{ "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": "Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.", "b": { "slug": "reflection-beam", "name": "Beam 1.0", "vendor": "Reflection", "vendorUrl": "https://reflection.ai", "kind": "model", "category": "inference", "summary": "Beam 1.0 is Reflection's open-weight text model for coding, reasoning and agentic work, with 501B total and 23B active parameters and a 1M-token context, served by the Reflection API and released as Apache-2.0 weights.", "url": "https://www.anchorterminal.com/tools/reflection-beam", "markdownUrl": "https://www.anchorterminal.com/tools/reflection-beam.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/reflection-beam.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/reflection-beam.json", "dns": "reflection-beam.grade.anchorterminal.com", "repo": "https://huggingface.co/reflection/Beam-1.0-501B-A23B", "license": "Weights under Apache-2.0. The hosted Reflection API is a closed service under Reflection's commercial terms of service", "transports": [ "http" ], "remoteUrl": "https://api.reflection.ai/openai/v1", "packages": [], "auth": "api-key", "authNotes": "Bearer API key created by a person on platform.reflection.ai under API Keys. Each key belongs to one project and to the user who made it, is shown once, can be revoked from the API Keys page, and is disabled if that user leaves the organisation or project. No scopes, expiry or key-management API found. The docs say some organisations must verify a credit card in Billing before their keys work (`payment_method_required`). Self-hosted weights need no account; the Hugging Face repositories are not gated.", "pricing": "freemium", "pricingNotes": "No per-token or plan price for the Reflection API is published on a page we can read. The rate-limits docs describe a daily token allowance that renews at 00:00 UTC and ask those requesting higher limits whether they want paid usage, and some organisations must verify a credit card before keys work (https://developers.reflection.ai/rate-limits.md, checked 2026-11-04). Reflection's beta pricing page and terms are on reflection.ai, which disallows our crawler. The weights are free to self-host under Apache-2.0.", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the Reflection docs, llms.txt or OpenAPI document. Access needs a browser sign-up on platform.reflection.ai and an API key (checked 2026-11-04).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-17" }, "docsUrl": "https://developers.reflection.ai/introduction", "rateLimitsUrl": "https://developers.reflection.ai/rate-limits", "llmsTxt": "https://developers.reflection.ai/llms.txt", "openapi": "https://developers.reflection.ai/api-reference/openapi.yaml", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "official", "hosted", "model", "open-weights", "self-hosted", "free", "openai-compatible", "openapi", "llms-txt", "status-page", "apache-2.0", "gpu", "new" ], "lastRelease": "2026-10-16", "graded": true, "anchor": { "graded": true, "score": 53.4, "grade": "D", "agentReady": false, "rank": 950, "ranked": true, "rankOf": 1515, "categoryRank": 31, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 67, "maintenance": 50, "payments": 10, "reliability": 52, "schema": 84, "security": 45, "transparency": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "The Reflection API is OpenAI-compatible, with an OpenAPI document, strict structured outputs and tool calls, and error codes that say when to retry, and the weights are Apache-2.0. No price or rate-limit figure is published on a readable page, reasoning can't be turned off, and the API had two full outages on 14 and 15 October 2026.", "bestFor": "An OpenAI-compatible endpoint for a large open-weight coding and agent model, for builders who want the same model hosted or self-hosted under Apache-2.0.", "strengths": [ "OpenAI-compatible Chat Completions with `tool_choice`, parallel tool calls, strict tool schemas and `json_schema` structured outputs", "Error reference lists every status and code with whether to retry, and responses carry `Retry-After`, `x-should-retry` and `x-request-id`", "OpenAPI 3.0.3 document, llms.txt, a Markdown copy of every page and a docs MCP endpoint at developers.reflection.ai/mcp", "1,048,576-token context and up to 131,072 output tokens, with reasoning returned in its own `reasoning_content` field", "Weights in BF16, FP8 and NVFP4 under Apache-2.0, ungated, with Reflection's guides for SGLang, vLLM, TokenSpeed, Transformers, Helm and Dynamo" ], "weaknesses": [ "No SLA, and per-organisation rate limits and the daily token allowance are not