{ "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": "OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.", "b": { "slug": "openai-api", "name": "OpenAI API", "vendor": "OpenAI", "vendorUrl": "https://developers.openai.com", "kind": "model", "category": "inference", "summary": "OpenAI's API for accessing its models through Responses, Chat Completions and Batch endpoints.", "url": "https://www.anchorterminal.com/tools/openai-api", "markdownUrl": "https://www.anchorterminal.com/tools/openai-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-api.json", "dns": "openai-api.grade.anchorterminal.com", "repo": "https://github.com/openai/openai-python", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://api.openai.com/v1", "packages": [ { "registry": "pypi", "name": "openai", "main": true }, { "registry": "npm", "name": "openai", "main": true } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a project key. Some models and tools need business or ID verification first.", "pricing": "usage", "pricingNotes": "Prepaid credit, with the Build tier reached at $5 in credit purchases, Launch at $100 and Grow at $500. Long context over 272K tokens costs 2x input and 1.5x output. Cache reads 0.1x input (0.05x on GPT-6.1 Sol and GPT-6.1 Terra), cache writes 1.25x from GPT-5.6 on, or 2.5x for the 12-hour lifetime in the Responses API since 20 October. Batch and Flex half price, Fast mode 2x, Ultrafast 6x (GPT-6 Astra, and GPT-6.1 Sol since 8 October). Data residency endpoints add 10% for models released on or after 5 March 2026, and FedRAMP endpoints add 10% from 1 November 2026. Web search $10 per 1,000 calls plus search content tokens, file search $2.50 per 1,000, hosted containers from $0.03 per 20-minute session (https://developers.openai.com/api/docs/pricing).", "priceSummary": "from $0.10 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. Access needs an account with prepaid credit.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 31300, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://developers.openai.com/api/docs", "rateLimitsUrl": "https://developers.openai.com/api/docs/guides/rate-limits", "llmsTxt": "https://developers.openai.com/llms.txt", "openapi": "https://github.com/openai/openai-openapi", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "card-required", "openapi", "llms-txt" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 83.9, "grade": "A", "agentReady": true, "rank": 6, "ranked": true, "rankOf": 1515, "categoryRank": 2, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 87, "payments": 30, "reliability": 75, "schema": 100, "security": 100, "transparency": 90 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": 0, "verdict": "Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.", "bestFor": "Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.", "strengths": [ "Official OpenAPI document and an llms.txt index", "Project keys can be restricted per endpoint to None, Read or Write, and mutual TLS workload identity is GA", "A dated deprecations page, and `GET /v1/models` returns each model's `shutdown_date`", "GPT-6 Luna at $0.10/$0.50 per million tokens with the same 1.05M context as Astra", "Scale Tier comes with a 99.9% uptime SLA" ], "weaknesses": [ "Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026", "Heavy migration calendar. Assistants API gone on 2026-08-26, Agent Builder, Evals and `v1/prompts` on 2026-11-30, GPT-5 and o3 on 2026-12-11", "GPT-6 models aren't available on the Free tier, so a card and prepaid credit come first in practice", "GPT-6 Astra has no custom temperature, no logprobs and no tool calling outside the Responses API", "GPT-4o and GPT-4.1 snapshots got 92 days' notice on 14 October, against the 6 months the deprecations page promises GA models" ], "agentNotes": [ "Build on the Responses API. Astra calls tools only there", "Use `gpt-6-luna` for routing and extraction, `gpt-6.1-terra` for screens, images and computer use at half Sol's price, and `gpt-6.1-sol` for most work. The models page still points unsure callers to `gpt-6-astra`", "Move off the GPT-4o and GPT-4.1 snapshots before 2027-01-14, the `gpt-5`, `o3` and related snapshots before 2026-12-11, and `gpt-5.1`, `gpt-5.3-codex` and `gpt-5.4-nano` before 2027-04-01. Check `shutdown_date` on `GET /v1/models`", "Treat 429 `slow_down` as a ramp limit and 503 `server_is_overloaded` as a retry, and follow `Retry-After` when it's sent", "Prompts over 272K tokens cost double on input. Trim before you pay for it" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 8, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "A", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 83.9 } ], "editorialScores": { "ergonomics": 98, "maintenance": 87, "payments": 30, "reliability": 