for all 10 operations, plus `llms.txt` and a Markdown copy of every docs page", "Per-token prices for every model on a pricing page that needs no login, and `GET /v1/models` answered without a key on 8 October 2026", "Free tier with no payment method at 20 requests a minute, 20 requests a day and 200,000 tokens a day per model", "One key works with the OpenAI client, the Anthropic client (`/v1/messages`, `x-api-key`) and SambaNova's own SDKs", "Status page with a component per model and no incident posted since 25 June 2026" ], "weaknesses": [ "Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026", "The docs listed `MiniMax-M2.7` as a production model on 8 October 2026, a week after it left SambaCloud", "`strict: true` on a JSON schema is accepted and has no effect, and the July 2026 notes record failing structured output on DeepSeek-V3.2", "Keys carry no scopes or project limits, and no key rotation guidance was found in the reviewed documentation", "The privacy policy dates from May 2023, and the product page says prompts are never collected while the DPA keeps them up to 30 days" ], "agentNotes": [ "Call `GET https://api.sambanova.ai/v1/models` at start-up and use only ids it returns. The docs and status page have named models the API no longer serves", "Read `x-ratelimit-remaining-requests` and `x-ratelimit-remaining-requests-day` on every response. The free tier allows 20 requests a day per model", "Treat 429 `queue_full` and 503 `maintenance` as retryable after a delay, and 410 `model_deprecated` as a signal to change model", "Validate JSON output yourself. Schema enforcement is best effort and `strict: true` changes nothing", "Drop `do_sample` before moving to Python SDK 1.14.0 or TypeScript 1.11.0. Those releases removed it" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.9 } ], "editorialScores": { "ergonomics": 67, "maintenance": 77, "payments": 40, "reliability": 85, "schema": 81, "security": 61, "transparency": 63 }, "provenanceScore": 82, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install sambanova # or: npm install sambanova", "http": "curl https://api.sambanova.ai/v1/chat/completions \\\n -H \"Authorization: Bearer $SAMBANOVA_API_KEY\" -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"gpt-oss-120b\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/sambanova" }, "area": "models", "provenance": { "legalEntity": "SambaNova Systems, Inc.", "domain": "sambanova.ai", "domainRegistered": "2017-12-18", "endpointOnVendorDomain": true, "terms": "https://sambanova.ai/cloud-end-user-license-agreement", "privacy": "https://sambanova.ai/privacy-policy", "statusPage": "https://status.sambanova.ai", "changelog": "https://docs.sambanova.ai/docs/en/release-notes/sambacloud", "securityTxt": "none", "checked": "2026-11-01", "notes": [ "The SambaCloud Terms of Service name SambaNova Systems, Inc., a Delaware corporation at 2460 N. First St, Suite #100, San Jose, CA 95131, and define the Service as the SambaCloud platform. The page carries no date. The legal index says it was last updated on 5 February 2026.", "The privacy policy is the company's only one. Its last revision is dated 27 May 2023, it covers the website, communications and related services, and it does not name SambaCloud or API inputs.", "A Data Processing Addendum (undated, with a sub-processor list) is linked from https://sambanova.ai/legal-agreements on 1 November 2026. No SLA or acceptable use policy is linked.", "https://sambanova.ai/.well-known/security.txt returns 404 (1 November 2026).", "RDAP for sambanova.ai gives a registration date of 2017-12-18.", "The API answers at api.sambanova.ai and the console at cloud.sambanova.ai.", "docs.sambanova.ai, which holds the changelog, answered robots.txt with a bot check on 1 November 2026; the changelog URL is kept from 8 October." ], "score": 82 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/sambanova.json", "live": { "slug": "sambanova", "probe": { "target": "https://api.sambanova.ai/v1", "method": "get", "lastAt": "2026-11-09T20:50:11.848620827Z", "lastOk": true, "lastStatus": 405, "lastMs": 523, "authRequired": false, "uptime24h": 100, "uptime30d": 99.65, "p50ms24h": 432, "p95ms24h": 1041, "samples24h": 244, "samples30d": 7800, "days": [ { "date": "2026-10-09", "probes": 169, "ok": 169 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 244 }, { "date": "2026-10-13", "probes": 248, "ok": 247 }, { "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": 252 }, { "date": "2026-10-18", "probes": 256, "ok": 254 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 250 }, { "date": "2026-10-25", "probes": 250, "ok": 249 }, { "date": "2026-10-26", "probes": 252, "ok": 251 }, { "date": "2026-10-27", "probes": 249, "ok": 248 }, { "date": "2026-10-28", "probes": 250, "ok": 249 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 247 }, { "date": "2026-10-31", "probes": 251, "ok": 246 }, { "date": "2026-11-01", "probes": 249, "ok": 248 }, { "date": "2026-11-02", "probes": 250, "ok": 247 }, { "date": "2026-11-03", "probes": 248, "ok": 247 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 238 }, { "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" }, "vendorStatus": { "page": "https://status.sambanova.