{ "data": { "a": { "slug": "gemini-4-hydrogen", "name": "Gemini 4 Hydrogen", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "inference", "summary": "Gemini 4 Hydrogen is Google's fast, lower-cost Gemini 4 model for everyday coding and agent work, on every tier of the Gemini API with a 1,048,576-token limit shared between input and output.", "url": "https://www.anchorterminal.com/tools/gemini-4-hydrogen", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-4-hydrogen.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-4-hydrogen.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json", "dns": "gemini-4-hydrogen.grade.anchorterminal.com", "license": "Closed model, used through the Gemini API and Google's apps under Google's terms", "transports": [], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta", "packages": [], "auth": "api-key", "authNotes": "An `x-goog-api-key` from AI Studio. Google's announcement puts Hydrogen on all tiers of AI Studio and the Gemini API, so a free-tier key with no billing works, per that post and the Gemini API listing's reading of the free tier. Paid Google AI Pro and Ultra subscribers can use it in Antigravity. On Google Cloud it's `gemini-4-hydrogen` on the Agent Platform, with Cloud credentials.", "pricing": "usage", "pricingNotes": "Google's announcement gives $1.50 per million input tokens and $7.50 output, flat across the whole 1M-token window, and names no introductory period, cached-input or batch price. Every processed token is billed, thinking included. Hydrogen's free-tier limits are on the Gemini API pricing page, now at aistudio.google.com, not yet read (checked 2026-11-13).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. Paid use needs billing on a Cloud project, per the Gemini API listing (checked 2026-11-13).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-13" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/guides/gemini-4-hydrogen", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "reasoning", "long-context", "tool-calling", "usage-priced", "free-tier", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-12", "graded": true, "anchor": { "graded": true, "score": 64.9, "grade": "B", "agentReady": false, "rank": 546, "ranked": true, "rankOf": 1524, "categoryRank": 23, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 90, "maintenance": 85, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-13" }, "negative": 0, "verdict": "Google's fast Gemini 4 model, on every tier of the Gemini API including the free one, at $1.50 and $7.50 per million tokens with a 1M-token budget, four thinking levels, function calling and structured output. Sampling parameters are ignored, explicit caching isn't supported, it's a short-term release with 45 days' notice on Cloud, and the benchmarks are Google's.", "bestFor": "Agents doing everyday coding, tool calls and document work at volume on the Gemini API, including ones that need to start on a free key, or as the executor beside Carbon or Argon.", "strengths": [ "On the free tier of the Gemini API, unlike Carbon and Argon", "$1.50 input and $7.50 output per million tokens, flat across the 1M-token window", "1,048,576 tokens shared between input and output, with long decodes to the full budget", "Four thinking levels, function calling, structured output, code execution, grounding and computer use", "The same API, keys and SDKs as Argon and Carbon, plus an OpenAI-compatible surface, so switching is a model ID" ], "weaknesses": [ "temperature, top_p and top_k are ignored, and thinking_budget, MINIMAL, penalties and extra candidates return a 400", "No explicit context caching or prefilled model turns, and strict thought-signature rules on parallel calls", "A short-term release, with a minimum of 45 days' notice before retirement on Google Cloud", "Free-tier content is used to improve Google's products and may be read by people", "Benchmark figures are Google's own, and the model card says jailbreak resistance is still being improved" ], "agentNotes": [ "Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-hydrogen and an x-goog-api-key", "Set thinking_level instead of thinking_budget; the default is HIGH, so set MEDIUM to keep 3.8 Flash's latency", "Don't send temperature, top_p, top_k, penalties or candidate_count above 1", "Pass every thought_signature back in its original part, on the first parallel functionCall only, or the call returns a 400", "Put static content of 4,096 tokens or more first so implicit caching applies, and keep confidential prompts off the free tier" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.9 } ], "editorialScores": { "ergonomics": 90, "maintenance": 85, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 55 }, "provenanceScore": 89, "checked": { "categories": { "ergonomics": "2026-11-13", "maintenance": "2026-11-13", "payments": "2026-11-13", "reliability": "2026-11-13", "schema": "2026-11-13", "security": "2026-11-13", "transparency": "2026-11-13" }, "last": "2026-11-13", "oldest": "2026-11-13", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-4-hydrogen: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-4-hydrogen" }, "sameCompany": [ "gemini-4-carbon", "vertex-ai", "embeddinggemma-2", "embeddinggemma-300m", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "shieldgemma", "google-imagen", "gemini-omni-1-2-flash", "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", "puppeteer", "mcp-toolbox-databases", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-decision-1", "antigravity-cli", "gemini-cli", "harness2", "jules", "gemini-spark", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api", "diffusiongemma-26b-a4b-it", "gemma-4-12b-it", "gemma-4-26b-a4b-it", "gemma-4-31b-it", "gemma-4-e2b-it", "gemma-4-e4b-it", "virustotal" ], "sameModel": [ "gemini-api" ], "area": "models", "unitPrices": [ { "item": "Gemini 4 Hydrogen input", "unit": "1m-tokens", "usd": 1.5, "note": "Flat across the 1M window" }, { "item": "Gemini 4 Hydrogen output", "unit": "1m-tokens", "usd": 7.5, "note": "Thinking tokens included" } ], "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": "2026-11-13", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-4-hydrogen.json", "live": { "slug": "gemini-4-hydrogen", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta", "method": "get", "lastAt": "2026-11-13T23:58:42.468588204Z", "lastOk": true, "lastStatus": 404, "lastMs": 10, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 33, "p95ms24h": 85, "samples24h": 32, "samples30d": 32, "days": [ { "date": "2026-11-13", "probes": 32, "ok": 32 } ], "since": "2026-11-13T20:58:02.850712558Z" }, "vendorStatus": { "page": "https://aistudio.google.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-13T23:54:32.148379631Z" }, "updatedAt": "2026-11-13T23:58:42.468588204Z" }, "streak": { "days": 1, "from": "2026-11-13", "sinceWatching": true, "best": 1 } }, "answer": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security \u0026 auth.", "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", "dns": "meta-model-api.grade.anchorterminal.com", "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": "from $1.25 / 1M in", "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": 53.6, "grade": "D", "agentReady": false, "rank": 1017, "ranked": true, "rankOf": 1524, "categoryRank": 39, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 46, "payments": 30, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "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 readable status history. The dev.meta.ai/status page showed \"Status information is unavailable\", 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": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.6 } ], "editorialScores": { "ergonomics": 75, "maintenance": 46, "payments": 30, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-11-13", "oldest": "2026-11-04", "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 '{\"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-spark-open", "llama-guard", "llamafirewall", "whatsapp-cloud-api", "muse-code", "meta-marketing-api", "muse-glimmer-30b" ], "sameModel": [ "muse-ember" ], "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": "2026-11-04", "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, 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.", "The API answers at api.meta.ai, whose robots.txt disallows every path, so nothing there was requested.", "dev.meta.ai/status, linked from the site header and the error guide, opens a Model API Status page. On 4 November it showed \"Status information is unavailable\" to our reader, so no component or incident history was read. The documented `GET /v1/status` endpoint reports only the current state.", "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 and www.facebook.com's robots.txt disallow ClaudeBot, so their security.txt files weren't read.", "developer.meta.com/ai/ redirects to dev.meta.ai." ], "score": 64 }, "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": "2026-11-13T23:58:49.830932295Z", "lastOk": true, "lastStatus": 404, "lastMs": 68, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 74, "p95ms24h": 177, "samples24h": 239, "samples30d": 7892, "days": [ { "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": 243, "ok": 243 }, { "date": "2026-11-10", "probes": 244, "ok": 244 }, { "date": "2026-11-11", "probes": 244, "ok": 244 }, { "date": "2026-11-12", "probes": 245, "ok": 245 }, { "date": "2026-11-13", "probes": 238, "ok": 238 } ], "since": "2026-10-12T00:00:00Z" }, "securityTxt": { "url": "https://meta.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-13T16:16:58.652426779Z" }, "llmsTxt": { "url": "https://dev.meta.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:55:09.91827235Z" }, "domain": { "domain": "meta.ai", "registered": "2017-12-16", "source": "https://rdap.identitydigital.services/rdap/domain/meta.ai", "checkedAt": "2026-11-08T14:51:01.706547979Z" }, "pages": [ { "url": "https://dev.meta.ai/legal/terms-of-service", "kind": "terms", "status": 200, "checkedAt": "2026-11-13T18:12:50.808409519Z", "changedAt": "2026-11-12T17:32:43.087073902Z", "fingerprint": "ddb40033dd8b" } ], "updatedAt": "2026-11-13T23:58:49.830932295Z" }, "streak": { "days": 33, "from": "2026-10-12", "sinceWatching": true, "best": 33 } }, "edges": [ { "by": 39, "category": "maintenance", "leads": "gemini-4-hydrogen", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 12 November 2026, recorded in Google Cloud's release notes; the Gemini API changelog, last updated 30 October, has no entry for it yet (30). Google Cloud states a minimum of 45 days' notice before Hydrogen is retired, short and stated for Cloud only; the Gemini API's deprecations give earliest dates with no stated minimum (6 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelog (15). SDK replies not sampled (5 of 10). Current SDKs; the developer guide names the minimum google-genai, @google/genai, Go and Java versions (15). Runtimes stated (10). 