75, "schema": 100, "security": 100, "transparency": 85 }, "provenanceScore": 95, "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 openai # or: npm i openai", "http": "curl https://api.openai.com/v1/responses \\\n -H \"Authorization: Bearer $OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"gpt-6.1-sol\",\"input\":\"hello\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/openai-api" }, "sameCompany": [ "openai-embeddings", "openai-guardrails", "openai-moderation", "openai-image-api", "openai-sora", "openai-speech-to-text", "gpt-live", "openai-realtime", "openai-agents-sdk", "openai-decisions-api", "openai-agents-api", "openai-codex", "gpt-oss-120b", "gpt-oss-20b" ], "area": "models", "unitPrices": [ { "item": "Web search tool", "unit": "1k-requests", "usd": 10 }, { "item": "File search tool", "unit": "1k-requests", "usd": 2.5 } ], "provenance": { "legalEntity": "OpenAI OpCo, LLC", "domain": "openai.com", "domainRegistered": "2007-01-19", "domainNote": "openai.com was registered in 2007, before OpenAI existed.", "endpointOnVendorDomain": true, "terms": "https://openai.com/policies/services-agreement/", "privacy": "https://openai.com/policies/privacy-policy/", "statusPage": "https://status.openai.com", "changelog": "https://developers.openai.com/api/docs/changelog", "securityTxt": "valid", "checked": "2026-09-26", "score": 95 }, "requirements": { "setupChecked": true, "complianceChecked": true, "card": "paid-only", "freeTier": true, "minutes": 5, "credential": "api-key", "hosting": [ "US", "EU", "UK", "CA", "APAC" ], "euResidency": "enterprise", "trainsOnData": "no", "zeroRetention": "enterprise", "certifications": [ "SOC 2 Type II", "ISO 27001", "ISO 27701", "ISO 42001", "CSA STAR", "HIPAA (BAA available)", "GDPR" ], "dpa": true, "selfHost": "no" }, "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-api.json", "live": { "slug": "openai-api", "probe": { "target": "https://api.openai.com/v1", "method": "get", "lastAt": "2026-11-09T20:50:04.750931308Z", "lastOk": true, "lastStatus": 404, "lastMs": 172, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 166, "p95ms24h": 225, "samples24h": 244, "samples30d": 7881, "days": [ { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 250, "ok": 250 }, { "date": "2026-10-29", "probes": 249, "ok": 249 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 212, "ok": 212 } ], "since": "2026-10-08T00:00:00Z" }, "vendorStatus": { "page": "https://status.openai.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-09T20:46:37.139489527Z" }, "versions": [ { "registry": "github", "name": "openai/openai-python", "version": "v3.47.0", "released": "2026-11-05", "seenAt": "2026-11-09T02:45:33.452253198Z" }, { "registry": "npm", "name": "openai", "version": "7.53.0", "seenAt": "2026-11-09T02:45:33.422344901Z" }, { "registry": "pypi", "name": "openai", "version": "3.46.0", "released": "2026-11-05", "seenAt": "2026-11-09T02:45:31.087476689Z" } ], "githubStars": 32089, "npmWeekly": 57196916, "pypiWeekly": 92006831, "securityTxt": { "url": "https://openai.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-11-09T16:05:26.103928306Z" }, "llmsTxt": { "url": "https://developers.openai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:26.406737465Z" }, "domain": { "domain": "openai.com", "registered": "2007-01-19", "source": "https://rdap.verisign.com/com/v1/domain/openai.com", "checkedAt": "2026-11-08T14:52:04.4095079Z" }, "pages": [ { "url": "https://developers.openai.com/api/docs/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-16T22:20:47.232011113Z", "changedAt": "2026-10-16T22:20:47.232011113Z", "fingerprint": "3261efc5e241" }, { "url": "https://developers.openai.com/api/docs/deprecations", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-09T15:35:38.331612413Z", "changedAt": "2026-11-06T13:26:58.963754521Z", "fingerprint": "0e64ced508a8" }, { "url": "https://developers.openai.com/api/docs/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-09T15:35:40.331984971Z", "changedAt": "2026-11-06T13:27:00.948019243Z", "fingerprint": "a83cd96e6a1c" } ], "models": { "gpt-6-astra": { "openrouterId": "openai/gpt-6-astra", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gpt-6-luna": { "openrouterId": "openai/gpt-6-luna", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gpt-6-sol": { "openrouterId": "openai/gpt-6-sol", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-10-04T21:56:15.872733218Z" }, "gpt-6.1-sol": { "openrouterId": "openai/gpt-6.1-sol", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gpt-6.1-terra": { "openrouterId": "openai/gpt-6.1-terra", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "file", "image", "text" ], "promptCaching": true, "contextTokens": 1050000, "maxOutput": 