{ "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", "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" }, { "registry": "npm", "name": "@google/genai" } ], "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 and Flash-Lite, not on 3.1 Pro preview, and free-tier content is used to improve Google products. 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": "2026-10-08", "graded": true, "anchor": { "graded": true, "score": 65.5, "grade": "B", "agentReady": false, "rank": 323, "ranked": true, "rankOf": 993, "categoryRank": 10, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 90, "maintenance": 83, "payments": 40, "reliability": 40, "schema": 85, "security": 60, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-12" }, "negative": 0, "verdict": "Free tier on 3.8 Flash and Flash-Lite with no billing account. Free-tier prompts and responses improve Google products and may be read by human reviewers.", "bestFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card.", "strengths": [ "Free tier on 3.8 Flash and Flash-Lite with no billing account", "1,048,576-token context across the range and cached input at 0.1x", "Backoff guidance with jitter, and the Python SDK retries transient errors four times", "Keys can be restricted to the Gemini API and by IP or origin, and unrestricted keys are rejected from 28 May 2026", "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", "Per-model rate limits are only visible inside AI Studio", "No SLA and no zero-retention route found for the Developer API", "The only Pro model, `gemini-3.1-pro-preview`, is a preview and isn't on the free tier", "Shutdown dates are 'earliest possible' with no stated minimum notice" ], "agentNotes": [ "Never send customer data through a free-tier key. Link billing first", "Don't request Gemini 4 Argon here. It has no Developer API model ID as of 5 October 2026", "Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403", "Stop sending temperature, top_p and top_k. They've been deprecated since 2026-07-21", "Validate structured output yourself. The docs don't promise schema-constrained decoding" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 65.5 } ], "editorialScores": { "ergonomics": 90, "maintenance": 83, "payments": 40, "reliability": 40, "schema": 85, "security": 60, "transparency": 55 }, "provenanceScore": 89 }, "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": [ "gemini-embedding", "vertex-ai-tuning", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "gemini-live", "google-adk", "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-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api" ], "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": "2026-10-05", "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": "2026-10-12T22:35:26.110556033Z", "lastOk": true, "lastStatus": 404, "lastMs": 63, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 35, "p95ms24h": 73, "samples24h": 248, "samples30d": 4117, "days": [ { "date": "2026-09-27", "probes": 132, "ok": 132 }, { "date": "2026-09-28", "probes": 285, "ok": 285 }, { "date": "2026-09-29", "probes": 286, "ok": 286 }, { "date": "2026-09-30", "probes": 286, "ok": 286 }, { "date": "2026-10-01", "probes": 276, "ok": 276 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "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": 233, "ok": 233 } ] }, "vendorStatus": { "page": "https://aistudio.google.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-12T21:46:58.833463992Z" }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.29.0", "released": "2026-10-07", "seenAt": "2026-10-12T22:02:45.37084288Z" }, { "registry": "npm", "name": "@google/genai", "version": "2.28.0", "seenAt": "2026-10-12T22:02:44.4732732Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.29.0", "released": "2026-10-07", "seenAt": "2026-10-12T22:02:44.358133213Z" } ], "githubStars": 4018, "npmWeekly": 24707752, "pypiWeekly": 34468136, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-12T15:43:27.023051831Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-12T14:09:09.427325061Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-11T13:17:33.092177011Z" }, "pages": [ { "url": "https://ai.google.dev/gemini-api/docs/changelog", "kind": "deprecations", "status": 200, "checkedAt": "2026-10-12T19:45:10.837825841Z", "changedAt": "2026-10-12T19:45:10.837825841Z", "fingerprint": "74d58ea0b366" }, { "url": "https://ai.google.dev/gemini-api/docs/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-10-12T19:45:13.668579176Z", "changedAt": "2026-10-12T19:45:13.668579176Z", "fingerprint": "a4caba671b85" }, { "url": "https://ai.google.dev/gemini-api/docs/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-10-12T19:45:15.120495556Z", "changedAt": "2026-10-10T18:56:51.551192358Z", "fingerprint": "e1b57499ea81" }, { "url": "https://ai.google.dev/gemini-api/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-12T19:45:17.326627816Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "95a70f72fb73" } ], "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": "2026-10-11T22:59:28.731564938Z" }, "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": "2026-10-11T22:59:28.731564938Z" }, "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": "2026-10-11T22:59:28.731564938Z" }, "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": "2026-10-11T22:59:28.731564938Z" } }, "updatedAt": "2026-10-12T22:35:26.110556033Z" } }, "answer": "Gemini Developer API scores 65.5 (B) on agent readiness against Meta Model API's 52.2 (D), and leads in 5 of 7 scored categories.", "b": { "slug": "meta-model-api", "name": "Meta