30 + 6 + 4 + 15 + 5 + 15 + 10 = 85.", "b": "Model reading. Muse Wonder launched on 12 October 2026 (30). No deprecation policy or notice period found. All three Muse Spark versions are still served, so no retirement has tested one (0 of 12). Dated launches on the API since 6 August that we read, Muse Image on 25 August, Muse Voice Transcribe on 3 September and Muse Wonder on 12 October (8 of 8). Muse Code leaving beta on 31 August is a separate product and isn't counted. No Model API changelog. A help centre with support tickets, answered 'typically within two business days' by Meta's own account, which we didn't test (8 of 15). No SDK of Meta's own for the API (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46." }, "scores": { "a": 85, "b": 46 } }, { "by": 25, "category": "schema", "leads": "gemini-4-hydrogen", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Discovery document for GenerateContent (20 of 25). llms.txt with Markdown twins (10). The Cloud developer guide says what Hydrogen takes, every parameter that's ignored or returns 400, the thought-signature rules and how to migrate from Gemini 3.8 Flash, though it doesn't document forced calls (15 of 20). Structured output to a JSON Schema with some keywords not enforced (10 of 15). Examples and an errors page (15). Dated changelog and model versions (15). 20 + 10 + 15 + 10 + 15 + 15 = 85.", "b": "No OpenAPI or similar machine-readable contract found. The reference is Markdown pages with schema sections, and /openapi.json paths on dev.meta.ai return 404 (0). llms.txt at dev.meta.ai/llms.txt, and every docs page has a Markdown copy (10). The models page says when to choose Muse Wonder and when Muse Spark, and the tool-calling and structured-output guides state their limits, such as `tool_choice` only accepting `auto` and which schema keywords `strict` allows (16 of 20). JSON Schema tools and output, `reasoning_effort` an enum from minimal to max, a strict subset that returns 400 when violated, though `tool_choice` has one value (12 of 15). Curl, Python and TypeScript examples on each page and an error guide covering 400 to 504 with the error envelope, codes and a fix for each (15). Versioned model IDs (muse-spark-1.1 to 1.3, muse-wonder-1.0) and dated launch posts on the developer blog, but no API changelog (7 of 15). 0 + 10 + 16 + 12 + 15 + 7 = 60." }, "scores": { "a": 85, "b": 60 } }, { "by": 19, "category": "transparency", "leads": "gemini-4-hydrogen", "name": "Transparency \u0026 trust", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). Free-tier content is used to improve products, paid-tier content isn't, abuse retention has no number, and request storage is on by default for new paid projects; the statements agree with each other (20 of 30). Deprecations table, and a stated 45-day minimum on Cloud (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). A DeepMind model card gives a March 2026 knowledge cutoff, automated safety results and Frontier Safety Framework outcomes, with the full safety report marked coming soon. Every benchmark figure is Google's own. 15 + 20 + 15 + 5 = 55.", "b": "Closed service with clear terms, under California law with AAA arbitration for Meta Platforms, Inc. and Irish law and courts for Meta Platforms Ireland Limited, with liability capped at the greater of 12 months of fees or $1,000. Muse Glimmer's open weights don't change the hosted service (15). The terms, the models page, the pricing page and the zero-retention help page agree that Standard content isn't used for training and Contributor content may be, but retention periods are not stated outside zero retention, and the privacy policy is Meta's general one (18 of 30). No deprecation policy or dated notices found, and the terms let Meta remove functionality without notice (0). A subprocessor list is linked from the terms but wasn't read, and no data locations were found (8 of 20). 15 + 18 + 0 + 8 = 41." }, "scores": { "a": 72, "b": 53 } }, { "by": 15, "category": "ergonomics", "leads": "gemini-4-hydrogen", "name": "Agent ergonomics", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling, with forced calls by FunctionCallingConfig on the API (20). Structured output to a response_schema, listed as supported on the Cloud model page (13 of 15). Implicit caching on prefixes of 4,096 tokens or more with durable caching on by default and explicit caches not supported, per the Cloud model page; the announcement names no cached-input price (12 of 15). 