published as numbers", "Reasoning is mandatory and defaults to `max`, and a tool-call turn returned without `reasoning_content` makes every later request in that session fail", "No per-token price, retention period or subprocessor list on a page we can read; Reflection's terms and pricing pages are on a host that disallows our crawler", "Two API incidents labelled full outages on 14 and 15 October 2026 and three partial ones to 3 November", "No changelog and no model deprecation notice period found" ], "agentNotes": [ "Send `reasoning_effort` `low` or `medium` for short tasks. The default is `max`, and `none` or `minimal` return `400 unsupported_value`", "Return every assistant message with its `reasoning_content` unchanged. If a turn comes back without it, start a new conversation, since later requests fail with `missing_required_parameter`", "Don't rely on `stop` or `seed`. Both are accepted and ignored, so truncate output in your own code", "Honour `x-should-retry: false` on a 429. It means a daily limit is spent and `Retry-After` counts to 00:00 UTC", "Set `max_completion_tokens` yourself on long prompts. The default budget isn't reduced to fit the prompt and includes reasoning tokens" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.4 } ], "editorialScores": { "ergonomics": 67, "maintenance": 50, "payments": 10, "reliability": 52, "schema": 84, "security": 45, "transparency": 39 }, "provenanceScore": 74, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai # or: npm install openai, with base URL https://api.reflection.ai/openai/v1", "http": "curl https://api.reflection.ai/openai/v1/chat/completions \\\n -H \"Authorization: Bearer $REFLECTION_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Beam-1.0-501B-A23B\",\n \"messages\": [\n {\"role\": \"user\", \"content\": \"Why is the sky blue?\"}\n ]\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/reflection-beam" }, "sameCompany": [ "reflection-mirror" ], "area": "models", "provenance": { "legalEntity": "Reflection AI", "domain": "reflection.ai", "domainRegistered": "2017-12-16", "domainNote": "RDAP gave a registration date of 16 December 2017 and a transfer on 15 March 2024 on 17 October 2026 (not re-read; RDAP is a JSON service), so the domain predates the current owner.", "endpointOnVendorDomain": true, "terms": "https://reflection.ai/legal/commercial-terms-of-service", "privacy": "https://reflection.ai/legal/privacy-policy", "statusPage": "https://status.reflection.ai", "changelog": "", "securityTxt": "unknown", "checked": "2026-11-04", "notes": [ "reflection.ai's robots.txt disallows ClaudeBot, Claude-Web and anthropic-ai (read 4 November 2026), so the commercial terms, the privacy policy and reflection.ai's security.txt weren't re-read and their 17 October readings are struck. Both URLs are kept as listed facts.", "The legal entity is the copyright holder in the NOTICE files of Reflection's Mirror repository (`Copyright 2026 Reflection AI`), as on the reflection-mirror listing, since the fuller name came from struck pages.", "developers.reflection.ai/.well-known/security.txt returned 404 on 17 October 2026. The model card names reports@reflection.ai for security vulnerabilities.", "No changelog or release notes found in the developers docs or llms.txt (4 November 2026).", "status.reflection.ai answers robots.txt with a plain 404 (no rules) and was read under it." ], "score": 74 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/reflection-beam.json", "live": { "slug": "reflection-beam", "probe": { "target": "https://api.reflection.ai/openai/v1", "method": "get", "lastAt": "2026-11-09T20:50:09.924360614Z", "lastOk": true, "lastStatus": 404, "lastMs": 381, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 371, "p95ms24h": 477, "samples24h": 244, "samples30d": 5656, "days": [ { "date": "2026-10-17", "probes": 21, "ok": 21 }, { "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": "2026-10-17T00:00:00Z" }, "vendorStatus": { "page": "https://status.reflection.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-11-09T20:46:58.142642652Z" }, "securityTxt": { "url": "https://reflection.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:05:44.796108773Z" }, "llmsTxt": { "url": "https://developers.reflection.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:40:46.521201731Z" }, "domain": { "domain": "reflection.