Model API", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Meta Model API is Meta's pay-as-you-go API for its hosted Muse models, including Muse Wonder and Muse Spark, through OpenAI-style Responses and Chat Completions endpoints and an Anthropic-style Messages endpoint, plus image, speech and segmentation routes.", "url": "https://www.anchorterminal.com/tools/meta-model-api", "markdownUrl": "https://www.anchorterminal.com/tools/meta-model-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/meta-model-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/meta-model-api.json", "license": "Closed service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party.", "transports": [ "http" ], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer API key created on the API keys tab of the Model API dashboard after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team, are shown once and are revoked by deletion. No key scopes were found, and every key on a team shares the team's rate limits.", "pricing": "usage", "pricingNotes": "Muse Wonder is $4.00 per million input tokens, $0.20 cached and $20.00 per million output tokens, Standard mode only (checked 2026-10-12). Muse Spark is $1.25, $0.15 and $4.25, or $0.10, $0.002 and $0.20 in Contributor mode, where Meta may train on the content. Billed in arrears to a card. No free allowance was found.", "priceSummary": "$2.50 / 1k req", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in dev.meta.ai/llms.txt, the pricing page, the API reference or the billing help pages, checked 12 October 2026. Usage is charged to a card at a payment threshold or on the 1st of each month.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-12" }, "docsUrl": "https://dev.meta.ai/docs/overview", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm", "image.generate", "image.edit", "speech.stt", "speech.streaming" ], "tags": [ "official", "hosted", "model", "usage-priced", "closed-source", "llms-txt", "multimodal", "streaming", "openai-compatible", "anthropic-compatible", "card-required" ], "lastRelease": "2026-10-12", "graded": true, "anchor": { "graded": true, "score": 52.2, "grade": "D", "agentReady": false, "rank": 757, "ranked": true, "rankOf": 993, "categoryRank": 16, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-12" }, "negative": 0, "verdict": "Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call.", "bestFor": "Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.", "strengths": [ "Muse Wonder and Muse Spark take text, image, video, audio and PDF input with a 1,048,576-token context window", "Per-token prices, per-model rate limits and rate-limit headers published without a login", "Content sent in Standard mode is not used to train Meta models, and training use is opt-in through `-contributor` model IDs", "Responses, Chat Completions and Messages endpoints, so the OpenAI and Anthropic SDKs work with a base-URL change", "llms.txt and a Markdown copy of every docs page, with an error guide that gives the Retry-After value for each retryable error" ], "weaknesses": [ "Muse Wonder is labelled a preview, and no deprecation policy or notice period was found", "No free API allowance found, and billing is set up with a card", "No status page with history. The docs' status link redirects to the home page, and `/v1/status` reports only the current state", "`tool_choice` accepts only `auto`, so a call can't be forced or ruled out", "No OpenAPI file, batch endpoint or Model API changelog found, and API keys have no scopes" ], "agentNotes": [ "Use `muse-wonder-1.0` for long multi-step work and `muse-spark-1.3` for cheaper, high-volume calls. Both take the same requests", "Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content", "Honour Retry-After on 429 and 503, which is 60 seconds for `service_overloaded`, and check `GET https://api.meta.ai/v1/status`, which needs no key", "Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY`", "Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.2 } ], "editorialScores": { "ergonomics": 75, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 63 }, "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 '{\"model\": \"muse-spark-1.3\", \"input\": \"What is the capital of France?\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/meta-model-api" }, "sameCompany": [ "muse-code" ], "area": "models", "unitPrices": [ { "item": "Muse Wonder input", "unit": "1m-tokens", "usd": 4, "note": "Standard mode, muse-wonder-1.0. No long-context premium." }, { "item": "Muse Wonder cached input", "unit": "1m-tokens", "usd": 0.2 }, { "item": "Muse Wonder output", "unit": "1m-tokens", "usd": 20 }, { "item": "Muse Spark input", "unit": "1m-tokens", "usd": 1.25, "note": "Standard mode, muse-spark-1.3, 1.2 and 1.1." }, { "item": "Muse Spark cached input", "unit": "1m-tokens", "usd": 0.15 }, { "item": "Muse Spark output", "unit": "1m-tokens", "usd": 4.25 }, { "item": "Muse Spark input, Contributor mode", "unit": "1m-tokens", "usd": 0.1, "note": "Meta may use the content to train its models." }, { "item": "Muse Spark output, Contributor mode", "unit": "1m-tokens", "usd": 0.2, "note": "Cached input is $0.002 per million tokens." }, { "item": "Web search grounding", "unit": "1k-requests", "usd": 2.5, "note": "Per 1,000 search queries, on top of token cost." }, { "item": "Muse Image generated image", "unit": "image", "usd": 0.01 } ], "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": "2026-10-12", "notes": [ "The terms (last updated 9 October 2026) are an agreement with Meta Platforms, Inc., or Meta Platforms Ireland Limited