1,048,576 tokens shared between input and output, with background long decode up to 1M (15). Batch at half price on the platform (10). Python and JavaScript SDKs, and an OpenAI-compatible Chat Completions surface (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 12 + 15 + 10 + 10 + 10 = 90.", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls on all three endpoints, parallel calls, tool search with deferred loading, built-in web search and a computer-use tool, but `tool_choice` accepts only `auto`, so a call can't be forced or forbidden (15 of 20). Output constrained to the JSON Schema on every surface (15). Automatic prompt caching with no setup, and cached input at 5 per cent of the input price on Muse Wonder (15). 1,048,576-token context on Muse Wonder and every Muse Spark version, with no long-context premium (15). No batch endpoint or discount found. Background responses run asynchronously at the normal price (0). No SDK of Meta's own. The docs use the OpenAI and Anthropic SDKs against compatible endpoints, the same reading as StepFun (0). OpenAI-style error envelope with `type` and `code`, per-status fixes and named retryable codes (15). 15 + 15 + 15 + 15 + 0 + 0 + 15 = 75." }, "scores": { "a": 90, "b": 75 } }, { "by": 14, "category": "security", "leads": "meta-model-api", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). Hydrogen is on the free tier, where Google's terms say content is used to improve products and may be read by humans; paid-tier content isn't (10 of 20, the platform's reading; Carbon, paid tier only, got 15). Abuse retention has no number and new paid projects store requests by default since 26 October; Google's prompt-injection figure (2.3 times more resilient on Gray Swan's benchmark) is its own, and the model card says jailbreak resistance is still being improved (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 15 + 5 = 50.", "b": "Model reading. Bearer keys per team, named, shown once and revoked by deletion, with rotation advice. No scopes, read-only keys or per-key limits found (20 of 30). The terms say Meta will not use Content submitted to the Standard Services to train Meta Models, and training applies only to the `-contributor` model IDs a caller picks. Content may still be used to develop and improve safety and security systems (17 of 20). Retention is 'as needed' for the service, the law, flagged violations and safety review, with no period stated. Zero data retention for qualified accounts through sales, with flagged content kept up to 2 years (10 of 15). The usage dashboard shows tokens and requests per day by model, with no per-key or per-call log found (5 of 15). No security.txt on dev.meta.ai. Meta's bug bounty at bugbounty.meta.com is named in the Muse Code SDK's security policy, and a security commitments page describes TLS 1.2 or later, PCI-DSS card handling and an ISMS, with no named certification for the API (12 of 20). 20 + 17 + 10 + 5 + 12 = 64." }, "scores": { "a": 50, "b": 64 } }, { "by": 10, "category": "payments", "leads": "gemini-4-hydrogen", "name": "Payments \u0026 pricing", "reasons": { "a": "Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). Prices published in Google's announcement without a login, $1.50 input and $7.50 output per million tokens, flat across the window; the Gemini API pricing page, now at aistudio.google.com, is still to be read for Hydrogen's row (20). A free tier with no card: Google's announcement puts Hydrogen on all tiers of the Gemini API, and the free tier needs no billing account per the gemini-api listing; its limits for Hydrogen are on that pricing page (20). A Google account and AI Studio for a key (0). 0 + 20 + 20 + 0 = 40.", "b": "No machine payment protocol (0). Per-token prices for Muse Wonder and Muse Spark, and per-image, per-hour and per-frame prices for the other models, published without a login (20). No free API allowance found. Billing needs a card, and Playground requests count towards usage (0). Browser sign-up with email, phone, Facebook, Google or GitHub (0). 0 + 20 + 0 + 0 = 20. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 20 is 30." }, "scores": { "a": 40, "b": 30 } } ], "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": "", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Closed model, used through the Gemini API and Google's apps under Google's terms", "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": "2026-11-12", "b": "2026-10-12", "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" } ], "faq": [ { "answer": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security \u0026 auth.", "question": "Which is better for AI agents, Gemini 4 Hydrogen or Meta Model API?" }, { "answer": "Both need an API key.", "question": "Do Gemini 4 Hydrogen and Meta Model API need an API key?" }, { "answer": "Yes. Gemini 4 Hydrogen 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 4 Hydrogen and Meta Model API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 45 against 40", "Schema \u0026 documentation, 85 against 60", "Agent ergonomics, 90 against 75", "Payments \u0026 pricing, 40 against 30", "Maintenance \u0026 community, 85 against 46", "Transparency \u0026 trust, 72 against 53" ], "also": null, "goodFor": "Agents