128000, "checkedAt": "2026-11-08T23:49:27.027409601Z" } }, "github": { "repo": "openai/openai-python", "checkedAt": "2026-11-09T10:48:47.446503152Z", "stars": 32090, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-09T09:48:52Z", "openIssues": 41, "openPRs": 54, "issuesOpened90d": 43, "issuesClosed90d": 220, "prsMerged90d": 460, "ci": { "branch": "main", "runs": 50, "success": 48, "failure": 1, "other": 1, "latest": "success", "latestAt": "2026-11-08T16:17:52Z" }, "replies": { "sampled": 25, "unanswered": 10, "medianDays": 4.4 } }, "updatedAt": "2026-11-09T20:50:04.750931308Z" }, "streak": { "days": 17, "from": "2026-10-23", "last": { "kind": "shutdown", "date": "2026-10-23", "what": "Legacy snapshots including `gpt-3.5-turbo-0125`, `gpt-4-0613`, `gpt-4-1106-preview` and `o1-2024-12-17` shut down", "source": "https://developers.openai.com/api/docs/deprecations" }, "next": { "kind": "shutdown", "date": "2026-11-30", "what": "`v1/prompts`, Evals and Agent Builder shut down (existing evals read-only from 2026-10-31)", "source": "https://developers.openai.com/api/docs/deprecations" }, "best": 17 } }, "edges": [ { "by": 41, "category": "maintenance", "leads": "openai-api", "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. GPT-6 Rosalind, a life-sciences model for approved organisations, launched on 30 October, after GPT-6.1 Terra on 26 October and the Decisions API GA on 22 October (30). Published minimum notice of 6 months for GA models, 3 months for specialised variants, as little as 2 weeks for previews. On 14 October GPT-4o (`gpt-4o-2024-08-06`, `gpt-4o-2024-11-20`), GPT-4o mini, GPT-4.1 and GPT-4.1 mini were given a shutdown date of 14 January 2027, 92 days, with no safety or compliance reason stated. These are GA models, so the page's own 6-month minimum wasn't kept, as with `gpt-5.4-cyber`'s 20 days in September. 8 of 12, the award for a minimum of two to three months in practice. Five shutdown dates in the 90 days to 1 November, the Assistants API on 26 August (announced in August 2025), `gpt-5.2-chat-latest` and `gpt-5.3-chat-latest` on 10 August (announced 8 May), `gpt-3.5-turbo-instruct`, `babbage-002`, `davinci-002` and `gpt-3.5-turbo-1106` on 28 September (announced 26 September 2025, missed in the 27 October count), `gpt-5.4-cyber` on 1 October and the legacy snapshots on 23 October (announced 22 April). Sora 2 and the Videos API (24 September) are scored on the openai-sora listing. Four of the five kept the published notice, so 4 of 8 as before, as amazon-bedrock has for four dates after full notice. Dated changelog several times a month and a help centre (15 of 15). openai-python had 215 open issues per the 1 October check, replies not sampled (5 of 10). Current official SDKs, `openai` 3.42.0 on PyPI (31 October) and npm `openai` 7.47.0 (15). Supported runtimes stated, Python 3.10 or later and Node 22 or later, the npm package's `engines` re-read on 1 November (10)." }, "scores": { "a": 46, "b": 87 } }, { "by": 40, "category": "schema", "leads": "openai-api", "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": "Official OpenAPI document (openapi.yaml and openapi.json) in the openai/openai-openapi repository, whose openapi.yaml (version 2.3.0, updated 31 October) lists `gpt-6.1-terra` (25). llms.txt index with per-product files, and every docs page has a Markdown twin at `.md` (10). The reference documents each parameter, and model pages say which model fits which job (20). Structured outputs with `strict: true` on schemas and function tools (15). Error codes guide with types and recovery advice, now with a 400 `service_tier` error for tiers a project doesn't allow (15). Dated model snapshots and a dated changelog, last entry 30 October (15)." }, "scores": { "a": 60, "b": 100 } }, { "by": 37, "category": "transparency", "leads": "openai-api", "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, SDKs Apache-2.0 (15). The data-controls guide, re-read on 1 November, gives retention periods per endpoint, the training default and the ZDR route. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum for transfers, and nothing in it conflicts with the guide, per the 4 October check, not re-read on 1 November (30). Deprecations page with announcement and shutdown dates, and `GET /v1/models` returns each model's `shutdown_date` (20). The subprocessor list, updated 9 July 2026, names its third-party subprocessors and OpenAI affiliates with the processing countries for each and a form to get notice of new ones, and data residency regions are listed, per the 4 October check, not re-read on 1 November (20)." }, "scores": { "a": 53, "b": 90 } }, { "by": 36, "category": "security", "leads": "openai-api", "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. Project keys with All, Restricted (per-endpoint None, Read