for users in the European Region. The model card names the same two.", "The privacy field is the Meta Privacy Policy, which the terms link. www.facebook.com's robots.txt asks readers like ours not to fetch it, as the 8 October policy reading records, so it wasn't read. The terms also link a subprocessor list at forwork.meta.com/legal/mfw-subprocessor-list/ and the Meta Global Processor Terms, neither of which was read.", "RDAP for meta.ai gives a registration date of 16 December 2017. The API answers at api.meta.ai.", "No status page. The error guide links dev.meta.ai/status, which redirects to the home page. `GET /v1/status` is an unauthenticated health endpoint with no history.", "No Model API changelog was found. The only changelog on dev.meta.ai is Muse Code's.", "dev.meta.ai/.well-known/security.txt returns 404. www.meta.ai's robots.txt disallows ClaudeBot, so its security.txt wasn't requested. www.facebook.com/.well-known/security.txt is valid until 11 November 2026 and points to bugbounty.meta.com.", "developer.meta.com/ai/ redirects to dev.meta.ai." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/meta-model-api.json", "live": { "slug": "meta-model-api", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-10-12T22:35:32.230718004Z", "lastOk": true, "lastStatus": 404, "lastMs": 115, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 166, "samples24h": 19, "samples30d": 19, "days": [ { "date": "2026-10-12", "probes": 19, "ok": 19 } ] }, "updatedAt": "2026-10-12T22:35:32.230718004Z" } }, "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": "HTTP", "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 service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party.", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-08", "b": "2026-10-12", "name": "Last release" }, { "a": "2026-04-28", "b": "", "name": "Terms last updated" }, { "a": "2026-10-01", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "", "name": "Customer content may train models" }, { "a": "yes", "b": "", "name": "Terms restrict automated access" }, { "a": "yes", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "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 65.5 (B) on agent readiness against Meta Model API's 52.2 (D), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, Gemini Developer API or Meta Model API?" }, { "answer": "Both need an API key.", "question": "Do Gemini Developer API and Meta Model API need an API key?" }, { "answer": "Yes. Gemini Developer API has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Meta Model API at https://api.meta.ai/v1.", "question": "Can an agent call Gemini Developer API and Meta Model API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 85 against 60", "Agent ergonomics, 90 against 75", "Payments \u0026 pricing, 40 against 20", "Maintenance \u0026 community, 83 against 46", "Transparency \u0026 trust, 72 against 52" ], "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.", "slug": "gemini-api", "watchFor": "Free-tier prompts and responses improve Google products and may be read by human reviewers" }, { "aheadOn": null, "also": null, "goodFor": "Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.", "slug": "meta-model-api", "watchFor": "Muse Wonder is labelled a preview, and no deprecation policy or notice period was found" } ], "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api.json", "title": "Claude API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.json", "title": "Claude API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-gemini-api.json", "title": "Antseed vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/antseed-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.json", "title": "Antseed vs Meta Model API", "url": "https://www.anchorterminal.com/compare/antseed-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.json", "title": "BlockRun.AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.json", "title": "BlockRun.AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api.json", "title": "Cloudflare AI Gateway vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.json", "title": "Cloudflare AI Gateway vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api.json", "title": "Cloudflare Workers AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.json", "title": "Cloudflare Workers AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api.json", "title": "Cohere Chat API (Command models) vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.json", "title": "Cohere Chat API (Command models) vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.json", "title": "DeepInfra vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.json", "title": "DeepInfra vs Meta Model API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api.json", "title": "DeepSeek API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.json", "title": "DeepSeek API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-groq.json", "title": "Gemini Developer API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api.json", "title": "Gemini Developer API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai.json", "title": "Gemini Developer API vs Novita AI", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api.json", "title": "Gemini Developer API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter.json", "title": "Gemini Developer API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.json", "title": "Gemini Developer API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.json", "title": "Gemini Developer