doing everyday coding, tool calls and document work at volume on the Gemini API, including ones that need to start on a free key, or as the executor beside Carbon or Argon.", "slug": "gemini-4-hydrogen", "watchFor": "temperature, top_p and top_k are ignored, and thinking_budget, MINIMAL, penalties and extra candidates return a 400" }, { "aheadOn": [ "Security \u0026 auth, 64 against 50" ], "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" } ], "history": { "gemini-4-hydrogen": null, "meta-model-api": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 1.3, "from": 52.3, "fromGrade": "D", "to": 53.6, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-30", "delta": -0.2, "from": 52.5, "fromGrade": "D", "to": 52.3, "toGrade": "D" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.3, "from": 52.2, "fromGrade": "D", "to": 52.5, "toGrade": "D" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-4-hydrogen.json", "title": "Amazon Bedrock vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-4-hydrogen" }, { "json": 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4 Hydrogen vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vercel-ai-gateway.json", "title": "Gemini 4 Hydrogen vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vertex-ai.json", "title": "Gemini 4 Hydrogen vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-xai-api.json", "title": "Gemini 4 Hydrogen vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.json", "title": "Gemini Developer API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-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/hugging-face-inference-providers-vs-meta-model-api.json", "title": "Hugging Face Inference Providers vs Meta Model API", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-meta-model-api.json", "title": "Tencent Hy4 vs Meta Model API", "url": "https://www.anchorterminal.com/compare/hy4-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-meta-model-api.json", "title": "Jais 3 vs Meta Model API", "url": "https://www.anchorterminal.com/compare/jais-3-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-api.json", "title": "Meta Model API vs MiniMax API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-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-muse-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open.json", "title": "Meta Model API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-nemotron-3-5-super-vl.json", "title": "Meta Model API vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-nemotron-3-5-super-vl" }, { "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-ramp-router.json", "title": "Meta Model API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-reflection-beam.json", "title": "Meta Model API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-routerplus.json", "title": "Meta Model API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-routerplus" }, { "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-simplismart.json", "title": "Meta Model API vs Simplismart", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-simplismart" }, { "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" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-swe-2-flash.json", "title": "Meta Model API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-tencent-tokenhub.json", "title": "Meta Model API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-together-inference.json", "title": "Meta Model API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-vercel-ai-gateway.json", "title": "Meta Model API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai.json", "title": "Meta Model API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-xai-api.json", "title": "Meta Model API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-xai-api" } ], "panel": null, "scores": [ { "by": 5, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 45, "key": "reliability", "meta-model-api": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 25, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 85, "key": "schema", "meta-model-api": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 15, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 90, "key": "ergonomics", "meta-model-api": 75, "name": "Agent ergonomics", "weight": 13 }, { "by": 14, "edge": "meta-model-api", "gemini-4-hydrogen": 50, "key": "security", "meta-model-api": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 40, "key": "payments", "meta-model-api": 30, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 39, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 85, "key": "maintenance", "meta-model-api": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 19, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 72, "key": "transparency", "meta-model-api": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "gemini-4-hydrogen": "Google's fast Gemini 4 model, on every tier of the Gemini API including the free one, at $1.50 and $7.50 per million tokens with a 1M-token budget, four thinking levels, function calling and structured output. Sampling parameters are ignored, explicit caching isn't supported, it's a short-term release with 45 days' notice on Cloud, and the benchmarks are Google's.", "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-4-hydrogen-vs-meta-model-api", "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.min.md" }, "markdown": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security \u0026 auth. Both do llm inference.