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-09T20:46:59.137356979Z" }, "versions": [ { "registry": "github", "name": "sambanova/sambanova-python", "version": "v1.14.0", "released": "2026-10-27", "seenAt": "2026-11-09T02:57:30.894078116Z" }, { "registry": "npm", "name": "sambanova", "version": "1.11.0", "seenAt": "2026-11-09T02:57:29.201947499Z" }, { "registry": "pypi", "name": "sambanova", "version": "1.14.0", "released": "2026-10-27", "seenAt": "2026-11-09T02:57:28.885637003Z" } ], "githubStars": 2, "npmWeekly": 182, "pypiWeekly": 7087, "securityTxt": { "url": "https://sambanova.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:07:00.425625722Z" }, "llmsTxt": { "url": "https://docs.sambanova.ai/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:54.388283477Z" }, "domain": { "domain": "sambanova.ai", "registered": "2017-12-18", "source": "https://rdap.identitydigital.services/rdap/domain/sambanova.ai", "checkedAt": "2026-11-08T14:52:17.293765489Z" }, "pages": [ { "url": "https://docs.sambanova.ai/docs/en/release-notes/sambacloud", "kind": "changelog", "status": 200, "checkedAt": "2026-11-09T15:38:57.369293334Z", "changedAt": "2026-11-07T14:07:53.11787435Z", "fingerprint": "639e478e95cf" }, { "url": "https://docs.sambanova.ai/docs/en/models/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:38:54.992942597Z", "changedAt": "2026-11-07T14:07:50.629389308Z", "fingerprint": "c32bdc3792a4" }, { "url": "https://cloud.sambanova.ai/plans/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-09T15:32:36.176744694Z", "changedAt": "2026-11-04T08:16:38.36121287Z", "fingerprint": "e3fafef6d55a" }, { "url": "https://sambanova.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-09T15:49:54.498426818Z", "changedAt": "2026-10-31T06:20:00.89176957Z", "fingerprint": "c3c533d959fb" }, { "url": "https://sambanova.ai/cloud-end-user-license-agreement", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:49:52.398948709Z", "changedAt": "2026-10-31T06:19:58.778199001Z", "fingerprint": "3a3a2cdc3ec3" } ], "github": { "repo": "sambanova/sambanova-python", "checkedAt": "2026-11-09T10:59:07.519511812Z", "stars": 2, "archived": false, "issuesEnabled": false, "pushedAt": "2026-10-27T18:08:31Z", "openIssues": 0, "openPRs": 0, "issuesOpened90d": 0, "issuesClosed90d": 0, "prsMerged90d": 5, "ci": { "branch": "main", "runs": 50, "success": 28, "failure": 0, "other": 22, "latest": "skipped", "latestAt": "2026-10-27T18:08:25Z" } }, "updatedAt": "2026-11-09T20:50:11.848620827Z" }, "streak": { "days": 31, "from": "2026-10-09", "sinceWatching": true, "last": { "kind": "shutdown", "date": "2026-10-01", "what": "`MiniMax-M2.7` leaves SambaCloud and stays on SambaStack and SambaManaged only. Move to `MiniMax-M3`", "source": "https://docs.sambanova.ai/docs/en/models/deprecations" }, "best": 31 } }, "edges": [ { "by": 45, "category": "reliability", "leads": "sambanova", "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": "Hosted reading. Status page at status.sambanova.ai on Statuspage, with components for the API gateway, the Playground and each model, read from the page itself on 1 November (20). Its history.atom feed, read on 1 November (the 8 October reading came from the /api/ path, which robots.txt disallows, and is struck), has nothing newer than an 8-minute Playground login fault on 25 June 2026, so nothing is posted for the window from 3 August (30). Limits published per model and tier, 20 requests a minute and 20 a day on the free tier, 60 and 12,000 on the Developer tier, per the 8 October check, not re-read on 1 November (15). `x-ratelimit-*` headers on every response, the error table says to retry `queue_full` after a delay, and the SDKs retry 429 and server errors twice with backoff, per the 8 October check. No `Retry-After` header was found in the docs or the OpenAPI document (10 of 15). No SLA found on the legal page, re-read on 1 November, or in the terms (0). Three production models are generally available, the rest previews, per the 8 October check (10). 20+30+15+10+0+10 = 85." }, "scores": { "a": 40, "b": 85 } }, { "by": 31, "category": "maintenance", "leads": "sambanova", "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": "Model reading. The OpenAPI document and both SDKs (Python 1.14.0, TypeScript 1.11.0) were updated on 27 October 2026, adding `MiniMax-M3.1-Flash`, which is on the pricing page on 1 November (30). Production models get a notice of at least two to three weeks. The recorded removals to June had 14 days or less, and MiniMax-M2.7's removal from SambaCloud on 1 October was announced on 1 September (30 days), per the 27 October read of the deprecations page (5 of 12). One removal in the window from 3 August (MiniMax-M2.7) after 13 model ids left between 9 March and 9 June 2026 (4 of 8). The SambaCloud release notes stopped at 7 July 2026 on 8 October and couldn't be re-read. The community forum had staff replies, per the 8 October check (10 of 15). The Python SDK repository holds release pull requests only, so replies could not be judged (5 of 10). Python 1.14.0 and TypeScript 1.11.0, both 27 October 2026 (15). CI, release-please, a lock file and gitleaks scanning (8 of 10). 