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/reflection.ai", "checkedAt": "2026-11-08T15:05:49.098411889Z" }, "pages": [ { "url": "https://reflection.ai/legal/privacy-policy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-09T15:49:10.668465695Z", "changedAt": "2026-11-07T14:18:16.340207649Z", "fingerprint": "10e2a9daf712" }, { "url": "https://reflection.ai/legal/commercial-terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:49:07.991666444Z", "changedAt": "2026-11-07T14:18:12.998666824Z", "fingerprint": "34980ad61f53" } ], "models": { "reflection/beam-1.0-501b-a23b": { "openrouterId": "reflection/beam-1.0-501b-a23b", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 131072, "checkedAt": "2026-11-03T23:49:21.261568406Z" } }, "updatedAt": "2026-11-09T20:50:09.924360614Z" }, "streak": { "days": 23, "from": "2026-10-17", "sinceWatching": true, "best": 23 } }, "edges": [ { "by": 24, "category": "schema", "leads": "reflection-beam", "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 3.0.3 document, version 0.1.0, covering all three documented endpoints with typed schemas and error examples. A second 'Reflection client' base URL, https://api.reflection.ai/v1, is named with no endpoints documented (22 of 25). llms.txt, a Markdown copy of every page and a docs MCP endpoint (10). The guides say which reasoning effort suits which task, which OpenAI parameters are fixed, ignored or unsupported and which endpoints don't exist, so a model knows what not to send (17 of 20). `reasoning_effort` is an enum, strict JSON Schema for tools and `json_schema` output with the unsupported keywords and size limits listed, and unknown parameters return `unsupported_parameter` (15). curl, Python and TypeScript examples on every guide, and an error reference with every status, `error.code` and retry advice (15). The model ID carries its version and the models endpoint has a `shutdown_date` field, but no changelog or release notes were found (5 of 15). 22 + 10 + 17 + 15 + 15 + 5 = 84. The OpenAPI file is linked from llms.txt; the guides beyond the overview, models, rate limits, authentication and reference overview pages are per the 17 October check." }, "scores": { "a": 60, "b": 84 } }, { "by": 19, "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. API keys per project, revocable, shown once and disabled when their creator leaves, with no scopes, expiry or rotation API found (20 of 30). Training 10 of 20, partial kept. The API reference says each response's `reflection-data-retention` header reports `contributor` when content may be used to train Reflection's models \"under contributor terms your organization accepted\". The API safety page and commercial terms read on 17 October are struck (reflection.ai now disallows our crawler), so whether training is off by default can't be read. Retention 5 of 15, partial kept. The header can report `zero`, `standard` or `contributor`, and no readable page states periods or whether zero retention is offered. `x-request-id` on every response and the retention header, with usage dashboards behind the login and not read (5 of 15). Security programme 5 of 20 (was 6). The model card names reports@reflection.ai for security vulnerabilities; developers.reflection.ai has no security.txt (404 on 17 October), and the safety report and reflection.ai's security.txt can't be read. No bug bounty, SOC 2 or ISO 27001 found on a readable page. 20 + 10 + 5 + 5 + 5 = 45." }, "scores": { "a": 64, "b": 45 } }, { "by": 12, "category": "reliability", "leads": "reflection-beam", "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 lines, for the Reflection API. Status page at status.reflection.ai on incident.io, with API, platform and developer docs components and Atom and RSS feeds (20). In the 90 days to 4 November 2026 its incident pages list two full outages of the API component, a serving cluster down from 22:01 to 23:20 UTC on 14 October and elevated errors from 06:10 to 07:10 UTC on 15 October, each an hour or more, so two majors. Three more are labelled partial outages (16 October 02:28 to 02:38 UTC, 22 October rate-limit errors in two windows, 3 November about 20:10 to 21:38 UTC on one inference provider), read as minor. Two majors give 5. Rate limits are described per organisation in requests and tokens per minute and per day, with concurrency caps and headers, but no figures are published (5 of 15). 429 and 503 responses carry `Retry-After`, daily limits add `x-should-retry: false`, and the errors page says which codes to retry, with exponential backoff and jitter. There are no idempotency keys, and a chat completion is the billed call, so this run's value for retry guidance without idempotency keys gives 12 (was 15). No SLA found (0). The developers docs read on 4 November carry no beta or preview label for the API; the earlier beta reading came from reflection.ai pages now struck (10). 