or Write) and Read Only permissions, service accounts, and mutual TLS with X.509 workload identity GA since 26 August, per the 1 October check. The changelog adds project key expiry dates on 10 September, key-creation controls on 15 September and, on 21 October, organisation and project settings that limit which service tiers a project may use (30). API data not used for training unless the customer opts in, since 1 March 2023 (20). Abuse-monitoring logs kept up to 30 days, and Zero Data Retention or Modified Abuse Monitoring by approval. The data-controls guide re-read on 1 November lists Chat Completions, Responses, Decisions and ten other endpoints as ZDR-eligible; `/v1/agents`, Conversations, Files and vector stores aren't (15). Usage and Costs filter by API key since 4 August, with Admin APIs to match, and audit logs for enterprise (15). SOC 2 Type 2, ISO 27001, 27017, 27018, 27701 and 42001 per the 1 October check, not re-read on 1 November, a Bugcrowd bug bounty and a coordinated disclosure policy, both linked from a signed security.txt. That security.txt had no Expires field on 27 October, which RFC 9116 requires, so the disclosure policy carries this line. It wasn't re-read on 1 November (20)." }, "scores": { "a": 64, "b": 100 } }, { "by": 35, "category": "reliability", "leads": "openai-api", "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": "Status page at status.openai.com with component history (20). Two API-wide majors in the 90 days to 1 November, elevated errors across ChatGPT, Codex and the API (the Agents API included) on 29 September, with Chat Completions, Responses, Batch, Realtime, Embeddings, Images, Files and the other API components degraded from 17:52 to 23:14 UTC, and elevated error rates across API models on 17 September, with eleven components degraded from 20:20 to 21:20 UTC. Two majors give 5 of 30. The 25 July incident is now outside the window. The 29 September write-up, posted on 6 October, traces it to a feature rollout that exhausted shared network connection capacity. Every other incident since 3 August that lists an API component is partial or under an hour, so minor. Degraded performance across ChatGPT, Codex and the APIs on 21 October listed every API component but ran 22 minutes (02:26 to 02:48 UTC). Single-model incidents were GPT-5.6 Sol errors on 11 September (09:08 to 11:21 UTC), `gpt-5.6-luna` errors on 21 October (70 minutes) and GPT-6.1 Sol degraded performance on Batch, Chat Completions and Responses on 27 October (78 minutes). Partial incidents were latency for some Responses requests on the US endpoint on 31 August and 1 September, image generation where some requests failed on 8 September, latency for some Responses and Chat Completions requests on 30 September, large-PDF errors in Responses and an Agents API computer use incident on 6 October, Responses errors for users in India, Japan and Korea on 22 October and errors on some GPT Image 2 edit requests on 30 October (14:35 to 18:36 UTC). Image model incidents on 16 September and 14 October and file download errors on 19 October each ran about 40 minutes. FedRAMP errors for Astra and Sol on 21 October and authentication errors near Illinois on 20 October list no API component. Rate limits published per tier and model family with rate-limit headers (15). 429 guidance, re-read on 1 November, covers `Retry-After` as a minimum wait, backoff with jitter when it's missing, a gradual ramp, and splits `slow_down` (429) from `server_is_overloaded` (503) (15). Scale Tier carries a 99.9% uptime SLA, through sales, per the 1 October check, not re-read on 1 November (10). Responses and Chat Completions are GA (10). 20 + 5 + 15 + 15 + 10 + 10 = 75." }, "scores": { "a": 40, "b": 75 } }, { "by": 26, "category": "ergonomics", "leads": "openai-api", "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). Tool use with parallel calls and forced tool choice, 18 of 20 because GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra call tools only through the Responses API (18). Strict structured outputs (15). Automatic prompt caching with cached input at 0.1x on Astra and Luna ($0.01 per million on Luna) and 0.05x on GPT-6.1 Sol ($0.10) and GPT-6.1 Terra ($0.05), and a 12-hour cache lifetime in the Responses API since 20 October (15). 