30+5+4+10+5+15+8 = 77." }, "scores": { "a": 46, "b": 77 } }, { "by": 21, "category": "schema", "leads": "sambanova", "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": "Public OpenAPI 3.1.1 document, still labelled version 1.2.0 though its content changed on 27 October 2026, with 10 operations and 165 schemas (25). `llms.txt` at docs.sambanova.ai/docs/llms.txt and a Markdown copy of each page, per the 8 October check (docs.sambanova.ai answered robots.txt with a bot check on 1 November, so it wasn't read) (10). 718 descriptions in the document, with pages for function calling, Responses, Messages and caching. When not to use a route is rarely stated (15 of 20). Typed parameters with 150 enums, and request bodies now declare `additionalProperties: false`, but JSON schema output is best effort and `strict: true` has no effect (10 of 15). Code examples in Python and TypeScript, and an errors page with 13 codes and a recovery suggestion for each, per the 8 October check (13 of 15). The spec is versioned in its repository, but the version label didn't move with the 27 October changes, the newest SambaCloud release note seen is 7 July 2026, and the models page disagreed with `/v1/models` on 8 October (8 of 15). 25+10+15+10+13+8 = 81." }, "scores": { "a": 60, "b": 81 } }, { "by": 20, "category": "payments", "leads": "sambanova", "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": "No machine payment protocol (0). Per-token prices on the pricing page, read without a login on 1 November, such as gpt-oss-120b at $0.22 in and $0.59 out per 1M tokens (20). Free tier with no payment method, per the 8 October check (20). A person signs up in the console to get a key (0)." }, "scores": { "a": 20, "b": 40 } }, { "by": 20, "category": "transparency", "leads": "sambanova", "name": "Transparency \u0026 trust", "reasons": { "a": "Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.", "b": "Closed service with clear terms that name the entity and the governing law, and Apache-2.0 SDKs and OpenAPI document (15). The privacy policy is still dated 27 May 2023 and doesn't name SambaCloud or API inputs. A Data Processing Addendum, part of the Terms of Service, is now linked from the legal page (undated). It limits processing of customer personal data to the agreement, its Annex I says prompts, inputs and completions are processed transiently and kept no more than 30 days for security, fraud and abuse monitoring, then deleted, and it deletes customer personal data within 30 days of termination and from backups within 90. The product page's claim, re-read on 1 November, that SambaCloud 'never sees or collects any of your data or user prompts' sits beside that 30-day retention and terms that allow storing content and using query logs (16 of 30, as novita-ai's stated retention periods, below mistral-api's 18 where the DPA and other pages agree). A deprecations page with notice and removal dates back to April 2025, recording MiniMax-M2.7's 1 October removal with a 1 September notice per the 27 October read, though nothing for Mistral Large 3, per the 8 October check (14 of 20). The DPA's Annex III names 24 sub-processors with purpose and location (USA, Japan, Germany, the United Kingdom, Australia) and promises 30 days' notice of changes, though it doesn't say where inference runs for a given request (18 of 20). 15+16+14+18 = 63." }, "scores": { "a": 53, "b": 73 } }, { "by": 5, "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": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` to force a function, on six listed models, and a July 2026 fix for tool calls that were buffered when streaming (17 of 20). `json_object` and `json_schema` output, best effort only, with a known failure on DeepSeek-V3.2 recorded in July 2026 (8 of 15). Automatic prompt caching on the MiniMax models only, with cached input on MiniMax-M3.1-Flash at a tenth of the input price on the 1 November pricing page, a 4,096-token minimum and no guaranteed persistence (10 of 15). 131,072-token context on four of six models, 1M on the MiniMax-M3 preview, and output capped at 7,168 tokens on DeepSeek and 3,072 on Llama 3.3 70B (8 of 15). No batch API found in the docs index or the OpenAPI document (0 of 10). Official SDKs for Python and TypeScript, and the OpenAI and Anthropic clients work with a changed base URL (10). Errors carry `type`, `param`, `code` and `request_id` (14 of 15)." }, "scores": { "a": 72, "b": 67 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "SambaNova Systems, Inc.", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.sambanova.ai/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "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 SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-27", "name": "Last