20 + 5 + 5 + 12 + 0 + 10 = 52." }, "scores": { "a": 40, "b": 52 } }, { "by": 10, "category": "payments", "leads": "muse-ember", "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": "Graded on the Reflection API. No x402, MPP or L402 (0). No per-token, plan or allocation price is published on any readable page; the beta pricing page on reflection.ai is struck (0, was 10). Free tier 10 of 20, partial kept. The rate-limits page describes a daily token allowance and asks those requesting higher limits whether they are \"interested in paid usage\", and the authentication page says some organisations must verify a credit card before keys work. A person signs up in the browser at platform.reflection.ai to get a key (0). 0 + 0 + 10 + 0 = 10." }, "scores": { "a": 20, "b": 10 } }, { "by": 5, "category": "ergonomics", "leads": "muse-ember", "name": "Agent ergonomics", "reasons": { "a": "Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.", "b": "Model reading (tool use, structured output, caching, context, batch, SDKs and defaults, errors). `tool_choice` of auto, none, required or a named function, `parallel_tool_calls`, strict tools and up to 128 tools. The tool-calling loop needs `reasoning_content` sent back on every assistant turn, and the docs say the model sometimes returns a tool-call step without it, after which every request in the session fails (12 of 20). `json_object` and strict `json_schema` (15). Cached prompt tokens are reported in `usage`, but caching can't be steered, the `prompt_cache_*` options are ignored and no cached price exists in the beta (5 of 10). 1,048,576-token context and 131,072 output tokens (15). No batch endpoint (0 of 10). No Reflection SDK, the OpenAI Python and TypeScript SDKs are documented, and defaults cost an agent turns, with reasoning always on at `max`, `stop` and `seed` ignored and a default output budget not reduced to fit the prompt (5 of 10). Machine-readable `error.code` values with `param`, `x-should-retry`, `Retry-After` and `x-request-id` (15). 12 + 15 + 5 + 15 + 0 + 5 + 15 = 67, per the 17 October reading of the developers guides, not re-read on 4 November beyond the models and rate-limits pages." }, "scores": { "a": 72, "b": 67 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "Reflection", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.reflection.ai/openai/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "b": "Weights under Apache-2.0. The hosted Reflection API is a closed service under Reflection's commercial terms of service", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-09", "b": "2026-10-16", "name": "Last release" }, { "a": "2026-10-02", "b": "2026-10-05", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "2026-08-28", "name": "Privacy policy last updated" }, { "a": "yes", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.", "question": "Which is better for AI agents, Muse Ember or Beam 1.0?" }, { "answer": "Both need an API key.", "question": "Do Muse Ember and Beam 1.0 need an API key?" }, { "answer": "Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Beam 1.0 at https://api.reflection.ai/openai/v1.", "question": "Can an agent call Muse Ember and Beam 1.0 without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 72 against 67", "Security \u0026 auth, 64 against 45", "Payments \u0026 pricing, 20 against 10" ], "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, 52 against 40", "Schema \u0026 documentation, 84 against 60" ], "also": null, "goodFor": "An OpenAI-compatible endpoint for a large open-weight coding and agent model, for builders who want the same model hosted or self-hosted under Apache-2.0.", "slug": "reflection-beam", "watchFor": "No SLA, and per-organisation rate limits and the daily token allowance are not published as numbers" } ], "history": { "muse-ember": null, "reflection-beam": [ { "causes": [ "evidence", "correction" ], "date": "2026-11-04", "delta": 0.1, "from": 53.3, "fromGrade": "D", "to": 53.4, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.1, "from": 53.4, "fromGrade": "D", "to": 53.3, "toGrade": "D" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-reflection-beam.json", "title": "Amazon Bedrock vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-reflection-beam" }, { "json": 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"https://www.anchorterminal.com/compare/reflection-beam-vs-stepfun-api.json", "title": "Beam 1.0 vs StepFun API", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-swe-2-flash.json", "title": "Beam 1.0 vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-tencent-tokenhub.json", "title": "Beam 