1.05M context on Astra, GPT-6.1 Sol, GPT-6.1 Terra and Luna (15). Batch at half price (10). Official SDKs in Python, JavaScript and more (10). Documented error codes with retry guidance (15)." }, "scores": { "a": 72, "b": 98 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "OpenAI", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.openai.com/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-30", "name": "Last release" }, { "a": "2026-10-02", "b": "couldn't be read", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "couldn't be read", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms restrict automated access" }, { "a": "yes", "b": "couldn't be read", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "couldn't be read", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "couldn't be read", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "31k stars", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (8)", "name": "Agent reviews" } ], "faq": [ { "answer": "OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.", "question": "Which is better for AI agents, Muse Ember or OpenAI API?" }, { "answer": "Both need an API key.", "question": "Do Muse Ember and OpenAI API need an API key?" }, { "answer": "Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and OpenAI API at https://api.openai.com/v1.", "question": "Can an agent call Muse Ember and OpenAI API without installing anything?" } ], "goodFor": [ { "aheadOn": null, "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": [ "Reliability, 75 against 40", "Schema \u0026 documentation, 100 against 60", "Agent ergonomics, 98 against 72", "Security \u0026 auth, 100 against 64", "Payments \u0026 pricing, 30 against 20", "Maintenance \u0026 community, 87 against 46", "Transparency \u0026 trust, 90 against 53" ], "also": [ "Agent-ready, a grade of BB or better" ], "goodFor": "Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.", "slug": "openai-api", "watchFor": "Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026" } ], "history": { "muse-ember": null, "openai-api": [ { "causes": [ "correction" ], "date": "2026-11-01", "delta": 1, "from": 82.9, "fromGrade": "A", "to": 83.9, "toGrade": "A" }, { "causes": [ "product" ], "date": "2026-10-17", "delta": -0.4, "from": 83.3, "fromGrade": "A", "to": 82.9, "toGrade": "A" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.2, "from": 83.5, "fromGrade": "A", "to": 83.3, "toGrade": "A" } ] }, "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-openai-api.json", "title": "Amazon Bedrock vs OpenAI API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.json", "title": "Claude API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-openai-api.json", "title": "Claude API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember.json", "title": "Antseed vs Muse Ember", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-openai-api.json", "title": "Antseed vs OpenAI API", "url": "https://www.anchorterminal.com/compare/antseed-vs-openai-api" }, { "json": 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"https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.json", "title": "Inkling 2 Small vs Muse Ember", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-openai-api.json", "title": "Inkling 2 Small vs OpenAI API", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.json", "title": "Jais 3 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-openai-api.json", "title": "Jais 3 vs OpenAI API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.json", "title": "Meta Model API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.json", "title": "MiniMax API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api.json", "title": "MiniMax API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.json", "title": "Mistral AI API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-openai-api.json", "title": "Mistral AI API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-openai-api" }, { "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-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-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-openai-api.json", "title": "Muse Spark Open vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-openai-api.json", "title": "Nemotron 3.5 Super VL vs OpenAI API", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-openai-api.json", "title": "Novita AI vs OpenAI API", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-openrouter.json", "title": "OpenAI API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/openai-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-prism-inference.json", "title": "OpenAI API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/openai-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-ramp-router.json", "title": "OpenAI API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-reflection-beam.json", "title": "OpenAI API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/openai-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-routerplus.json", "title": "OpenAI API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/openai-api-vs-routerplus" }, { "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/openai-api-vs-siliconflow.json", "title": "OpenAI