{ "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 Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.", "b": { "slug": "simplismart", "name": "Simplismart", "vendor": "Verute Technologies Private Limited", "vendorUrl": "https://simplismart.ai", "kind": "http-api", "category": "inference", "summary": "Simplismart is an inference platform for open models. It runs 150+ models on shared pay-per-token endpoints with an OpenAI-compatible API, and handles dedicated GPU deployments, bring-your-own-cloud clusters and fine-tuning jobs.", "url": "https://www.anchorterminal.com/tools/simplismart", "markdownUrl": "https://www.anchorterminal.com/tools/simplismart.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/simplismart.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/simplismart.json", "dns": "simplismart.grade.anchorterminal.com", "license": "Proprietary hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences.", "transports": [ "http" ], "remoteUrl": "https://api.simplismart.live", "packages": [ { "registry": "pypi", "name": "simplismart-sdk" } ], "auth": "api-key", "authNotes": "Bearer API keys made under Settings, API Keys. Each key has a unique name and an expiry chosen at creation; non-expiring keys need a request to support. Keys return 401 after midnight UTC on the expiry date and can be deleted at once. The Python SDK and CLI use a separate Playground token. Organisation roles are Admin, Member and Billing User, and workspaces scope models and deployments.", "pricing": "usage", "pricingNotes": "Shared endpoints are priced per 1M tokens, per image or per audio minute, such as Llama 3.1 8B at $0.13 and DeepSeek-V3 at $0.90 per 1M tokens. Dedicated GPUs run from $1.20 (T4) to $5.20 (H200) per GPU-hour. Prepaid credits at $1 each; new organisations may receive $5 of free credits.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs (llms.txt index, billing, API keys and API reference pages) or on the pricing page, checked 28 October 2026. Credits are bought by card, UPI or netbanking, or billed postpaid.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-28" }, "docsUrl": "https://docs.simplismart.ai", "llmsTxt": "https://docs.simplismart.ai/llms.txt", "openapi": "https://docs.simplismart.ai/openapi-specs/playground/llama-8b.yaml", "capabilities": [ "inference.llm", "inference.open-weights", "speech.stt", "image.generate", "compute.endpoints", "finetune.sft", "finetune.lora", "finetune.preference", "finetune.rl" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "openai-compatible", "openapi", "llms-txt", "python", "dedicated-endpoints", "byoc", "fine-tuning" ], "lastRelease": "2026-10-19", "graded": true, "anchor": { "graded": true, "score": 29.5, "grade": "F", "agentReady": false, "rank": 1429, "ranked": true, "rankOf": 1515, "categoryRank": 43, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 17, "maintenance": 42, "payments": 25, "reliability": 15, "schema": 51, "security": 35, "transparency": 29 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-28" }, "negative": 0, "verdict": "Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data.", "bestFor": "Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.", "strengths": [ "Public per-unit prices without a login, such as Llama 3.1 8B at $0.13 per 1M tokens and Whisper v3 Turbo at $0.0018 per audio minute", "API keys are named, carry an expiry date chosen at creation, return 401 after expiry and can be deleted at once", "Markdown docs and an llms.txt index, with OpenAPI 3.1 files for seven LLMs, Whisper and Flux", "Shared endpoints, dedicated GPU deployments and bring-your-own-cloud clusters sit on one account, with workspaces and organisation roles" ], "weaknesses": [ "No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages", "No status page, incident history or SLA document found; the 99.99% uptime figure is a homepage claim", "Tool calling, structured output, prompt caching and context lengths are not documented for the shared endpoints", "The terms (13 January 2023) and privacy policy cover the website and say nothing about prompt retention or training on API data", "The pricing page lists older models (Llama 3.1, DeepSeek-R1) and omits Gemma 4 31B, the model the quickstart uses" ], "agentNotes": [ "Point an OpenAI-compatible client at https://api.simplismart.live with SIMPLISMART_API_KEY as a Bearer token, and take model ids from the marketplace or the per-model API pages.", "Set a key expiry and rotate before midnight UTC on the expiry date, because an expired key returns 401.", "Cap retries yourself with exponential backoff, because no rate limits, 429 format or retry guidance are published.", "Check that a model supports tools or JSON output with a test call before relying on it, because neither is documented.", "Confirm the per-token price of the exact model in the console before running large jobs, because the public price list is incomplete." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 29.5 } ], "editorialScores": { "ergonomics": 17, "maintenance": 42, "payments": 25, "reliability": 15, "schema": 51, "security": 35, "transparency": 14 }, "provenanceScore": 44, "checked": { "categories": { "ergonomics": "2026-10-28", "maintenance": "2026-10-28", "payments": "2026-10-28", "reliability": "2026-10-28", "schema": "2026-10-28", "security": "2026-10-28", "transparency": "2026-10-28" }, "last": "2026-10-28", "lastMoved": "2026-10-30", "oldest": "2026-10-28", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl 'https://api.simplismart.live/chat/completions' -H 'Content-Type: application/json' -H \"Authorization: Bearer $SIMPLISMART_API_KEY\" -d '{\"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\", \"messages\": [{\"role\": \"user\", \"content\": \"Say hello.\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/simplismart" }, "area": "models", "unitPrices": [ { "item": "Llama 3.1 8B (shared)", "unit": "1m-tokens", "usd": 0.13, "note": "Single per-1M-token rate on the pricing page, checked 28 October 2026; input and output are not priced separately" }, { "item": "Llama 3.3 70B (shared)", "unit": "1m-tokens", "usd": 0.74, "note": "Pricing page, checked 28 October 2026" }, { "item": "DeepSeek-V3 (shared)", "unit": "1m-tokens", "usd": 0.9, "note": "Pricing page, checked 28 October 2026" }, { "item": "Whisper v3 Turbo (shared)", "unit": "audio-minute", "usd": 0.0018, "note": "Pricing page, checked 28 October 2026" }, { "item": "Flux Dev (shared, 1024x1024)", "unit": "image", "usd": 0.03, "note": "Pricing page, checked 28 October 2026" }, { "item": "NVIDIA H100, dedicated", "unit": "gpu-hour", "usd": 5, "note": "Pricing page table order, checked 28 October 2026; a Simplismart article gives $4.00 per hour" } ], "provenance": { "legalEntity": "Verute Technologies Private Limited", "domain": "simplismart.ai", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://simplismart.ai/terms-of-service", "privacy": "https://simplismart.ai/privacy-policy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-28", "notes": [ "The terms (last updated 13 January 2023) and privacy policy (effective 13 January 2023) name Verute Technologies Private Limited, Indian law and an address on Residency Road, Bengaluru. Both define the Service as the simplismart.ai website.", "The inference API answers on api.simplismart.live, a separate domain named in the docs. Domain registration was not checked.", "No status page found. The docs and site link none, and status.simplismart.ai could not be reached from our network.", "No changelog found in the docs sitemap or llms.txt. Model additions appear as blog posts.", "simplismart.ai/.well-known/security.txt answered 404." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/simplismart.json", "live": { "slug": "simplismart", "probe": { "target": "https://api.simplismart.live", "method": "get", "lastAt": "2026-11-09T20:50:14.546768646Z", "lastOk": true, "lastStatus": 405, "lastMs": 56, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 77, "p95ms24h": 154, "samples24h": 244, "samples30d": 3074, "days": [ { "date": "2026-10-28", "probes": 208, "ok": 208 }, { "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": "2026-10-28T00:00:00Z" }, "versions": [ { "registry": "pypi", "name": "simplismart-sdk", "version": "0.1.15", "released": "2026-11-02", "seenAt": "2026-11-09T03:00:59.194656517Z" } ], "pypiWeekly": 191, "securityTxt": { "url": "https://simplismart.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:06:19.325232182Z" }, "llmsTxt": { "url": "https://docs.simplismart.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:58.151305823Z" }, "domain": { "domain": "simplismart.ai", "registered": "2022-11-18", "source": "https://rdap.identitydigital.services/rdap/domain/simplismart.ai", "checkedAt": "2026-11-08T15:13:33.504274943Z" }, "pages": [ { "url": "https://simplismart.ai/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-09T15:50:52.126195512Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "bd2d6d5ffe37" }, { "url": "https://simplismart.ai/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:50:54.15952003Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "94fe8807daf7" } ], "updatedAt": "2026-11-09T20:50:14.546768646Z" }, "streak": { "days": 12, "from": "2026-10-28", "sinceWatching": true, "best": 12 } }, "edges": [ { "by": 55, "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). The LiveKit guide calls the LLM API fully OpenAI-compatible, but no page documents tools or tool_choice, and the OpenAPI files allow only system, user and assistant roles (3 of 20). No structured-output or JSON mode documented (0 of 15). No prompt caching documented for shared endpoints (0 of 15). Context lengths are not stated per model; output is capped at 4,096 tokens in the API files (3 of 15). No batch API for shared endpoints (0 of 10). Inference goes through the OpenAI client; the simplismart-sdk Python package and CLI manage repositories and deployments, not inference, and a community Pipecat package exists (5 of 10). Expired keys return a documented 401, and the SDK exposes status_code, message and payload, but no error codes or bodies are listed for the inference API (6 of 15). 