1.0 vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-together-inference.json", "title": "Beam 1.0 vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-vercel-ai-gateway.json", "title": "Beam 1.0 vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai.json", "title": "Beam 1.0 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/reflection-beam-vs-xai-api.json", "title": "Beam 1.0 vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/reflection-beam-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-reflection-beam.json", "title": "Nemotron 3.5 Super VL vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-reflection-beam" } ], "panel": null, "scores": [ { "by": 12, "edge": "reflection-beam", "key": "reliability", "muse-ember": 40, "name": "Reliability", "reflection-beam": 52, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 24, "edge": "reflection-beam", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "reflection-beam": 84, "weight": 13 }, { "by": 5, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "reflection-beam": 67, "weight": 13 }, { "by": 19, "edge": "muse-ember", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "reflection-beam": 45, "weight": 14 }, { "by": 10, "edge": "muse-ember", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "reflection-beam": 10, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 4, "edge": "reflection-beam", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "reflection-beam": 50, "weight": 7 }, { "by": 4, "edge": "reflection-beam", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "reflection-beam": 57, "weight": 7 } ], "summary": "Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "muse-ember": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "reflection-beam": "The Reflection API is OpenAI-compatible, with an OpenAPI document, strict structured outputs and tool calls, and error codes that say when to retry, and the weights are Apache-2.0. No price or rate-limit figure is published on a readable page, reasoning can't be turned off, and the API had two full outages on 14 and 15 October 2026." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam", "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.min.md" }, "markdown": "Beam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing. Both do llm inference.\n\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Beam 1.0: grade D, 53.4/100, rank #950 of 1515. Markdown https://www.anchorterminal.com/tools/reflection-beam.md · JSON https://www.anchorterminal.com/api/v1/tools/reflection-beam.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Muse Ember (D)\n\nGood for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.\n\nAhead on:\n- Agent ergonomics, 72 against 67\n- Security \u0026 auth, 64 against 45\n- Payments \u0026 pricing, 20 against 10\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n### Beam 1.0 (D)\n\nGood for: An OpenAI-compatible endpoint for a large open-weight coding and agent model, for builders who want the same model hosted or self-hosted under Apache-2.0.\n\nAhead on:\n- Reliability, 52 against 40\n- Schema \u0026 documentation, 84 against 60\n\nWatch for: No SLA, and per-organisation rate limits and the daily token allowance are not published as numbers\n\n\n## Why the edge\n\n### Schema \u0026 documentation: Beam 1.0 +24\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- Beam 1.0 (84): Model reading. OpenAPI 3.0.3 document, version 0.1.0, covering all three documented endpoints with typed schemas and error examples. A second 'Reflection client' base URL, https://api.reflection.ai/v1, is named with no endpoints documented (22 of 25). llms.txt, a Markdown copy of every page and a docs MCP endpoint (10). The guides say which reasoning effort suits which task, which OpenAI parameters are fixed, ignored or unsupported and which endpoints don't exist, so a model knows what not to send (17 of 20). `reasoning_effort` is an enum, strict JSON Schema for tools and `json_schema` output with the unsupported keywords and size limits listed, and unknown parameters return `unsupported_parameter` (15). curl, Python and TypeScript examples on every guide, and an error reference with every status, `error.code` and retry advice (15). The model ID carries its version and the models endpoint has a `shutdown_date` field, but no changelog or release notes were found (5 of 15). 22 + 10 + 17 + 15 + 15 + 5 = 84. The OpenAPI file is linked from llms.txt; the guides beyond the overview, models, rate limits, authentication and reference overview pages are per the 17 October check.