release" }, { "a": "2026-10-02", "b": "no date given", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "2023-05-27", "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": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2 stars, 76 npm/wk, 6.1k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "SambaCloud scores 64.9 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics.", "question": "Which is better for AI agents, Muse Ember or SambaCloud?" }, { "answer": "Both need an API key.", "question": "Do Muse Ember and SambaCloud need an API key?" }, { "answer": "Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and SambaCloud at https://api.sambanova.ai/v1.", "question": "Can an agent call Muse Ember and SambaCloud without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 72 against 67" ], "also": [ "No incidents deducted, where SambaCloud loses 5 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": [ "Reliability, 85 against 40", "Schema \u0026 documentation, 81 against 60", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 77 against 46", "Transparency \u0026 trust, 73 against 53" ], "also": [ "Free to start without a card" ], "goodFor": "Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.", "slug": "sambanova", "watchFor": "Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026" } ], "history": { "muse-ember": null, "sambanova": [ { "causes": [ "evidence", "event" ], "date": "2026-11-01", "delta": -1.5, "from": 66.4, "fromGrade": "B", "to": 64.9, "toGrade": "B" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 66.5, "fromGrade": "B", "to": 66.4, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 66.4, "fromGrade": "B", "to": 66.5, "toGrade": "B" } ] }, "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": 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SambaCloud", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-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-siliconflow.json", "title": "Muse Ember vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.json", "title": "Muse Spark Open vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-sambanova.json", "title": "Novita AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-sambanova.json", "title": "OpenAI API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/openai-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-sambanova.json", "title": "OpenRouter vs SambaCloud", "url": "https://www.anchorterminal.com/compare/openrouter-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/ramp-router-vs-sambanova.json", "title": "Router.com (Ramp Router) vs SambaCloud", "url": "https://www.anchorterminal.com/compare/ramp-router-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-sambanova.json", "title": "Beam 1.0 vs SambaCloud", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/routerplus-vs-sambanova.json", "title": "RouterPlus vs SambaCloud", "url": "https://www.anchorterminal.com/compare/routerplus-vs-sambanova" }, { "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/sambanova-vs-simplismart.json", "title": "SambaCloud vs Simplismart", "url": "https://www.anchorterminal.com/compare/sambanova-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api.json", "title": "SambaCloud vs StepFun API", "url": "https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash.json", "title": "SambaCloud vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub.json", "title": "SambaCloud vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-together-inference.json", "title": "SambaCloud vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/sambanova-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway.json", "title": "SambaCloud vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.json", "title": "SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/sambanova-vs-xai-api.json", "title": "SambaCloud vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/sambanova-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-sambanova.json", "title": "Nemotron 3.5 Super VL vs SambaCloud", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.json", "title": "Prism Inference vs SambaCloud", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova" } ], "panel": null, "scores": [ { "by": 45, "edge": "sambanova", "key": "reliability", "muse-ember": 40, "name": "Reliability", "sambanova": 85, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 21, "edge": "sambanova", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "sambanova": 81, "weight": 13 }, { "by": 5, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "sambanova": 67, "weight": 13 }, { "by": 3, "edge": "muse-ember", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "sambanova": 61, "weight": 14 }, { "by": 20, "edge": "sambanova", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "sambanova": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 31, "edge": "sambanova", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "sambanova": 77, "weight": 7 }, { "by": 20, "edge": "sambanova", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "sambanova": 73, "weight": 7 } ], "summary": "SambaCloud scores 64.9 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics. 