API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.json", "title": "Gemini Developer API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow" }, { "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/groq-vs-meta-model-api.json", "title": "GroqCloud vs Meta Model API", "url": "https://www.anchorterminal.com/compare/groq-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api.json", "title": "Meta Model API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.json", "title": "Meta Model API vs Novita AI", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.json", "title": "Meta Model API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.json", "title": "Meta Model API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.json", "title": "Meta Model API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.json", "title": "Meta Model API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.json", "title": "Meta Model API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.json", "title": "Meta Model API vs StepFun API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api" } ], "scores": [ { "by": 0, "edge": "", "gemini-api": 40, "key": "reliability", "meta-model-api": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 25, "edge": "gemini-api", "gemini-api": 85, "key": "schema", "meta-model-api": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 15, "edge": "gemini-api", "gemini-api": 90, "key": "ergonomics", "meta-model-api": 75, "name": "Agent ergonomics", "weight": 13 }, { "by": 4, "edge": "meta-model-api", "gemini-api": 60, "key": "security", "meta-model-api": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 20, "edge": "gemini-api", "gemini-api": 40, "key": "payments", "meta-model-api": 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", "meta-model-api": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 20, "edge": "gemini-api", "gemini-api": 72, "key": "transparency", "meta-model-api": 52, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini Developer API scores 65.5 (B) on agent readiness against Meta Model API's 52.2 (D), and leads in 5 of 7 scored categories. Both do llm inference.", "verdicts": { "gemini-api": "Free tier on 3.8 Flash and Flash-Lite with no billing account. Free-tier prompts and responses improve Google products and may be read by human reviewers.", "meta-model-api": "Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api", "json": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.md", "slim": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.min.md" }, "markdown": "Gemini Developer API scores 65.5 (B) on agent readiness against Meta Model API's 52.2 (D), and leads in 5 of 7 scored categories. Both do llm inference.\n\n- Gemini Developer API: grade B, 65.5/100, rank #323 of 993. Markdown https://www.anchorterminal.com/tools/gemini-api.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-api.json\n- Meta Model API: grade D, 52.2/100, rank #757 of 993. Markdown https://www.anchorterminal.com/tools/meta-model-api.md · JSON https://www.anchorterminal.com/api/v1/tools/meta-model-api.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 171 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.\n\nAhead on:\n- Schema \u0026 documentation, 85 against 60\n- Agent ergonomics, 90 against 75\n- Payments \u0026 pricing, 40 against 20\n- Maintenance \u0026 community, 83 against 46\n- Transparency \u0026 trust, 72 against 52\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### Meta Model API (D)\n\nGood for: Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.\n\nWatch for: Muse Wonder is labelled a preview, and no deprecation policy or notice period was found\n\n\n## Score by category\n\n| Category | Weight | Gemini Developer API | Meta Model API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 40 | even |\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) | 90 | 75 | Gemini Developer API +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 60 | 64 | Meta Model API +4 |\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 | 52 | Gemini Developer API +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **65.5 · B** | **52.2 · D** | |\n\n## Facts side by side\n\n| Fact | Gemini Developer API | Meta Model API |\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 | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDKs) | Closed service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party. |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-08 | 2026-10-12 |\n| Terms last updated | 2026-04-28 | |\n| Privacy policy last updated | 2026-10-01 | couldn't be read |\n| Customer content may train models | yes | |\n| Terms restrict automated access | yes | |\n| Terms restrict benchmarking | yes | |\n| Terms or service can change without notice | not found in the text | |\n| Arbitration or class-action waiver | not found in the text | |\n| Popularity | 4k stars | none |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**Gemini Developer API.** Free tier on 3.8 Flash and Flash-Lite with no billing account. Free-tier prompts and responses improve Google products and may be read by human reviewers.\n\n**Meta Model API.** Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call.\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. Don't request Gemini 4 Argon here. It has no Developer API model ID as of 5 October 2026\n3. Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403\n4. Stop sending temperature, top_p and top_k. They've been deprecated since 2026-07-21\n5. Validate structured output yourself. The docs don't promise schema-constrained decoding\n\n### Meta Model API\n\n1. Use `muse-wonder-1.0` for long multi-step work and `muse-spark-1.3` for cheaper, high-volume calls. Both take the same requests\n2. Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content\n3. Honour Retry-After on 429 and 503, which is 60 seconds for `service_overloaded`, and check `GET https://api.meta.ai/v1/status`, which needs no key\n4. Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY`\n5. Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported\n\n## Questions\n\n### Which is better for AI agents, Gemini Developer API or Meta Model API?\n\nGemini Developer API scores 65.5 (B) on agent readiness against Meta Model API's 52.2 (D), and leads in 5 of 7 scored categories.\n\n### Do Gemini Developer API and Meta Model API need an API key?\n\nBoth need an API key.\n\n### Can an agent call Gemini Developer API and Meta Model API without installing anything?\n\nYes. Gemini Developer API has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Meta Model API 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-meta-model-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-api\", \"b\": \"meta-model-api\"}`. From a terminal: `anchor compare gemini-api meta-model-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-api.json and https://www.anchorterminal.com/api/v1/tools/meta-model-api.json\n\n## Other comparisons with Gemini Developer API or Meta Model API\n\n- [Claude API vs Gemini Developer API](https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api.md)\n- [Claude API vs Meta Model API](https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.md)\n- [Antseed vs Gemini Developer API](https://www.anchorterminal.com/compare/antseed-vs-gemini-api.md)\n- [Antseed vs Meta Model API](https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.md)\n- [BlockRun.AI vs Gemini Developer API](https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.md)\n- [BlockRun.AI vs Meta Model API](https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.md)\n- [Cloudflare AI Gateway vs Gemini Developer API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api.md)\n- [Cloudflare AI Gateway vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.md)\n- [Cloudflare Workers AI vs Gemini Developer API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api.md)\n- [Cloudflare Workers AI vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.md)\n- [Cohere Chat API (Command models) vs Gemini Developer API](https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api.md)\n- [Cohere Chat API (Command models) vs Meta Model API](https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.md)\n- [DeepInfra vs Gemini Developer API](https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.md)\n- [DeepInfra vs Meta Model API](https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.md)\n- [DeepSeek API vs Gemini Developer API](https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api.md)\n- [DeepSeek API vs Meta Model API](https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.md)\n- [Gemini Developer API vs GroqCloud](https://www.anchorterminal.com/compare/gemini-api-vs-groq.md)\n- [Gemini Developer API vs Mistral AI API](https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api.md)\n- [Gemini Developer API vs Novita AI](https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai.md)\n- [Gemini Developer API vs OpenAI API](https://www.anchorterminal.com/compare/gemini-api-vs-openai-api.md)\n- [Gemini Developer API vs OpenRouter](https://www.anchorterminal.com/compare/gemini-api-vs-openrouter.md)\n- [Gemini Developer API vs Prism Inference](https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.md)\n- [Gemini Developer API vs SambaCloud](https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.md)\n- [Gemini Developer API vs SiliconFlow](https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.md)\n- [Gemini Developer API vs StepFun API](https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.md)\n- [GroqCloud vs Meta Model API](https://www.anchorterminal.com/compare/groq-vs-meta-model-api.md)\n- [Meta Model API vs Mistral AI API](https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api.md)\n- [Meta Model API vs Novita AI](https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.md)\n- [Meta Model API vs OpenAI API](https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.md)\n- [Meta Model API vs OpenRouter](https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.md)\n- [Meta Model API vs Prism Inference](https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.md)\n- [Meta Model API vs SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md)\n- [Meta Model API vs SiliconFlow](https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.md)\n- [Meta Model API vs StepFun API](https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-12", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemini Developer API vs Meta Model API", "url": "" } ], "description": "Gemini Developer scores 65.5 (B) to Meta Model's 52.2 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemini Developer API B 65.5", "Meta Model API D 52.2", "scores" ], "h1": "Gemini Developer API vs Meta Model API", "image": "https://www.anchorterminal.com/assets/og/compare-gemini-api-vs-meta-model-api.png", "path": "/compare/gemini-api-vs-meta-model-api", "published": "2026-10-01", "section": "tools", "title": "Gemini Developer vs Meta Model for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-12", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api" }, "tokens": { "markdown": 2700, "slim": 680 }, "version": 1 }