\n\n### Security \u0026 auth: Muse Ember +19\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- Beam 1.0 (45): Model reading. API keys per project, revocable, shown once and disabled when their creator leaves, with no scopes, expiry or rotation API found (20 of 30). Training 10 of 20, partial kept. The API reference says each response's `reflection-data-retention` header reports `contributor` when content may be used to train Reflection's models \"under contributor terms your organization accepted\". The API safety page and commercial terms read on 17 October are struck (reflection.ai now disallows our crawler), so whether training is off by default can't be read. Retention 5 of 15, partial kept. The header can report `zero`, `standard` or `contributor`, and no readable page states periods or whether zero retention is offered. `x-request-id` on every response and the retention header, with usage dashboards behind the login and not read (5 of 15). Security programme 5 of 20 (was 6). The model card names reports@reflection.ai for security vulnerabilities; developers.reflection.ai has no security.txt (404 on 17 October), and the safety report and reflection.ai's security.txt can't be read. No bug bounty, SOC 2 or ISO 27001 found on a readable page. 20 + 10 + 5 + 5 + 5 = 45.\n\n### Reliability: Beam 1.0 +12\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- Beam 1.0 (52): Hosted lines, for the Reflection API. Status page at status.reflection.ai on incident.io, with API, platform and developer docs components and Atom and RSS feeds (20). In the 90 days to 4 November 2026 its incident pages list two full outages of the API component, a serving cluster down from 22:01 to 23:20 UTC on 14 October and elevated errors from 06:10 to 07:10 UTC on 15 October, each an hour or more, so two majors. Three more are labelled partial outages (16 October 02:28 to 02:38 UTC, 22 October rate-limit errors in two windows, 3 November about 20:10 to 21:38 UTC on one inference provider), read as minor. Two majors give 5. Rate limits are described per organisation in requests and tokens per minute and per day, with concurrency caps and headers, but no figures are published (5 of 15). 429 and 503 responses carry `Retry-After`, daily limits add `x-should-retry: false`, and the errors page says which codes to retry, with exponential backoff and jitter. There are no idempotency keys, and a chat completion is the billed call, so this run's value for retry guidance without idempotency keys gives 12 (was 15). No SLA found (0). The developers docs read on 4 November carry no beta or preview label for the API; the earlier beta reading came from reflection.ai pages now struck (10). 20 + 5 + 5 + 12 + 0 + 10 = 52.\n\n### Payments \u0026 pricing: Muse Ember +10\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- Beam 1.0 (10): Graded on the Reflection API. No x402, MPP or L402 (0). No per-token, plan or allocation price is published on any readable page; the beta pricing page on reflection.ai is struck (0, was 10). Free tier 10 of 20, partial kept. The rate-limits page describes a daily token allowance and asks those requesting higher limits whether they are \"interested in paid usage\", and the authentication page says some organisations must verify a credit card before keys work. A person signs up in the browser at platform.reflection.ai to get a key (0). 0 + 0 + 10 + 0 = 10.\n\n### Agent ergonomics: Muse Ember +5\n\n- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n- Beam 1.0 (67): Model reading (tool use, structured output, caching, context, batch, SDKs and defaults, errors). `tool_choice` of auto, none, required or a named function, `parallel_tool_calls`, strict tools and up to 128 tools. The tool-calling loop needs `reasoning_content` sent back on every assistant turn, and the docs say the model sometimes returns a tool-call step without it, after which every request in the session fails (12 of 20). `json_object` and strict `json_schema` (15). Cached prompt tokens are reported in `usage`, but caching can't be steered, the `prompt_cache_*` options are ignored and no cached price exists in the beta (5 of 10). 1,048,576-token context and 131,072 output tokens (15). No batch endpoint (0 of 10). No Reflection SDK, the OpenAI Python and TypeScript SDKs are documented, and defaults cost an agent turns, with reasoning always on at `max`, `stop` and `seed` ignored and a default output budget not reduced to fit the prompt (5 of 10). Machine-readable `error.code` values with `param`, `x-should-retry`, `Retry-After` and `x-request-id` (15). 