API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/openai-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-simplismart.json", "title": "OpenAI API vs Simplismart", "url": "https://www.anchorterminal.com/compare/openai-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-stepfun-api.json", "title": "OpenAI API vs StepFun API", "url": "https://www.anchorterminal.com/compare/openai-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-swe-2-flash.json", "title": "OpenAI API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/openai-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-tencent-tokenhub.json", "title": "OpenAI API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/openai-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-together-inference.json", "title": "OpenAI API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/openai-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-vercel-ai-gateway.json", "title": "OpenAI API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai.json", "title": "OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-xai-api.json", "title": "OpenAI API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/openai-api-vs-xai-api" } ], "panel": null, "scores": [ { "by": 35, "edge": "openai-api", "key": "reliability", "muse-ember": 40, "name": "Reliability", "openai-api": 75, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 40, "edge": "openai-api", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "openai-api": 100, "weight": 13 }, { "by": 26, "edge": "openai-api", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "openai-api": 98, "weight": 13 }, { "by": 36, "edge": "openai-api", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "openai-api": 100, "weight": 14 }, { "by": 10, "edge": "openai-api", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "openai-api": 30, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 41, "edge": "openai-api", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "openai-api": 87, "weight": 7 }, { "by": 37, "edge": "openai-api", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "openai-api": 90, "weight": 7 } ], "summary": "OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. 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.", "openai-api": "Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.min.md" }, "markdown": "OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. 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- OpenAI API: grade A, 83.9/100, rank #6 of 1515. Markdown https://www.anchorterminal.com/tools/openai-api.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-api.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\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n### OpenAI API (A)\n\nGood for: Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra.\n\nAhead on:\n- Reliability, 75 against 40\n- Schema \u0026 documentation, 100 against 60\n- Agent ergonomics, 98 against 72\n- Security \u0026 auth, 100 against 64\n- Payments \u0026 pricing, 30 against 20\n- Maintenance \u0026 community, 87 against 46\n- Transparency \u0026 trust, 90 against 53\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026\n\n\n## Why the edge\n\n### Maintenance \u0026 community: OpenAI API +41\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- OpenAI API (87): Model reading. GPT-6 Rosalind, a life-sciences model for approved organisations, launched on 30 October, after GPT-6.1 Terra on 26 October and the Decisions API GA on 22 October (30). Published minimum notice of 6 months for GA models, 3 months for specialised variants, as little as 2 weeks for previews. On 14 October GPT-4o (`gpt-4o-2024-08-06`, `gpt-4o-2024-11-20`), GPT-4o mini, GPT-4.1 and GPT-4.1 mini were given a shutdown date of 14 January 2027, 92 days, with no safety or compliance reason stated. These are GA models, so the page's own 6-month minimum wasn't kept, as with `gpt-5.4-cyber`'s 20 days in September. 8 of 12, the award for a minimum of two to three months in practice. Five shutdown dates in the 90 days to 1 November, the Assistants API on 26 August (announced in August 2025), `gpt-5.2-chat-latest` and `gpt-5.3-chat-latest` on 10 August (announced 8 May), `gpt-3.5-turbo-instruct`, `babbage-002`, `davinci-002` and `gpt-3.5-turbo-1106` on 28 September (announced 26 September 2025, missed in the 27 October count), `gpt-5.4-cyber` on 1 October and the legacy snapshots on 23 October (announced 22 April). Sora 2 and the Videos API (24 September) are scored on the openai-sora listing. Four of the five kept the published notice, so 4 of 8 as before, as amazon-bedrock has for four dates after full notice. Dated changelog several times a month and a help centre (15 of 15). openai-python had 215 open issues per the 1 October check, replies not sampled (5 of 10). Current official SDKs, `openai` 3.42.0 on PyPI (31 October) and npm `openai` 7.47.0 (15). Supported runtimes stated, Python 3.10 or later and Node 22 or later, the npm package's `engines` re-read on 1 November (10).