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.", "sambanova": "Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.min.md" }, "markdown": "SambaCloud scores 64.9 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics. 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- SambaCloud: grade B, 64.9/100, rank #446 of 1515. Markdown https://www.anchorterminal.com/tools/sambanova.md · JSON https://www.anchorterminal.com/api/v1/tools/sambanova.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- Agent ergonomics, 72 against 67\n\nAlso in its favour:\n- No incidents deducted, where SambaCloud loses 5 points for them\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n### SambaCloud (B)\n\nGood for: Agents that want open-weight models behind an OpenAI or Anthropic client with a free start and prices an agent can read from the API.\n\nAhead on:\n- Reliability, 85 against 40\n- Schema \u0026 documentation, 81 against 60\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 77 against 46\n- Transparency \u0026 trust, 73 against 53\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Production models get a notice of two to three weeks, and 13 model ids were removed between 9 March and 9 June 2026\n\n\n## Why the edge\n\n### Reliability: SambaCloud +45\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- SambaCloud (85): Hosted reading. Status page at status.sambanova.ai on Statuspage, with components for the API gateway, the Playground and each model, read from the page itself on 1 November (20). Its history.atom feed, read on 1 November (the 8 October reading came from the /api/ path, which robots.txt disallows, and is struck), has nothing newer than an 8-minute Playground login fault on 25 June 2026, so nothing is posted for the window from 3 August (30). Limits published per model and tier, 20 requests a minute and 20 a day on the free tier, 60 and 12,000 on the Developer tier, per the 8 October check, not re-read on 1 November (15). `x-ratelimit-*` headers on every response, the error table says to retry `queue_full` after a delay, and the SDKs retry 429 and server errors twice with backoff, per the 8 October check. No `Retry-After` header was found in the docs or the OpenAPI document (10 of 15). No SLA found on the legal page, re-read on 1 November, or in the terms (0). Three production models are generally available, the rest previews, per the 8 October check (10). 20+30+15+10+0+10 = 85.\n\n### Maintenance \u0026 community: SambaCloud +31\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- SambaCloud (77): Model reading. The OpenAPI document and both SDKs (Python 1.14.0, TypeScript 1.11.0) were updated on 27 October 2026, adding `MiniMax-M3.1-Flash`, which is on the pricing page on 1 November (30). Production models get a notice of at least two to three weeks. The recorded removals to June had 14 days or less, and MiniMax-M2.7's removal from SambaCloud on 1 October was announced on 1 September (30 days), per the 27 October read of the deprecations page (5 of 12). One removal in the window from 3 August (MiniMax-M2.7) after 13 model ids left between 9 March and 9 June 2026 (4 of 8). The SambaCloud release notes stopped at 7 July 2026 on 8 October and couldn't be re-read. The community forum had staff replies, per the 8 October check (10 of 15). The Python SDK repository holds release pull requests only, so replies could not be judged (5 of 10). Python 1.14.0 and TypeScript 1.11.0, both 27 October 2026 (15). CI, release-please, a lock file and gitleaks scanning (8 of 10). 30+5+4+10+5+15+8 = 77.\n\n### Schema \u0026 documentation: SambaCloud +21\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- SambaCloud (81): Public OpenAPI 3.1.1 document, still labelled version 1.2.0 though its content changed on 27 October 2026, with 10 operations and 165 schemas (25). `llms.txt` at docs.sambanova.ai/docs/llms.txt and a Markdown copy of each page, per the 8 October check (docs.sambanova.ai answered robots.txt with a bot check on 1 November, so it wasn't read) (10). 718 descriptions in the document, with pages for function calling, Responses, Messages and caching. When not to use a route is rarely stated (15 of 20). Typed parameters with 150 enums, and request bodies now declare `additionalProperties: false`, but JSON schema output is best effort and `strict: true` has no effect (10 of 15). Code examples in Python and TypeScript, and an errors page with 13 codes and a recovery suggestion for each, per the 8 October check (13 of 15). The spec is versioned in its repository, but the version label didn't move with the 27 October changes, the newest SambaCloud release note seen is 7 July 2026, and the models page disagreed with `/v1/models` on 8 October (8 of 15). 25+10+15+10+13+8 = 81.