12 + 15 + 5 + 15 + 0 + 5 + 15 = 67, per the 17 October reading of the developers guides, not re-read on 4 November beyond the models and rate-limits pages.\n\n## Score by category\n\n| Category | Weight | Muse Ember | Beam 1.0 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 52 | Beam 1.0 +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 84 | Beam 1.0 +24 |\n| Agent ergonomics | 13% (16.2 this run) | 72 | 67 | Muse Ember +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 45 | Muse Ember +19 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 10 | Muse Ember +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 50 | Beam 1.0 +4 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 53 | 57 | Beam 1.0 +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **51.8 · D** | **53.4 · D** | |\n\n## Facts side by side\n\n| Fact | Muse Ember | Beam 1.0 |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | Reflection |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.reflection.ai/openai/v1` |\n| Transports | | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer | Weights under Apache-2.0. The hosted Reflection API is a closed service under Reflection's commercial terms of service |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-09 | 2026-10-16 |\n| Terms last updated | 2026-10-02 | 2026-10-05 |\n| Privacy policy last updated | couldn't be read | 2026-08-28 |\n| Customer content may train models | yes | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | yes |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Muse Ember\n\nNo score change since it was listed.\n\n### Beam 1.0\n\n- +0.1 on 4 November 2026: new evidence, correction\n- -0.1 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**Beam 1.0.** The Reflection API is OpenAI-compatible, with an OpenAPI document, strict structured outputs and tool calls, and error codes that say when to retry, and the weights are Apache-2.0. No price or rate-limit figure is published on a readable page, reasoning can't be turned off, and the API had two full outages on 14 and 15 October 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### Beam 1.0\n\n1. Send `reasoning_effort` `low` or `medium` for short tasks. The default is `max`, and `none` or `minimal` return `400 unsupported_value`\n2. Return every assistant message with its `reasoning_content` unchanged. If a turn comes back without it, start a new conversation, since later requests fail with `missing_required_parameter`\n3. Don't rely on `stop` or `seed`. Both are accepted and ignored, so truncate output in your own code\n4. Honour `x-should-retry: false` on a 429. It means a daily limit is spent and `Retry-After` counts to 00:00 UTC\n5. Set `max_completion_tokens` yourself on long prompts. The default budget isn't reduced to fit the prompt and includes reasoning tokens\n\n## Questions\n\n### Which is better for AI agents, Muse Ember or Beam 1.0?\n\nBeam 1.0 scores 53.4 (D) on agent readiness against Muse Ember's 51.8 (D), and leads in 4 of 7 scored categories. Muse Ember leads on agent ergonomics, security \u0026 auth and payments \u0026 pricing.\n\n### Do Muse Ember and Beam 1.0 need an API key?\n\nBoth need an API key.\n\n### Can an agent call Muse Ember and Beam 1.0 without installing anything?\n\nYes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and Beam 1.0 at https://api.reflection.ai/openai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json, and with the fewest tokens: https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"muse-ember\", \"b\": \"reflection-beam\"}`. From a terminal: `anchor compare muse-ember reflection-beam`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/muse-ember.json and https://www.anchorterminal.com/api/v1/tools/reflection-beam.json\n\n## Other comparisons with Muse Ember or Beam 1.0\n\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Amazon Bedrock vs Beam 1.0](https://www.anchorterminal.com/compare/amazon-bedrock-vs-reflection-beam.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Claude API vs Beam 1.0](https://www.anchorterminal.com/compare/anthropic-api-vs-reflection-beam.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Antseed vs Beam 1.0](https://www.anchorterminal.com/compare/antseed-vs-reflection-beam.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- 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xAI API (Grok)](https://www.anchorterminal.com/compare/reflection-beam-vs-xai-api.md)\n- [Nemotron 3.5 Super VL vs Beam 1.0](https://www.anchorterminal.com/compare/nemotron-3-5-super-vl-vs-reflection-beam.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/" }, { 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