\n\n- Gemini 4 Hydrogen: grade B, 64.9/100, rank #546 of 1524. Markdown https://www.anchorterminal.com/tools/gemini-4-hydrogen.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json\n- Meta Model API: grade D, 53.6/100, rank #1017 of 1524. 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 1044 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Gemini 4 Hydrogen (B)\n\nGood for: Agents doing everyday coding, tool calls and document work at volume on the Gemini API, including ones that need to start on a free key, or as the executor beside Carbon or Argon.\n\nAhead on:\n- Reliability, 45 against 40\n- Schema \u0026 documentation, 85 against 60\n- Agent ergonomics, 90 against 75\n- Payments \u0026 pricing, 40 against 30\n- Maintenance \u0026 community, 85 against 46\n- Transparency \u0026 trust, 72 against 53\n\nWatch for: temperature, top_p and top_k are ignored, and thinking_budget, MINIMAL, penalties and extra candidates return a 400\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\nAhead on:\n- Security \u0026 auth, 64 against 50\n\nWatch for: Muse Wonder is labelled a preview, and no deprecation policy or notice period was found\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Gemini 4 Hydrogen +39\n\n- Gemini 4 Hydrogen (85): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released on 12 November 2026, recorded in Google Cloud's release notes; the Gemini API changelog, last updated 30 October, has no entry for it yet (30). Google Cloud states a minimum of 45 days' notice before Hydrogen is retired, short and stated for Cloud only; the Gemini API's deprecations give earliest dates with no stated minimum (6 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelog (15). SDK replies not sampled (5 of 10). Current SDKs; the developer guide names the minimum google-genai, @google/genai, Go and Java versions (15). Runtimes stated (10). 30 + 6 + 4 + 15 + 5 + 15 + 10 = 85.\n- Meta Model API (46): Model reading. Muse Wonder launched on 12 October 2026 (30). No deprecation policy or notice period found. All three Muse Spark versions are still served, so no retirement has tested one (0 of 12). Dated launches on the API since 6 August that we read, Muse Image on 25 August, Muse Voice Transcribe on 3 September and Muse Wonder on 12 October (8 of 8). Muse Code leaving beta on 31 August is a separate product and isn't counted. No Model API changelog. A help centre with support tickets, answered 'typically within two business days' by Meta's own account, which we didn't test (8 of 15). No SDK of Meta's own for the API (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.\n\n### Schema \u0026 documentation: Gemini 4 Hydrogen +25\n\n- Gemini 4 Hydrogen (85): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Discovery document for GenerateContent (20 of 25). llms.txt with Markdown twins (10). The Cloud developer guide says what Hydrogen takes, every parameter that's ignored or returns 400, the thought-signature rules and how to migrate from Gemini 3.8 Flash, though it doesn't document forced calls (15 of 20). Structured output to a JSON Schema with some keywords not enforced (10 of 15). Examples and an errors page (15). Dated changelog and model versions (15). 20 + 10 + 15 + 10 + 15 + 15 = 85.\n- Meta Model API (60): No OpenAPI or similar machine-readable contract found. The reference is Markdown pages with schema sections, and /openapi.json paths on dev.meta.ai return 404 (0). llms.txt at dev.meta.ai/llms.txt, and every docs page has a Markdown copy (10). The models page says when to choose Muse Wonder and when Muse Spark, and the tool-calling and structured-output guides state their limits, such as `tool_choice` only accepting `auto` and which schema keywords `strict` allows (16 of 20). JSON Schema tools and output, `reasoning_effort` an enum from minimal to max, a strict subset that returns 400 when violated, though `tool_choice` has one value (12 of 15). Curl, Python and TypeScript examples on each page and an error guide covering 400 to 504 with the error envelope, codes and a fix for each (15). Versioned model IDs (muse-spark-1.1 to 1.3, muse-wonder-1.0) and dated launch posts on the developer blog, but no API changelog (7 of 15). 0 + 10 + 16 + 12 + 15 + 7 = 60.\n\n### Transparency \u0026 trust: Gemini 4 Hydrogen +19\n\n- Gemini 4 Hydrogen (72): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Closed service with clear terms, SDKs Apache-2.0 (15). Free-tier content is used to improve products, paid-tier content isn't, abuse retention has no number, and request storage is on by default for new paid projects; the statements agree with each other (20 of 30). Deprecations table, and a stated 45-day minimum on Cloud (15 of 20). Data may be cached anywhere Google has facilities; no subprocessor list checked (5 of 20). A DeepMind model card gives a March 2026 knowledge cutoff, automated safety results and Frontier Safety Framework outcomes, with the full safety report marked coming soon. Every benchmark figure is Google's own. 15 + 20 + 15 + 5 = 55.