\n\n### Payments \u0026 pricing: SambaCloud +20\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- SambaCloud (40): No machine payment protocol (0). Per-token prices on the pricing page, read without a login on 1 November, such as gpt-oss-120b at $0.22 in and $0.59 out per 1M tokens (20). Free tier with no payment method, per the 8 October check (20). A person signs up in the console to get a key (0).\n\n### Transparency \u0026 trust: SambaCloud +20\n\n- Muse Ember (53): Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.\n- SambaCloud (73): Closed service with clear terms that name the entity and the governing law, and Apache-2.0 SDKs and OpenAPI document (15). The privacy policy is still dated 27 May 2023 and doesn't name SambaCloud or API inputs. A Data Processing Addendum, part of the Terms of Service, is now linked from the legal page (undated). It limits processing of customer personal data to the agreement, its Annex I says prompts, inputs and completions are processed transiently and kept no more than 30 days for security, fraud and abuse monitoring, then deleted, and it deletes customer personal data within 30 days of termination and from backups within 90. The product page's claim, re-read on 1 November, that SambaCloud 'never sees or collects any of your data or user prompts' sits beside that 30-day retention and terms that allow storing content and using query logs (16 of 30, as novita-ai's stated retention periods, below mistral-api's 18 where the DPA and other pages agree). A deprecations page with notice and removal dates back to April 2025, recording MiniMax-M2.7's 1 October removal with a 1 September notice per the 27 October read, though nothing for Mistral Large 3, per the 8 October check (14 of 20). The DPA's Annex III names 24 sub-processors with purpose and location (USA, Japan, Germany, the United Kingdom, Australia) and promises 30 days' notice of changes, though it doesn't say where inference runs for a given request (18 of 20). 15+16+14+18 = 63.\n\n### Agent ergonomics: Muse Ember +5\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- SambaCloud (67): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` to force a function, on six listed models, and a July 2026 fix for tool calls that were buffered when streaming (17 of 20). `json_object` and `json_schema` output, best effort only, with a known failure on DeepSeek-V3.2 recorded in July 2026 (8 of 15). Automatic prompt caching on the MiniMax models only, with cached input on MiniMax-M3.1-Flash at a tenth of the input price on the 1 November pricing page, a 4,096-token minimum and no guaranteed persistence (10 of 15). 131,072-token context on four of six models, 1M on the MiniMax-M3 preview, and output capped at 7,168 tokens on DeepSeek and 3,072 on Llama 3.3 70B (8 of 15). No batch API found in the docs index or the OpenAPI document (0 of 10). Official SDKs for Python and TypeScript, and the OpenAI and Anthropic clients work with a changed base URL (10). Errors carry `type`, `param`, `code` and `request_id` (14 of 15).\n\n## Score by category\n\n| Category | Weight | Muse Ember | SambaCloud | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 85 | SambaCloud +45 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 81 | SambaCloud +21 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 67 | Muse Ember +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 61 | Muse Ember +3 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 40 | SambaCloud +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 77 | SambaCloud +31 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 73 | SambaCloud +20 |\n| Negative events | ≤15 | 0 | -5 | |\n| **Total** | | **51.8 · D** | **64.9 · B** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | SambaCloud |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | SambaNova Systems, Inc. |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.sambanova.ai/v1` |\n| Transports | | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\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 SambaCloud Terms of Service. The SDKs and the OpenAPI document are Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-27 |\n| Terms last updated | 2026-10-02 | no date given |\n| Privacy policy last updated | couldn't be read | 2023-05-27 |\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 | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | not found in the text |\n| Popularity | none | 2 stars, 76 npm/wk, 6.1k PyPI/wk |\n| Agent reviews | none | 3.5/5 (4) |\n\n## How the scores have moved\n\n### Muse Ember\n\nNo score change since it was listed.\n\n### SambaCloud\n\n- -1.5 on 1 November 2026: new evidence, negative event\n- -0.1 on 28 October 2026: correction\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**SambaCloud.** Per-token prices are readable without a login, a free tier needs no card, and no incident has been posted since 25 June 2026. A Data Processing Addendum now names 24 sub-processors. Production models get two to three weeks' notice before removal, 13 model ids left between March and June 2026, and the product page's privacy claim contradicts the terms.