3+0+0+3+0+5+6 = 17." }, "scores": { "a": 72, "b": 17 } }, { "by": 29, "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": "Model reading. Named Bearer keys with an expiry chosen at creation, a 401 after expiry and immediate deletion. No scopes or per-key limits found, keys belong to the organisation, and the SDK uses a separate Playground token (20 of 30). No statement on training on API data in the terms, the privacy policy, the AI policy or the docs (0 of 20). No retention or zero-retention statement for prompts and outputs. The privacy policy covers the website only (0 of 15). The Usage page shows request counts and a per-request timeline per model, and an LLM metrics call returns token counts and timings per request id. A Simplismart article claims exportable audit trails, not documented (8 of 15). The homepage shows GDPR, ISO 27001 v2022 and AICPA SOC 2 badges, and a Simplismart article says it holds a SOC 2 Type 2 report, available on request. No trust centre was reachable, security.txt answered 404, and no disclosure policy or bug bounty was found (7 of 20). 20+0+0+8+7 = 35." }, "scores": { "a": 64, "b": 35 } }, { "by": 25, "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": "Hosted reading. No status page found. The docs and site link none, a search found none, and status.simplismart.ai could not be reached from our network (0). No readable incident history (5). No request or token rate limits are published for shared endpoints. The docs give GPU quotas for training, compilation and private deployments only, 1 H100 and 1 L40 on the Quotas page and 8 of each on the Organisation page (0). No 429 response, Retry-After header or retry guidance found; the OpenAPI files list only 400, 401 and 500 (0). No SLA document found. The homepage claims 99.99% uptime and a Simplismart post calls its autoscaling SLA-backed, but no terms state a commitment, and the terms provide the service as is (0). Shared endpoints carry no beta label, while Muse Spark Open is in beta (10). 0+5+0+0+0+10 = 15." }, "scores": { "a": 40, "b": 15 } }, { "by": 24, "category": "transparency", "leads": "muse-ember", "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. The terms, last updated 13 January 2023, name Verute Technologies Private Limited and Indian law but define the Service as the website, grant Simplismart a licence to use and redistribute posted Content, bar robots and automatic access, and rule out refunds. No API or platform terms were found (8 of 30). The privacy policy, effective 13 January 2023, is a website policy that says personal data is processed in India. It says nothing about prompts, outputs, logs or training, and no DPA, retention period or subprocessor list was found. A Simplismart article claims GDPR and HIPAA support (3 of 30). No deprecation policy or dated removals (0 of 20). Data goes to India per the privacy policy, and an article says workloads are kept in approved regions by scheduling policy. No subprocessor or hosting-provider list (3 of 20). 8+3+0+3 = 14." }, "scores": { "a": 53, "b": 29 } }, { "by": 9, "category": "schema", "leads": "muse-ember", "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": "Model reading. OpenAPI 3.1 files for seven LLMs and 3.0 or 3.1 files for Whisper, Flux, an LLM metrics call and training jobs. They cover a handful of the 150+ models, omit tools and response_format, show a streaming `delta` as the non-streaming response, and several name hosts on s9t.link instead of api.simplismart.live. The Llama 4 Maverick file gives `llama3_1` as its only model id, and /api-reference/openapi.json is the Flux spec (12 of 25). llms.txt and a Markdown copy of each docs page (10). The docs say when to use shared or dedicated endpoints; model pages carry one-line descriptions (10 of 20). Roles are enums, temperature is bounded 0 to 2, max_tokens 1 to 4096, and messages and model are required (10 of 15). curl and OpenAI-client Python examples. Errors are bare 400, 401 and 500 descriptions, plus an SDK example of a 404 payload (7 of 15). No changelog. Every spec reads version 1.0.0 (2 of 15). 12+10+10+10+7+2 = 51." }, "scores": { "a": 60, "b": 51 } }, { "by": 5, "category": "payments", "leads": "simplismart", "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 x402, MPP or L402 in the docs or pricing (0). Per-1M-token, per-image, per-audio-minute and per-GPU-hour prices are public without a login. The list omits current models such as Gemma 4 31B, which the quickstart uses, and doesn't say whether the single token price covers input and output (15 of 20). The billing page says new organisations may receive $5 of free credits; whether a card is needed isn't stated, and we did not sign up (10 of 20). A person signs up in the browser with a work email and a verification code, and makes keys in the console (0). 