{ "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 Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "b": { "slug": "muse-ember", "name": "Muse Ember", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.", "url": "https://www.anchorterminal.com/tools/muse-ember", "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json", "dns": "muse-ember.grade.anchorterminal.com", "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "transports": [], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://dev.meta.ai/docs/muse-ember", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "openai-compatible", "anthropic-compatible", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 53.1, "grade": "D", "agentReady": false, "rank": 1034, "ranked": true, "rankOf": 1524, "categoryRank": 40, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 46, "payments": 30, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "strengths": [ "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login", "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens", "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints", "Tool calling, structured output, computer use and search grounding", "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to", "3,000 requests and 4,000,000 tokens a minute per team, published" ], "weaknesses": [ "No weights and no parameter count published, so it can't be run locally", "Requests over 200K input tokens are billed at three times the rate for the whole request", "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower", "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing", "Benchmark and cost figures are Meta's own, several on internal benchmarks" ], "agentNotes": [ "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY", "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing", "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate", "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises", "Use strict structured output for extraction and routing steps", "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.1 } ], "editorialScores": { "ergonomics": 72, "maintenance": 46, "payments": 30, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-09", "maintenance": "2026-11-09", "payments": "2026-11-09", "reliability": "2026-11-09", "schema": "2026-11-09", "security": "2026-11-09", "transparency": "2026-11-09" }, "last": "2026-11-09", "lastMoved": "2026-11-13", "oldest": "2026-11-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"muse-ember-1.0\",\n \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n \"reasoning\": {\"effort\": \"low\"}\n}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-ember" }, "sameCompany": [ "muse-spark-open", "llama-guard", "llamafirewall", "whatsapp-cloud-api", "muse-code", "meta-marketing-api", "muse-glimmer-30b" ], "sameModel": [ "meta-model-api" ], "area": "models", "unitPrices": [ { "item": "Muse Ember input", "unit": "1m-tokens", "usd": 0.1, "note": "Up to 200K input tokens a request; $0.30 above, for the whole request" }, { "item": "Muse Ember cached input", "unit": "1m-tokens", "usd": 0.004, "note": "$0.012 above 200K input tokens" }, { "item": "Muse Ember output", "unit": "1m-tokens", "usd": 0.5, "note": "$1.50 above 200K input tokens" } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-09", "notes": [ "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.", "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json", "live": { "slug": "muse-ember", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-11-13T23:58:50.873571021Z", "lastOk": true, "lastStatus": 404, "lastMs": 49, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 90, "samples24h": 239, "samples30d": 1012, "days": [ { "date": "2026-11-09", "probes": 41, "ok": 41 }, { "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": "2026-11-09T19:55:51.172718582Z" }, "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": "2026-11-13T15:55:15.55140255Z" }, "updatedAt": "2026-11-13T23:58:50.873571021Z" }, "streak": { "days": 5, "from": "2026-11-09", "sinceWatching": true, "best": 5 } }, "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. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46." }, "scores": { "a": 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": "Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60." }, "scores": { "a": 85, "b": 60 } }, { "by": 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": "Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41." }, "scores": { "a": 72, "b": 53 } }, { "by": 18, "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. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72." }, "scores": { "a": 90, "b": 72 } }, { "by": 14, "category": "security", "leads": "muse-ember", "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": "Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64." }, "scores": { "a": 50, "b": 64 } }, { "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": "Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20. 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": "", "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 model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-12", "b": "2026-11-09", "name": "Last release" }, { "a": "2026-04-28", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "2026-10-01", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "yes", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "yes", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.", "question": "Which is better for AI agents, Gemini 4 Hydrogen or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do Gemini 4 Hydrogen and Muse Ember need an API key?" }, { "answer": "Yes. Gemini 4 Hydrogen has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call Gemini 4 Hydrogen and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 45 against 40", "Schema \u0026 documentation, 85 against 60", "Agent ergonomics, 90 against 72", "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": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "gemini-4-hydrogen": null, "muse-ember": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 1.3, "from": 51.8, "fromGrade": "D", "to": 53.1, "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": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-4-hydrogen.json", "title": "Claude API vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.json", "title": "Claude API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-gemini-4-hydrogen.json", "title": "Antseed vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/antseed-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember.json", "title": "Antseed vs Muse Ember", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-4-hydrogen.json", "title": "Azure OpenAI in Foundry Models vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.json", "title": "Azure OpenAI in Foundry Models vs Muse Ember", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-4-hydrogen.json", "title": "BlockRun.AI vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.json", "title": "BlockRun.AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-4-hydrogen.json", "title": "Cerebras Inference vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.json", "title": "Cerebras Inference vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-4-hydrogen.json", "title": "Claude Fable 5.2 vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.json", "title": "Claude Fable 5.2 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-gemini-4-hydrogen.json", "title": "Claude Mythos 5.2 vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-ember.json", "title": "Claude Mythos 5.2 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-4-hydrogen.json", "title": "Cloudflare AI Gateway vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.json", "title": "Cloudflare AI Gateway vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-4-hydrogen.json", "title": "Cloudflare Workers AI vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.json", "title": "Cloudflare Workers AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-4-hydrogen.json", "title": "Cohere Chat API (Command models) vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.json", "title": "Cohere Chat API (Command models) vs Muse Ember", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-hydrogen.json", "title": "DeepInfra vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.json", "title": "DeepInfra vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-hydrogen.json", "title": "DeepSeek API vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.json", "title": "DeepSeek API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-hydrogen.json", "title": "Eden AI vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.json", "title": "Eden AI vs Muse Ember", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-hydrogen.json", "title": "Glimpse vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.json", "title": "Glimpse vs Muse Ember", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-hydrogen.json", "title": "Fireworks AI Inference vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.json", "title": "Fireworks AI Inference vs Muse Ember", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-4-hydrogen.json", "title": "Gemini 4 Carbon vs Gemini 4 Hydrogen", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-4-hydrogen" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember.json", "title": "Gemini 4 Carbon vs Muse Ember", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-gemini-api.json", "title": "Gemini 4 Hydrogen vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-groq.json", "title": "Gemini 4 Hydrogen vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hugging-face-inference-providers.json", "title": "Gemini 4 Hydrogen vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hy4.json", "title": "Gemini 4 Hydrogen vs Tencent Hy4", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hy4" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-jais-3.json", "title": "Gemini 4 Hydrogen vs Jais 3", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.json", "title": "Gemini 4 Hydrogen vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-minimax-api.json", "title": "Gemini 4 Hydrogen vs MiniMax API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-mistral-api.json", "title": "Gemini 4 Hydrogen vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-spark-open.json", "title": "Gemini 4 Hydrogen vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-nemotron-3-5-super-vl.json", "title": "Gemini 4 Hydrogen vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-novita-ai.json", "title": "Gemini 4 Hydrogen vs Novita AI", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openai-api.json", "title": "Gemini 4 Hydrogen vs OpenAI API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openrouter.json", "title": "Gemini 4 Hydrogen vs OpenRouter", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-prism-inference.json", "title": "Gemini 4 Hydrogen vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-ramp-router.json", "title": "Gemini 4 Hydrogen vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-reflection-beam.json", "title": "Gemini 4 Hydrogen vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-routerplus.json", "title": "Gemini 4 Hydrogen vs RouterPlus", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-sambanova.json", "title": "Gemini 4 Hydrogen vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-siliconflow.json", "title": "Gemini 4 Hydrogen vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-simplismart.json", "title": "Gemini 4 Hydrogen vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-stepfun-api.json", "title": "Gemini 4 Hydrogen vs StepFun API", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-swe-2-flash.json", "title": "Gemini 4 Hydrogen vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-tencent-tokenhub.json", "title": "Gemini 4 Hydrogen vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-together-inference.json", "title": "Gemini 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-muse-ember.json", "title": "Gemini Developer API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-muse-ember.json", "title": "GroqCloud vs Muse Ember", "url": "https://www.anchorterminal.com/compare/groq-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.json", "title": "Hugging Face Inference Providers vs Muse Ember", "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember.json", "title": "Tencent Hy4 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.json", "title": "Jais 3 vs Muse Ember", "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.json", "title": "MiniMax API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.json", "title": "Mistral AI API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.json", "title": "Muse Ember vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.json", "title": "Muse Ember vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "title": "Muse Ember vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json", "title": "Muse Ember vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" } ], "panel": null, "scores": [ { "by": 5, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 45, "key": "reliability", "muse-ember": 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", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 18, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 90, "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 14, "edge": "muse-ember", "gemini-4-hydrogen": 50, "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 40, "key": "payments", "muse-ember": 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", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 19, "edge": "gemini-4-hydrogen", "gemini-4-hydrogen": 72, "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "gemini-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.", "muse-ember": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember.min.md" }, "markdown": "Gemini 4 Hydrogen scores 64.9 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Muse Ember 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- Muse Ember: grade D, 53.1/100, rank #1034 of 1524. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 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 72\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### Muse Ember (D)\n\nGood for: Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.