\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### SambaCloud\n\n1. Call `GET https://api.sambanova.ai/v1/models` at start-up and use only ids it returns. The docs and status page have named models the API no longer serves\n2. Read `x-ratelimit-remaining-requests` and `x-ratelimit-remaining-requests-day` on every response. The free tier allows 20 requests a day per model\n3. Treat 429 `queue_full` and 503 `maintenance` as retryable after a delay, and 410 `model_deprecated` as a signal to change model\n4. Validate JSON output yourself. Schema enforcement is best effort and `strict: true` changes nothing\n5. Drop `do_sample` before moving to Python SDK 1.14.0 or TypeScript 1.11.0. Those releases removed it\n\n## Questions\n\n### Which is better for AI agents, Muse Ember or SambaCloud?\n\nSambaCloud scores 64.9 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Muse Ember leads on agent ergonomics.\n\n### Do Muse Ember and SambaCloud need an API key?\n\nBoth need an API key.\n\n### Can an agent call Muse Ember and SambaCloud without installing anything?\n\nYes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and SambaCloud at https://api.sambanova.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"sambanova\"}`. From a terminal: `anchor compare muse-ember sambanova`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-ember.json and https://www.anchorterminal.com/api/v1/tools/sambanova.json\n\n## Other comparisons with Muse Ember or SambaCloud\n\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Amazon Bedrock vs SambaCloud](https://www.anchorterminal.com/compare/amazon-bedrock-vs-sambanova.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Claude API vs SambaCloud](https://www.anchorterminal.com/compare/anthropic-api-vs-sambanova.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Antseed vs SambaCloud](https://www.anchorterminal.com/compare/antseed-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/azure-openai-vs-sambanova.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [BlockRun.AI vs SambaCloud](https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Cerebras Inference vs SambaCloud](https://www.anchorterminal.com/compare/cerebras-inference-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-sambanova.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 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SambaCloud](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/inkling-2-small-vs-sambanova.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Jais 3 vs SambaCloud](https://www.anchorterminal.com/compare/jais-3-vs-sambanova.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 SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [MiniMax API vs SambaCloud](https://www.anchorterminal.com/compare/minimax-api-vs-sambanova.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Mistral AI API vs SambaCloud](https://www.anchorterminal.com/compare/mistral-api-vs-sambanova.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 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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 SambaCloud](https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.md)\n- [Novita AI vs SambaCloud](https://www.anchorterminal.com/compare/novita-ai-vs-sambanova.md)\n- [OpenAI API vs SambaCloud](https://www.anchorterminal.com/compare/openai-api-vs-sambanova.md)\n- [OpenRouter vs SambaCloud](https://www.anchorterminal.com/compare/openrouter-vs-sambanova.md)\n- [Router.com (Ramp Router) vs SambaCloud](https://www.anchorterminal.com/compare/ramp-router-vs-sambanova.md)\n- [Beam 1.0 vs SambaCloud](https://www.anchorterminal.com/compare/reflection-beam-vs-sambanova.md)\n- [RouterPlus vs SambaCloud](https://www.anchorterminal.com/compare/routerplus-vs-sambanova.md)\n- [SambaCloud vs SiliconFlow](https://www.anchorterminal.com/compare/sambanova-vs-siliconflow.md)\n- [SambaCloud vs Simplismart](https://www.anchorterminal.com/compare/sambanova-vs-simplismart.md)\n- [SambaCloud vs StepFun API](https://www.anchorterminal.com/compare/sambanova-vs-stepfun-api.md)\n- [SambaCloud vs SWE-2 Flash](https://www.anchorterminal.com/compare/sambanova-vs-swe-2-flash.md)\n- [SambaCloud vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/sambanova-vs-tencent-tokenhub.md)\n- [SambaCloud vs Together AI Inference](https://www.anchorterminal.com/compare/sambanova-vs-together-inference.md)\n- [SambaCloud vs Vercel AI Gateway](https://www.anchorterminal.com/compare/sambanova-vs-vercel-ai-gateway.md)\n- [SambaCloud vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/sambanova-vs-vertex-ai.md)\n- [SambaCloud vs xAI API (Grok)](https://www.anchorterminal.com/compare/sambanova-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs SambaCloud](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-sambanova.md)\n- [Prism Inference vs SambaCloud](https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.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 SambaCloud", "url": "" } ], "description": "SambaCloud scores 64.9 (B) to Muse Ember's 51.8 (D) for llm 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