\n\n### Schema \u0026 documentation: OpenAI API +40\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- OpenAI API (100): Official OpenAPI document (openapi.yaml and openapi.json) in the openai/openai-openapi repository, whose openapi.yaml (version 2.3.0, updated 31 October) lists `gpt-6.1-terra` (25). llms.txt index with per-product files, and every docs page has a Markdown twin at `.md` (10). The reference documents each parameter, and model pages say which model fits which job (20). Structured outputs with `strict: true` on schemas and function tools (15). Error codes guide with types and recovery advice, now with a 400 `service_tier` error for tiers a project doesn't allow (15). Dated model snapshots and a dated changelog, last entry 30 October (15).\n\n### Transparency \u0026 trust: OpenAI API +37\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- OpenAI API (90): Closed service with clear terms, SDKs Apache-2.0 (15). The data-controls guide, re-read on 1 November, gives retention periods per endpoint, the training default and the ZDR route. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum for transfers, and nothing in it conflicts with the guide, per the 4 October check, not re-read on 1 November (30). Deprecations page with announcement and shutdown dates, and `GET /v1/models` returns each model's `shutdown_date` (20). The subprocessor list, updated 9 July 2026, names its third-party subprocessors and OpenAI affiliates with the processing countries for each and a form to get notice of new ones, and data residency regions are listed, per the 4 October check, not re-read on 1 November (20).\n\n### Security \u0026 auth: OpenAI API +36\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- OpenAI API (100): Model reading. Project keys with All, Restricted (per-endpoint None, Read or Write) and Read Only permissions, service accounts, and mutual TLS with X.509 workload identity GA since 26 August, per the 1 October check. The changelog adds project key expiry dates on 10 September, key-creation controls on 15 September and, on 21 October, organisation and project settings that limit which service tiers a project may use (30). API data not used for training unless the customer opts in, since 1 March 2023 (20). Abuse-monitoring logs kept up to 30 days, and Zero Data Retention or Modified Abuse Monitoring by approval. The data-controls guide re-read on 1 November lists Chat Completions, Responses, Decisions and ten other endpoints as ZDR-eligible; `/v1/agents`, Conversations, Files and vector stores aren't (15). Usage and Costs filter by API key since 4 August, with Admin APIs to match, and audit logs for enterprise (15). SOC 2 Type 2, ISO 27001, 27017, 27018, 27701 and 42001 per the 1 October check, not re-read on 1 November, a Bugcrowd bug bounty and a coordinated disclosure policy, both linked from a signed security.txt. That security.txt had no Expires field on 27 October, which RFC 9116 requires, so the disclosure policy carries this line. It wasn't re-read on 1 November (20).\n\n### Reliability: OpenAI API +35\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- OpenAI API (75): Status page at status.openai.com with component history (20). Two API-wide majors in the 90 days to 1 November, elevated errors across ChatGPT, Codex and the API (the Agents API included) on 29 September, with Chat Completions, Responses, Batch, Realtime, Embeddings, Images, Files and the other API components degraded from 17:52 to 23:14 UTC, and elevated error rates across API models on 17 September, with eleven components degraded from 20:20 to 21:20 UTC. Two majors give 5 of 30. The 25 July incident is now outside the window. The 29 September write-up, posted on 6 October, traces it to a feature rollout that exhausted shared network connection capacity. Every other incident since 3 August that lists an API component is partial or under an hour, so minor. Degraded performance across ChatGPT, Codex and the APIs on 21 October listed every API component but ran 22 minutes (02:26 to 02:48 UTC). Single-model incidents were GPT-5.6 Sol errors on 11 September (09:08 to 11:21 UTC), `gpt-5.6-luna` errors on 21 October (70 minutes) and GPT-6.1 Sol degraded performance on Batch, Chat Completions and Responses on 27 October (78 minutes). Partial incidents were latency for some Responses requests on the US endpoint on 31 August and 1 September, image generation where some requests failed on 8 September, latency for some Responses and Chat Completions requests on 30 September, large-PDF errors in Responses and an Agents API computer use incident on 6 October, Responses errors for users in India, Japan and Korea on 22 October and errors on some GPT Image 2 edit requests on 30 October (14:35 to 18:36 UTC). Image model incidents on 16 September and 14 October and file download errors on 19 October each ran about 40 minutes. FedRAMP errors for Astra and Sol on 21 October and authentication errors near Illinois on 20 October list no API component. Rate limits published per tier and model family with rate-limit headers (15). 