\n- Meta Model API (53): Closed service with clear terms, under California law with AAA arbitration for Meta Platforms, Inc. and Irish law and courts for Meta Platforms Ireland Limited, with liability capped at the greater of 12 months of fees or $1,000. Muse Glimmer's open weights don't change the hosted service (15). The terms, the models page, the pricing page and the zero-retention help page agree that Standard content isn't used for training and Contributor content may be, but retention periods are not stated outside zero retention, and the privacy policy is Meta's general one (18 of 30). No deprecation policy or dated notices found, and the terms let Meta remove functionality without notice (0). A subprocessor list is linked from the terms but wasn't read, and no data locations were found (8 of 20). 15 + 18 + 0 + 8 = 41.\n\n### Agent ergonomics: Gemini 4 Hydrogen +15\n\n- Gemini 4 Hydrogen (90): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling, with forced calls by FunctionCallingConfig on the API (20). Structured output to a response_schema, listed as supported on the Cloud model page (13 of 15). Implicit caching on prefixes of 4,096 tokens or more with durable caching on by default and explicit caches not supported, per the Cloud model page; the announcement names no cached-input price (12 of 15). 1,048,576 tokens shared between input and output, with background long decode up to 1M (15). Batch at half price on the platform (10). Python and JavaScript SDKs, and an OpenAI-compatible Chat Completions surface (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 12 + 15 + 10 + 10 + 10 = 90.\n- Meta Model API (75): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls on all three endpoints, parallel calls, tool search with deferred loading, built-in web search and a computer-use tool, but `tool_choice` accepts only `auto`, so a call can't be forced or forbidden (15 of 20). Output constrained to the JSON Schema on every surface (15). Automatic prompt caching with no setup, and cached input at 5 per cent of the input price on Muse Wonder (15). 1,048,576-token context on Muse Wonder and every Muse Spark version, with no long-context premium (15). No batch endpoint or discount found. Background responses run asynchronously at the normal price (0). No SDK of Meta's own. The docs use the OpenAI and Anthropic SDKs against compatible endpoints, the same reading as StepFun (0). OpenAI-style error envelope with `type` and `code`, per-status fixes and named retryable codes (15). 15 + 15 + 15 + 15 + 0 + 0 + 15 = 75.\n\n### Security \u0026 auth: Meta Model API +14\n\n- Gemini 4 Hydrogen (50): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). Hydrogen is on the free tier, where Google's terms say content is used to improve products and may be read by humans; paid-tier content isn't (10 of 20, the platform's reading; Carbon, paid tier only, got 15). Abuse retention has no number and new paid projects store requests by default since 26 October; Google's prompt-injection figure (2.3 times more resilient on Gray Swan's benchmark) is its own, and the model card says jailbreak resistance is still being improved (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 10 + 5 + 15 + 5 = 50.\n- Meta Model API (64): Model reading. Bearer keys per team, named, shown once and revoked by deletion, with rotation advice. No scopes, read-only keys or per-key limits found (20 of 30). The terms say Meta will not use Content submitted to the Standard Services to train Meta Models, and training applies only to the `-contributor` model IDs a caller picks. Content may still be used to develop and improve safety and security systems (17 of 20). Retention is 'as needed' for the service, the law, flagged violations and safety review, with no period stated. Zero data retention for qualified accounts through sales, with flagged content kept up to 2 years (10 of 15). The usage dashboard shows tokens and requests per day by model, with no per-key or per-call log found (5 of 15). No security.txt on dev.meta.ai. Meta's bug bounty at bugbounty.meta.com is named in the Muse Code SDK's security policy, and a security commitments page describes TLS 1.2 or later, PCI-DSS card handling and an ISMS, with no named certification for the API (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.\n\n### Payments \u0026 pricing: Gemini 4 Hydrogen +10\n\n- Gemini 4 Hydrogen (40): Model reading for gemini-4-hydrogen on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). Prices published in Google's announcement without a login, $1.50 input and $7.50 output per million tokens, flat across the window; the Gemini API pricing page, now at aistudio.google.com, is still to be read for Hydrogen's row (20). A free tier with no card: Google's announcement puts Hydrogen on all tiers of the Gemini API, and the free tier needs no billing account per the gemini-api listing; its limits for Hydrogen are on that pricing page (20). A Google account and AI Studio for a key (0). 