0+15+10+0 = 25." }, "scores": { "a": 20, "b": 25 } } ], "facts": [ { "a": "Model API", "b": "HTTP API", "name": "Kind" }, { "a": "Meta", "b": "Verute Technologies Private Limited", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.simplismart.live", "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 hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences.", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-19", "name": "Last release" }, { "a": "2026-10-02", "b": "2023-01-13", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "2023-01-13", "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": "not found in the text", "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" } ], "faq": [ { "answer": "Muse Ember scores 51.8 (D) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.", "question": "Which is better for AI agents, Muse Ember or Simplismart?" }, { "answer": "Both need an API key.", "question": "Do Muse Ember and Simplismart need an API key?" }, { "answer": "Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Simplismart at https://api.simplismart.live.", "question": "Can an agent call Muse Ember and Simplismart without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 40 against 15", "Schema \u0026 documentation, 60 against 51", "Agent ergonomics, 72 against 17", "Security \u0026 auth, 64 against 35", "Transparency \u0026 trust, 53 against 29" ], "also": null, "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" }, { "aheadOn": [ "Payments \u0026 pricing, 25 against 20" ], "also": null, "goodFor": "Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.", "slug": "simplismart", "watchFor": "No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages" } ], "history": { "muse-ember": null, "simplismart": [ { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.2, "from": 29.7, "fromGrade": "F", "to": 29.5, "toGrade": "F" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-simplismart.json", "title": "Amazon Bedrock vs Simplismart", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-simplismart" }, { "json": 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"url": "https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash.json", "title": "Simplismart vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub.json", "title": "Simplismart vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-together-inference.json", "title": "Simplismart vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/simplismart-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway.json", "title": "Simplismart vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.json", "title": "Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/simplismart-vs-xai-api.json", "title": "Simplismart vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/simplismart-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart.json", "title": "Nemotron 3.5 Super VL vs Simplismart", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart" } ], "panel": null, "scores": [ { "by": 25, "edge": "muse-ember", "key": "reliability", "muse-ember": 40, "name": "Reliability", "simplismart": 15, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 9, "edge": "muse-ember", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "simplismart": 51, "weight": 13 }, { "by": 55, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "simplismart": 17, "weight": 13 }, { "by": 29, "edge": "muse-ember", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "simplismart": 35, "weight": 14 }, { "by": 5, "edge": "simplismart", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "simplismart": 25, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 4, "edge": "muse-ember", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "simplismart": 42, "weight": 7 }, { "by": 24, "edge": "muse-ember", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "simplismart": 29, "weight": 7 } ], "summary": "Muse Ember scores 51.8 (D) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on 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.", "simplismart": "Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.min.md" }, "markdown": "Muse Ember scores 51.8 (D) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on 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- Simplismart: grade F, 29.5/100, rank #1429 of 1515. Markdown https://www.anchorterminal.com/tools/simplismart.md · JSON https://www.anchorterminal.com/api/v1/tools/simplismart.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 15\n- Schema \u0026 documentation, 60 against 51\n- Agent ergonomics, 72 against 17\n- Security \u0026 auth, 64 against 35\n- Transparency \u0026 trust, 53 against 29\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n### Simplismart (F)\n\nGood for: Teams that want open models on a pay-per-token OpenAI-compatible endpoint and a path to dedicated GPUs, their own cloud or fine-tuning with one vendor, and can work without published rate limits or data-handling terms.\n\nAhead on:\n- Payments \u0026 pricing, 25 against 20\n\nWatch for: No request or token rate limits, 429 handling or retry guidance found in the docs; only GPU quotas, given as 1 and as 8 H100s on two pages\n\n\n## Why the edge\n\n### Agent ergonomics: Muse Ember +55\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- Simplismart (17): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). The LiveKit guide calls the LLM API fully OpenAI-compatible, but no page documents tools or tool_choice, and the OpenAPI files allow only system, user and assistant roles (3 of 20). No structured-output or JSON mode documented (0 of 15). No prompt caching documented for shared endpoints (0 of 15). Context lengths are not stated per model; output is capped at 4,096 tokens in the API files (3 of 15). No batch API for shared endpoints (0 of 10). Inference goes through the OpenAI client; the simplismart-sdk Python package and CLI manage repositories and deployments, not inference, and a community Pipecat package exists (5 of 10). Expired keys return a documented 401, and the SDK exposes status_code, message and payload, but no error codes or bodies are listed for the inference API (6 of 15). 