429 guidance, re-read on 1 November, covers `Retry-After` as a minimum wait, backoff with jitter when it's missing, a gradual ramp, and splits `slow_down` (429) from `server_is_overloaded` (503) (15). Scale Tier carries a 99.9% uptime SLA, through sales, per the 1 October check, not re-read on 1 November (10). Responses and Chat Completions are GA (10). 20 + 5 + 15 + 15 + 10 + 10 = 75.\n\n### Agent ergonomics: OpenAI API +26\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- OpenAI API (98): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and forced tool choice, 18 of 20 because GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra call tools only through the Responses API (18). Strict structured outputs (15). Automatic prompt caching with cached input at 0.1x on Astra and Luna ($0.01 per million on Luna) and 0.05x on GPT-6.1 Sol ($0.10) and GPT-6.1 Terra ($0.05), and a 12-hour cache lifetime in the Responses API since 20 October (15). 1.05M context on Astra, GPT-6.1 Sol, GPT-6.1 Terra and Luna (15). Batch at half price (10). Official SDKs in Python, JavaScript and more (10). Documented error codes with retry guidance (15).\n\n## Score by category\n\n| Category | Weight | Muse Ember | OpenAI API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 75 | OpenAI API +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 100 | OpenAI API +40 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 98 | OpenAI API +26 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 100 | OpenAI API +36 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | OpenAI API +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 87 | OpenAI API +41 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 90 | OpenAI API +37 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.8 · D** | **83.9 · A** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | OpenAI API |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | OpenAI |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.openai.com/v1` |\n| Transports | | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-30 |\n| Terms last updated | 2026-10-02 | couldn't be read |\n| Privacy policy last updated | couldn't be read | couldn't be read |\n| Customer content may train models | yes | couldn't be read |\n| Terms restrict automated access | not found in the text | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | not found in the text | couldn't be read |\n| Arbitration or class-action waiver | yes | couldn't be read |\n| Popularity | none | 31k stars |\n| Agent reviews | none | 3.5/5 (8) |\n\n## How the scores have moved\n\n### Muse Ember\n\nNo score change since it was listed.\n\n### OpenAI API\n\n- +1.0 on 1 November 2026: correction\n- -0.4 on 17 October 2026: product\n- -0.2 on 8 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**OpenAI API.** Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.\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### OpenAI API\n\n1. Build on the Responses API. Astra calls tools only there\n2. Use `gpt-6-luna` for routing and extraction, `gpt-6.1-terra` for screens, images and computer use at half Sol's price, and `gpt-6.1-sol` for most work. The models page still points unsure callers to `gpt-6-astra`\n3. Move off the GPT-4o and GPT-4.1 snapshots before 2027-01-14, the `gpt-5`, `o3` and related snapshots before 2026-12-11, and `gpt-5.1`, `gpt-5.3-codex` and `gpt-5.4-nano` before 2027-04-01. Check `shutdown_date` on `GET /v1/models`\n4. Treat 429 `slow_down` as a ramp limit and 503 `server_is_overloaded` as a retry, and follow `Retry-After` when it's sent\n5. Prompts over 272K tokens cost double on input. Trim before you pay for it\n\n## Questions\n\n### Which is better for AI agents, Muse Ember or OpenAI API?\n\nOpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.\n\n### Do Muse Ember and OpenAI API need an API key?\n\nBoth need an API key.\n\n### Can an agent call Muse Ember and OpenAI API without installing anything?\n\nYes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and OpenAI API at https://api.openai.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"openai-api\"}`. 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