0 + 20 + 20 + 0 = 40.\n- Meta Model API (30): No machine payment protocol (0). Per-token prices for Muse Wonder and Muse Spark, and per-image, per-hour and per-frame prices for the other models, published without a login (20). No free API allowance found. Billing needs a card, and Playground requests count towards usage (0). Browser sign-up with email, phone, Facebook, Google or GitHub (0). 0 + 20 + 0 + 0 = 20. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 20 is 30.\n\n## Score by category\n\n| Category | Weight | Gemini 4 Hydrogen | Meta Model API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 45 | 40 | Gemini 4 Hydrogen +5 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 85 | 60 | Gemini 4 Hydrogen +25 |\n| Agent ergonomics | 13% (16.2 this run) | 90 | 75 | Gemini 4 Hydrogen +15 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 64 | Meta Model API +14 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 30 | Gemini 4 Hydrogen +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 85 | 46 | Gemini 4 Hydrogen +39 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 72 | 53 | Gemini 4 Hydrogen +19 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64.9 · B** | **53.6 · D** | |\n\n## Facts side by side\n\n| Fact | Gemini 4 Hydrogen | 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 |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Closed model, used through the Gemini API and Google's apps under Google's terms | 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 | 2026-11-12 | 2026-10-12 |\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\n## How the scores have moved\n\n### Gemini 4 Hydrogen\n\nNo score change since it was listed.\n\n### Meta Model API\n\n- +1.3 on 13 November 2026: checklist\n- -0.2 on 30 October 2026: checklist\n- +0.3 on 27 October 2026: checklist\n\n## Verdicts\n\n**Gemini 4 Hydrogen.** Google's fast Gemini 4 model, on every tier of the Gemini API including the free one, at $1.50 and $7.50 per million tokens with a 1M-token budget, four thinking levels, function calling and structured output. Sampling parameters are ignored, explicit caching isn't supported, it's a short-term release with 45 days' notice on Cloud, and the benchmarks are Google's.\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 4 Hydrogen\n\n1. Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-hydrogen and an x-goog-api-key\n2. Set thinking_level instead of thinking_budget; the default is HIGH, so set MEDIUM to keep 3.8 Flash's latency\n3. Don't send temperature, top_p, top_k, penalties or candidate_count above 1\n4. Pass every thought_signature back in its original part, on the first parallel functionCall only, or the call returns a 400\n5. Put static content of 4,096 tokens or more first so implicit caching applies, and keep confidential prompts off the free tier\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 4 Hydrogen or Meta Model API?\n\nGemini 4 Hydrogen scores 64.9 (B) on agent readiness against Meta Model API's 53.6 (D), and leads in 6 of 7 scored categories. Meta Model API leads on security \u0026 auth.\n\n### Do Gemini 4 Hydrogen and Meta Model API need an API key?\n\nBoth need an API key.\n\n### Can an agent call Gemini 4 Hydrogen and Meta Model API without installing anything?\n\nYes. Gemini 4 Hydrogen 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-4-hydrogen-vs-meta-model-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-4-hydrogen\", \"b\": \"meta-model-api\"}`. From a terminal: `anchor compare gemini-4-hydrogen meta-model-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json and https://www.anchorterminal.com/api/v1/tools/meta-model-api.json\n\n## Other comparisons with Gemini 4 Hydrogen or Meta Model API\n\n- [Amazon Bedrock vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-4-hydrogen.md)\n- [Amazon Bedrock vs Meta Model API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-meta-model-api.md)\n- [Claude API vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-4-hydrogen.md)\n- [Claude API vs Meta Model API](https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.md)\n- [Antseed vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/antseed-vs-gemini-4-hydrogen.md)\n- [Antseed vs Meta Model API](https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.md)\n- [Azure OpenAI in Foundry Models vs 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"attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-14", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.8", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Gemini 4 Hydrogen vs Meta Model API", "url": "" } ], "description": "Gemini 4 Hydrogen scores 64.9 (B) to Meta Model's 53.6 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemini 4 Hydrogen B 64.9", "Meta Model API D 53.6", "scores" ], "h1": "Gemini 4 Hydrogen vs Meta Model API", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gemini-4-hydrogen-vs-meta-model-api", "published": "2026-10-01", "section": "tools", "title": "Gemini 4 Hydrogen vs Meta Model for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api" }, "tokens": { "markdown": 7200, "slim": 980 }, "version": 1 }