3+0+0+3+0+5+6 = 17.\n\n### Security \u0026 auth: Muse Ember +29\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- Simplismart (35): Model reading. Named Bearer keys with an expiry chosen at creation, a 401 after expiry and immediate deletion. No scopes or per-key limits found, keys belong to the organisation, and the SDK uses a separate Playground token (20 of 30). No statement on training on API data in the terms, the privacy policy, the AI policy or the docs (0 of 20). No retention or zero-retention statement for prompts and outputs. The privacy policy covers the website only (0 of 15). The Usage page shows request counts and a per-request timeline per model, and an LLM metrics call returns token counts and timings per request id. A Simplismart article claims exportable audit trails, not documented (8 of 15). The homepage shows GDPR, ISO 27001 v2022 and AICPA SOC 2 badges, and a Simplismart article says it holds a SOC 2 Type 2 report, available on request. No trust centre was reachable, security.txt answered 404, and no disclosure policy or bug bounty was found (7 of 20). 20+0+0+8+7 = 35.\n\n### Reliability: Muse Ember +25\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- Simplismart (15): Hosted reading. No status page found. The docs and site link none, a search found none, and status.simplismart.ai could not be reached from our network (0). No readable incident history (5). No request or token rate limits are published for shared endpoints. The docs give GPU quotas for training, compilation and private deployments only, 1 H100 and 1 L40 on the Quotas page and 8 of each on the Organisation page (0). No 429 response, Retry-After header or retry guidance found; the OpenAPI files list only 400, 401 and 500 (0). No SLA document found. The homepage claims 99.99% uptime and a Simplismart post calls its autoscaling SLA-backed, but no terms state a commitment, and the terms provide the service as is (0). Shared endpoints carry no beta label, while Muse Spark Open is in beta (10). 0+5+0+0+0+10 = 15.\n\n### Transparency \u0026 trust: Muse Ember +24\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- Simplismart (29): Closed service. The terms, last updated 13 January 2023, name Verute Technologies Private Limited and Indian law but define the Service as the website, grant Simplismart a licence to use and redistribute posted Content, bar robots and automatic access, and rule out refunds. No API or platform terms were found (8 of 30). The privacy policy, effective 13 January 2023, is a website policy that says personal data is processed in India. It says nothing about prompts, outputs, logs or training, and no DPA, retention period or subprocessor list was found. A Simplismart article claims GDPR and HIPAA support (3 of 30). No deprecation policy or dated removals (0 of 20). Data goes to India per the privacy policy, and an article says workloads are kept in approved regions by scheduling policy. No subprocessor or hosting-provider list (3 of 20). 8+3+0+3 = 14.\n\n### Schema \u0026 documentation: Muse Ember +9\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- Simplismart (51): Model reading. OpenAPI 3.1 files for seven LLMs and 3.0 or 3.1 files for Whisper, Flux, an LLM metrics call and training jobs. They cover a handful of the 150+ models, omit tools and response_format, show a streaming `delta` as the non-streaming response, and several name hosts on s9t.link instead of api.simplismart.live. The Llama 4 Maverick file gives `llama3_1` as its only model id, and /api-reference/openapi.json is the Flux spec (12 of 25). llms.txt and a Markdown copy of each docs page (10). The docs say when to use shared or dedicated endpoints; model pages carry one-line descriptions (10 of 20). Roles are enums, temperature is bounded 0 to 2, max_tokens 1 to 4096, and messages and model are required (10 of 15). curl and OpenAI-client Python examples. Errors are bare 400, 401 and 500 descriptions, plus an SDK example of a 404 payload (7 of 15). No changelog. Every spec reads version 1.0.0 (2 of 15). 12+10+10+10+7+2 = 51.\n\n### Payments \u0026 pricing: Simplismart +5\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- Simplismart (25): No x402, MPP or L402 in the docs or pricing (0). Per-1M-token, per-image, per-audio-minute and per-GPU-hour prices are public without a login. The list omits current models such as Gemma 4 31B, which the quickstart uses, and doesn't say whether the single token price covers input and output (15 of 20). The billing page says new organisations may receive $5 of free credits; whether a card is needed isn't stated, and we did not sign up (10 of 20). A person signs up in the browser with a work email and a verification code, and makes keys in the console (0). 0+15+10+0 = 25.