{ "data": { "a": { "slug": "gemini-api", "name": "Gemini Developer API", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "inference", "summary": "Google's API for Gemini models, including content generation and agent interactions.", "url": "https://www.anchorterminal.com/tools/gemini-api", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-api.json", "dns": "gemini-api.grade.anchorterminal.com", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta", "packages": [ { "registry": "pypi", "name": "google-genai", "main": true }, { "registry": "npm", "name": "@google/genai", "main": true } ], "auth": "api-key", "authNotes": "`x-goog-api-key` header with a key from AI Studio. A Google account is needed to make one.", "pricing": "freemium", "pricingNotes": "Free tier on 3.8 Flash, 3.6 Flash and the Flash-Lite models, not on Gemini 4 Argon or 3.1 Pro preview, and free-tier content is used to improve Google products. Gemini 4 Argon (`gemini-4-argon`) costs $2/$10 per million tokens through 2026-11-30, then $4/$20, with implicit caching only. 3.1 Pro preview costs $2/$12 per million tokens, $4/$18 over 200K. 3.8 Flash is $0.75/$3.75 until 2026-12-31, then $1.50/$7.50. Cached input 0.1x. Nano Banana 2.1 (`gemini-nano-banana-2.1`, images) is paid tier only at $1.50 input and $30 per million image output tokens, about $0.034 a 1K image, $0.050 at 2K and $0.113 at 4K, half that in batch. Search grounding 5,000 free a month, then $14 per 1,000. Batch half price (https://ai.google.dev/gemini-api/docs/pricing).", "priceSummary": "from $0.25 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. The free tier needs a Google account and paid use needs billing on a Cloud project.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3992, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://ai.google.dev/gemini-api/docs", "rateLimitsUrl": "https://ai.google.dev/gemini-api/docs/rate-limits", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "free-tier", "no-card", "llms-txt" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 62.2, "grade": "B", "agentReady": false, "rank": 568, "ranked": true, "rankOf": 1515, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -3, "negativeNotes": [ "2026-10-08. Google deprecated the generally available `gemini-3.7-flash` and `gemini-3.5-flash` and routed API requests for them to `gemini-3.8-flash` and `gemini-3.6-flash` the same day, announced in that day's changelog. Callers pinned to those names got a different model without notice, the reading DeepSeek API takes for its 10 September routing, so 3 points (https://ai.google.dev/gemini-api/docs/changelog, https://ai.google.dev/gemini-api/docs/deprecations)." ], "verdict": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.", "bestFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "strengths": [ "Free tier on 3.8 Flash and Flash-Lite with no billing account", "Gemini 4 Argon went GA on 13 October 2026 with a 1,048,576-token shared window and long decode up to 1M tokens", "Backoff guidance with jitter, and the Python SDK retries transient errors four times", "New keys are bound to a service account and restricted to the Gemini API, and unrestricted standard keys are rejected", "A keyless Discovery document types the v1beta GenerateContent surface, and function calling can be forced to named tools" ], "weaknesses": [ "Free-tier prompts and responses improve Google products and may be read by human reviewers", "The API reads an OAuth access token from the `access_token` query parameter", "No SLA and no zero-retention route found for the Developer API", "Gemini 4 Argon isn't on the free tier and doesn't support explicit context caching", "Shutdown dates are 'earliest possible' with no stated minimum notice, and one preview agent got 15 days" ], "agentNotes": [ "Never send customer data through a free-tier key. Link billing first", "Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL", "Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403", "Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400", "On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.2 } ], "editorialScores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 55 }, "provenanceScore": 89, "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 google-genai # or: npm i @google/genai", "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-3.8-flash:generateContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"contents\":[{\"parts\":[{\"text\":\"hello\"}]}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/gemini-api" }, "sameCompany": [ "vertex-ai", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-cloud-tts", "gemini-live", "google-adk", "google-memory-bank", "google-secret-manager", "google-cloud-document-ai", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api" ], "sameModel": [ "gemma-4-26b-a4b-it", "gemma-4-31b-it" ], "area": "models", "unitPrices": [ { "item": "Search grounding over 5,000 a month", "unit": "1k-requests", "usd": 14 }, { "item": "Nano Banana 2.1 image, 1K", "unit": "image", "usd": 0.0336, "note": "1,120 output tokens at $30 per million. Paid tier only, half price in batch" }, { "item": "Nano Banana 2.1 image, 2K", "unit": "image", "usd": 0.0504, "note": "1,680 output tokens" }, { "item": "Nano Banana 2.1 image, 4K", "unit": "image", "usd": 0.113, "note": "3,780 output tokens" } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-11-01", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-api.json", "live": { "slug": "gemini-api", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta", "method": "get", "lastAt": "2026-11-09T20:49:52.233616427Z", "lastOk": true, "lastStatus": 404, "lastMs": 32, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 37, "p95ms24h": 98, "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": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "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://aistudio.google.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-09T20:23:47.559735588Z" }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.0.0", "released": "2026-11-06", "seenAt": "2026-11-09T02:20:44.470512881Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.0.0", "seenAt": "2026-11-09T02:20:43.608527005Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.0.0", "released": "2026-11-06", "seenAt": "2026-11-09T02:20:43.496074448Z" } ], "githubStars": 4058, "npmWeekly": 31882943, "pypiWeekly": 38426138, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-09T16:06:24.657280803Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:39:36.621155174Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-11-08T15:01:59.545325134Z" }, "pages": [ { "url": "https://ai.google.dev/gemini-api/docs/changelog", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:30:20.454005581Z", "changedAt": "2026-11-09T15:30:20.454005581Z", "fingerprint": "b6484ac061f9" }, { "url": "https://ai.google.dev/gemini-api/docs/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:30:22.799102803Z", "changedAt": "2026-11-07T13:58:57.400152684Z", "fingerprint": "f0958caa84a5" }, { "url": "https://ai.google.dev/gemini-api/docs/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:30:24.920393365Z", "changedAt": "2026-11-06T13:21:47.729671724Z", "fingerprint": "d3b5522104e1" }, { "url": "https://ai.google.dev/gemini-api/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:30:26.847243224Z", "changedAt": "2026-11-03T07:41:20.562939041Z", "fingerprint": "07b98470ed02" } ], "models": { "gemini-3.1-flash-lite": { "openrouterId": "google/gemini-3.1-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gemini-3.1-pro-preview": { "openrouterId": "google/gemini-3.1-pro-preview", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gemini-3.5-flash-lite": { "openrouterId": "google/gemini-3.5-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gemini-3.8-flash": { "openrouterId": "google/gemini-3.8-flash", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "gemini-4-argon": { "openrouterId": "google/gemini-4-argon", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 943718, "checkedAt": "2026-11-08T23:49:27.027409601Z" } }, "github": { "repo": "googleapis/python-genai", "checkedAt": "2026-11-09T09:47:04.856295504Z", "stars": 4058, "archived": false, "issuesEnabled": true, "pushedAt": "2026-11-08T20:32:42Z", "openIssues": 224, "openPRs": 170, "issuesOpened90d": 83, "issuesClosed90d": 49, "prsMerged90d": 219, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-11-06T02:02:46Z" }, "replies": { "sampled": 25, "unanswered": 9, "medianDays": 2.6 } }, "updatedAt": "2026-11-09T20:49:52.233616427Z" }, "streak": { "days": 17, "from": "2026-10-23", "last": { "kind": "shutdown", "date": "2026-10-23", "what": "`deep-research-pro-preview-12-2025` agent shuts down", "source": "https://ai.google.dev/gemini-api/docs/changelog" }, "next": { "kind": "shutdown", "date": "2026-11-17", "what": "`gemini-3.1-flash-tts-preview`, `gemini-2.5-flash-preview-tts` and `gemini-2.5-pro-preview-tts` shut down at the earliest. Move to `gemini-3.8-flash-tts` or `gemini-3.8-flash-lite-tts`", "source": "https://ai.google.dev/gemini-api/docs/deprecations" }, "best": 17 } }, "answer": "Gemini Developer API scores 62.2 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "b": { "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 } }, "edges": [ { "by": 37, "category": "maintenance", "leads": "gemini-api", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. `gemini-4-argon` went GA on 13 October and the newest changelog entry, on 30 October, takes the Computer Use tool to GA (30). The deprecations page lists earliest possible shutdown dates and promises advance notice, with no stated minimum, and `deep-research-pro-preview-12-2025` was announced on 8 October for shutdown on 23 October (4 of 12). Shutdown dates in the 90 days from 3 August include Imagen 4 on 17 August, Robotics ER 1.6 on 31 August, `antigravity-preview-05-2026` on 5 October, the Veo 3.1 previews and `gemini-omni-flash-preview` on 22 October and `deep-research-pro-preview-12-2025` on 23 October, each in the table with a replacement, and 3.7 and 3.5 Flash were deprecated on 8 October with requests routed to newer models (4 of 8). 16 dated changelog entries from 13 August to 30 October (15 of 15). SDK repository replies not sampled (5 of 10). Current official SDKs, `google-genai` 2.34.0 and `@google/genai` 2.32.0, both tagged on 29 October, with releases about weekly through October (15). Supported runtimes stated, Python 3.11 and later since 2.34.0 dropped 3.10, Node 20 and later (10).", "b": "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." }, "scores": { "a": 83, "b": 46 } }, { "by": 25, "category": "schema", "leads": "gemini-api", "name": "Schema \u0026 documentation", "reasons": { "a": "A keyless Discovery document at generativelanguage.googleapis.com/$discovery/rest?version=v1beta (revision 20261101, re-read on 1 November) types 86 methods and 225 schemas for GenerateContent, files, batches, caching and models, but not the Interactions API the guides now lead with (20 of 25). llms.txt at ai.google.dev/gemini-api/docs/llms.txt, with Markdown twins of each page (10). Reference not read in full, and the changelog and latest-model guide name the recommended models (15 of 20). Structured output accepts JSON Schema with enums, `minimum`, `maximum`, `prefixItems` and `$ref`, but the server ignores `minLength`, `maxLength`, `minProperties`, `maxProperties` and `uniqueItems`, per the structured output page re-read on 1 November (10 of 15). Examples on every guide and an API errors page with HTTP, blocked and content codes (15). Dated changelog and model versions (15).", "b": "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." }, "scores": { "a": 85, "b": 60 } }, { "by": 21, "category": "ergonomics", "leads": "gemini-api", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed).", "b": "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." }, "scores": { "a": 93, "b": 72 } }, { "by": 