\n\n## Score by category\n\n| Category | Weight | Muse Ember | Simplismart | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 15 | Muse Ember +25 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 51 | Muse Ember +9 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 17 | Muse Ember +55 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 35 | Muse Ember +29 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 25 | Simplismart +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 42 | Muse Ember +4 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 29 | Muse Ember +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.8 · D** | **29.5 · F** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | Simplismart |\n| --- | --- | --- |\n| Kind | Model API | HTTP API |\n| Vendor | Meta | Verute Technologies Private Limited |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.simplismart.live` |\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 hosted service. The community Pipecat integration (pipecat-simplismart, by the simpli-smart GitHub account) is BSD-2-Clause; served models keep their own licences. |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-19 |\n| Terms last updated | 2026-10-02 | 2023-01-13 |\n| Privacy policy last updated | couldn't be read | 2023-01-13 |\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 | not found in the text |\n| Terms or service can change without notice | not found in the text | yes |\n| Arbitration or class-action waiver | yes | yes |\n\n## How the scores have moved\n\n### Muse Ember\n\nNo score change since it was listed.\n\n### Simplismart\n\n- -0.2 on 30 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**Simplismart.** Simplismart exposes open models through an OpenAI-compatible endpoint with public per-token prices and expiring, revocable keys. Its public docs give no request rate limits, status page, retry guidance, tool-calling or structured-output reference, and its terms and privacy policy cover the website, not API data.\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### Simplismart\n\n1. Point an OpenAI-compatible client at https://api.simplismart.live with SIMPLISMART_API_KEY as a Bearer token, and take model ids from the marketplace or the per-model API pages.\n2. Set a key expiry and rotate before midnight UTC on the expiry date, because an expired key returns 401.\n3. Cap retries yourself with exponential backoff, because no rate limits, 429 format or retry guidance are published.\n4. Check that a model supports tools or JSON output with a test call before relying on it, because neither is documented.\n5. Confirm the per-token price of the exact model in the console before running large jobs, because the public price list is incomplete.\n\n## Questions\n\n### Which is better for AI agents, Muse Ember or Simplismart?\n\nMuse Ember scores 51.8 (D) on agent readiness against Simplismart's 29.5 (F), and leads in 6 of 7 scored categories. Simplismart leads on payments \u0026 pricing.\n\n### Do Muse Ember and Simplismart need an API key?\n\nBoth need an API key.\n\n### Can an agent call Muse Ember and Simplismart without installing anything?\n\nYes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Simplismart at https://api.simplismart.live.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"simplismart\"}`. From a terminal: `anchor compare muse-ember simplismart`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-ember.json and https://www.anchorterminal.com/api/v1/tools/simplismart.json\n\n## Other comparisons with Muse Ember or Simplismart\n\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Amazon Bedrock vs Simplismart](https://www.anchorterminal.com/compare/amazon-bedrock-vs-simplismart.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Claude API vs Simplismart](https://www.anchorterminal.com/compare/anthropic-api-vs-simplismart.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Antseed vs Simplismart](https://www.anchorterminal.com/compare/antseed-vs-simplismart.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- [Azure 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Simplismart](https://www.anchorterminal.com/compare/routerplus-vs-simplismart.md)\n- [SambaCloud vs Simplismart](https://www.anchorterminal.com/compare/sambanova-vs-simplismart.md)\n- [SiliconFlow vs Simplismart](https://www.anchorterminal.com/compare/siliconflow-vs-simplismart.md)\n- [Simplismart vs StepFun API](https://www.anchorterminal.com/compare/simplismart-vs-stepfun-api.md)\n- [Simplismart vs SWE-2 Flash](https://www.anchorterminal.com/compare/simplismart-vs-swe-2-flash.md)\n- [Simplismart vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/simplismart-vs-tencent-tokenhub.md)\n- [Simplismart vs Together AI Inference](https://www.anchorterminal.com/compare/simplismart-vs-together-inference.md)\n- [Simplismart vs Vercel AI Gateway](https://www.anchorterminal.com/compare/simplismart-vs-vercel-ai-gateway.md)\n- [Simplismart vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/simplismart-vs-vertex-ai.md)\n- [Simplismart vs xAI API (Grok)](https://www.anchorterminal.com/compare/simplismart-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs Simplismart](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-simplismart.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 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