20, "category": "payments", "leads": "gemini-api", "name": "Payments \u0026 pricing", "reasons": { "a": "No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0).", "b": "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." }, "scores": { "a": 40, "b": 20 } }, { "by": 19, "category": "transparency", "leads": "gemini-api", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20).", "b": "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." }, "scores": { "a": 72, "b": 53 } }, { "by": 14, "category": "security", "leads": "muse-ember", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20).", "b": "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." }, "scores": { "a": 50, "b": 64 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Google", "b": "Meta", "name": "Vendor" }, { "a": "https://generativelanguage.googleapis.com/v1beta", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0 (SDKs)", "b": "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", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-30", "b": "2026-11-09", "name": "Last release" }, { "a": "2026-04-28", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "2026-10-01", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "4k stars", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemini Developer API scores 62.2 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "question": "Which is better for AI agents, Gemini Developer API or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do Gemini Developer API and Muse Ember need an API key?" }, { "answer": "Yes. Gemini Developer API has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call Gemini Developer API and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 45 against 40", "Schema \u0026 documentation, 85 against 60", "Agent ergonomics, 93 against 72", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 83 against 46", "Transparency \u0026 trust, 72 against 53" ], "also": [ "Free to start without a card" ], "goodFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "slug": "gemini-api", "watchFor": "Free-tier prompts and responses improve Google products and may be read by human reviewers" }, { "aheadOn": [ "Security \u0026 auth, 64 against 50" ], "also": [ "No incidents deducted, where Gemini Developer API loses 3 points for them" ], "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "gemini-api": [ { "causes": [ "product", "evidence", "correction" ], "date": "2026-11-01", "delta": -3.3, "from": 65.5, "fromGrade": "B", "to": 62.2, "toGrade": "B" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 65.6, "fromGrade": "B", "to": 65.5, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-28", "delta": 0.1, "from": 65.5, "fromGrade": "B", "to": 65.6, "toGrade": "B" } ], "muse-ember": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api.json", "title": "Amazon Bedrock vs 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Developer API vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.json", "title": "Gemini Developer API vs StepFun API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash.json", "title": "Gemini Developer API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub.json", "title": "Gemini Developer API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference.json", "title": "Gemini Developer API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference" }, { "json": 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"url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember" }, { "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/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/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/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/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/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.json", "title": "Muse Ember vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.json", "title": "Muse Ember vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "title": "Muse Ember vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-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" } ], "panel": null, "scores": [ { "by": 5, "edge": "gemini-api", "gemini-api": 45, "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 25, "edge": "gemini-api", "gemini-api": 85, "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 21, "edge": "gemini-api", "gemini-api": 93, "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 14, "edge": "muse-ember", "gemini-api": 50, "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 20, "edge": "gemini-api", "gemini-api": 40, "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 37, "edge": "gemini-api", "gemini-api": 83, "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 19, "edge": "gemini-api", "gemini-api": 72, "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini Developer API scores 62.2 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "gemini-api": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.min.md" }, "markdown": "Gemini Developer API scores 62.2 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth. Both do llm inference.\n\n- Gemini Developer API: grade B, 62.2/100, rank #568 of 1515. Markdown https://www.anchorterminal.com/tools/gemini-api.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-api.json\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- 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### Gemini Developer API (B)\n\nGood for: Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.\n\nAhead on:\n- Reliability, 45 against 40\n- Schema \u0026 documentation, 85 against 60\n- Agent ergonomics, 93 against 72\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 83 against 46\n- Transparency \u0026 trust, 72 against 53\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Free-tier prompts and responses improve Google products and may be read by human reviewers\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- Security \u0026 auth, 64 against 50\n\nAlso in its favour:\n- No incidents deducted, where Gemini Developer API loses 3 points for them\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Gemini Developer API +37\n\n- Gemini Developer API (83): Model reading. `gemini-4-argon` went GA on 13 October and the newest changelog entry, on 30 October, takes the Computer Use tool to GA (30). The deprecations page lists earliest possible shutdown dates and promises advance notice, with no stated minimum, and `deep-research-pro-preview-12-2025` was announced on 8 October for shutdown on 23 October (4 of 12). Shutdown dates in the 90 days from 3 August include Imagen 4 on 17 August, Robotics ER 1.6 on 31 August, `antigravity-preview-05-2026` on 5 October, the Veo 3.1 previews and `gemini-omni-flash-preview` on 22 October and `deep-research-pro-preview-12-2025` on 23 October, each in the table with a replacement, and 3.7 and 3.5 Flash were deprecated on 8 October with requests routed to newer models (4 of 8). 16 dated changelog entries from 13 August to 30 October (15 of 15). SDK repository replies not sampled (5 of 10). Current official SDKs, `google-genai` 2.34.0 and `@google/genai` 2.32.0, both tagged on 29 October, with releases about weekly through October (15). Supported runtimes stated, Python 3.11 and later since 2.34.0 dropped 3.10, Node 20 and later (10).\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\n### Schema \u0026 documentation: Gemini Developer API +25\n\n- Gemini Developer API (85): A keyless Discovery document at generativelanguage.googleapis.com/$discovery/rest?version=v1beta (revision 20261101, re-read on 1 November) types 86 methods and 225 schemas for GenerateContent, files, batches, caching and models, but not the Interactions API the guides now lead with (20 of 25). llms.txt at ai.google.dev/gemini-api/docs/llms.txt, with Markdown twins of each page (10). Reference not read in full, and the changelog and latest-model guide name the recommended models (15 of 20). Structured output accepts JSON Schema with enums, `minimum`, `maximum`, `prefixItems` and `$ref`, but the server ignores `minLength`, `maxLength`, `minProperties`, `maxProperties` and `uniqueItems`, per the structured output page re-read on 1 November (10 of 15). Examples on every guide and an API errors page with HTTP, blocked and content codes (15). Dated changelog and model versions (15).\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\n### Agent ergonomics: Gemini Developer API +21\n\n- Gemini Developer API (93): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed).\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\n### Payments \u0026 pricing: Gemini Developer API +20\n\n- Gemini Developer API (40): No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0).\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\n### Transparency \u0026 trust: Gemini Developer API +19\n\n- Gemini Developer API (72): Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20).\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\n### Security \u0026 auth: Muse Ember +14\n\n- Gemini Developer API (50): Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20).\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\n## Score by category\n\n| Category | Weight | Gemini Developer API | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 45 | 40 | Gemini Developer API +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 60 | Gemini Developer API +25 |\n| Agent ergonomics | 13% (16.2 this run) | 93 | 72 | Gemini Developer API +21 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 64 | Muse Ember +14 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 20 | Gemini Developer API +20 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 83 | 46 | Gemini Developer API +37 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 72 | 53 | Gemini Developer API +19 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **62.2 · B** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | Gemini Developer API | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Google | Meta |\n| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta` | `https://api.meta.ai/v1` |\n| Transports | HTTP | |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | 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 |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-30 | 2026-11-09 |\n| Terms last updated | 2026-04-28 | 2026-10-02 |\n| Privacy policy last updated | 2026-10-01 | couldn't be read |\n| Customer content may train models | yes | yes |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 4k stars | none |\n| Agent reviews | 3.5/5 (2) | none |\n\n## How the scores have moved\n\n### Gemini Developer API\n\n- -3.3 on 1 November 2026: product, new evidence, correction\n- -0.1 on 28 October 2026: correction\n- +0.1 on 28 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Gemini Developer API.** Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.\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## Before you call either\n\n### Gemini Developer API\n\n1. Never send customer data through a free-tier key. Link billing first\n2. Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL\n3. Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403\n4. Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400\n5. On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution\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## Questions\n\n### Which is better for AI agents, Gemini Developer API or Muse Ember?\n\nGemini Developer API scores 62.2 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.\n\n### Do Gemini Developer API and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call Gemini Developer API and Muse Ember without installing anything?\n\nYes. Gemini Developer API has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Muse Ember at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-api\", \"b\": \"muse-ember\"}`. 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