\n\nAhead on:\n- Security \u0026 auth, 64 against 50\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Maintenance \u0026 community: Gemini 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- Muse Ember (46): Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.\n\n### Schema \u0026 documentation: Gemini 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- Muse Ember (60): Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.\n\n### 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- Muse Ember (53): Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.\n\n### Agent ergonomics: Gemini 4 Hydrogen +18\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- Muse Ember (72): Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.\n\n### Security \u0026 auth: Muse Ember +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- Muse Ember (64): Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.\n\n### 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- Muse Ember (30): Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20. 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 | Muse Ember | 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 | 72 | Gemini 4 Hydrogen +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 64 | Muse Ember +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** | **53.1 · D** | |\n\n## Facts side by side\n\n| Fact | Gemini 4 Hydrogen | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Google | Meta |\n| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta` | `https://api.meta.ai/v1` |\n| Transports | | |\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 model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-12 | 2026-11-09 |\n| Terms last updated | 2026-04-28 | 2026-10-02 |\n| Privacy policy last updated | 2026-10-01 | couldn't be read |\n| Customer content may train models | yes | yes |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n\n## How the scores have moved\n\n### Gemini 4 Hydrogen\n\nNo score change since it was listed.\n\n### Muse Ember\n\n- +1.3 on 13 November 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**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.\n\n## Before you call either\n\n### Gemini 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### Muse Ember\n\n1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY\n2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing\n3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate\n4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises\n5. Use strict structured output for extraction and routing steps\n6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide\n\n## Questions\n\n### Which is better for AI agents, Gemini 4 Hydrogen or Muse Ember?\n\nGemini 4 Hydrogen scores 64.9 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Muse Ember leads on security \u0026 auth.\n\n### Do Gemini 4 Hydrogen and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call Gemini 4 Hydrogen and Muse Ember without installing anything?\n\nYes. Gemini 4 Hydrogen has a hosted endpoint at https://generativelanguage.googleapis.com/v1beta and Muse Ember at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"gemini-4-hydrogen\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare gemini-4-hydrogen muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemini-4-hydrogen.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with Gemini 4 Hydrogen or Muse Ember\n\n- [Amazon Bedrock vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-4-hydrogen.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-4-hydrogen.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/antseed-vs-gemini-4-hydrogen.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/azure-openai-vs-gemini-4-hydrogen.md)\n- [Azure OpenAI in Foundry Models vs Muse Ember](https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.md)\n- [BlockRun.AI vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-4-hydrogen.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [Cerebras Inference vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-4-hydrogen.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Claude Fable 5.2 vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-4-hydrogen.md)\n- [Claude Fable 5.2 vs Muse Ember](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-muse-ember.md)\n- [Claude Mythos 5.2 vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-gemini-4-hydrogen.md)\n- [Claude Mythos 5.2 vs Muse Ember](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-muse-ember.md)\n- [Cloudflare AI Gateway vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-4-hydrogen.md)\n- [Cloudflare AI Gateway vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.md)\n- [Cloudflare Workers AI vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-4-hydrogen.md)\n- [Cloudflare Workers AI vs Muse Ember](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.md)\n- [Cohere Chat API (Command models) vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-4-hydrogen.md)\n- [Cohere Chat API (Command models) vs Muse Ember](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.md)\n- [DeepInfra vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-hydrogen.md)\n- [DeepInfra vs Muse Ember](https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md)\n- [DeepSeek API vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-hydrogen.md)\n- [DeepSeek API vs Muse Ember](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.md)\n- [Eden AI vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-hydrogen.md)\n- [Eden AI vs Muse Ember](https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.md)\n- [Glimpse vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-hydrogen.md)\n- [Glimpse vs Muse Ember](https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.md)\n- [Fireworks AI Inference vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-hydrogen.md)\n- [Fireworks AI Inference vs Muse Ember](https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.md)\n- [Gemini 4 Carbon vs Gemini 4 Hydrogen](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-4-hydrogen.md)\n- [Gemini 4 Carbon vs Muse Ember](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember.md)\n- [Gemini 4 Hydrogen vs Gemini Developer API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-gemini-api.md)\n- [Gemini 4 Hydrogen vs GroqCloud](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-groq.md)\n- [Gemini 4 Hydrogen vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hugging-face-inference-providers.md)\n- [Gemini 4 Hydrogen vs Tencent Hy4](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hy4.md)\n- [Gemini 4 Hydrogen vs Jais 3](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-jais-3.md)\n- [Gemini 4 Hydrogen vs Meta Model API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-meta-model-api.md)\n- [Gemini 4 Hydrogen vs MiniMax API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-minimax-api.md)\n- [Gemini 4 Hydrogen vs Mistral AI API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-mistral-api.md)\n- [Gemini 4 Hydrogen vs Muse Spark Open](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-spark-open.md)\n- [Gemini 4 Hydrogen vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-nemotron-3-5-super-vl.md)\n- [Gemini 4 Hydrogen vs Novita AI](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-novita-ai.md)\n- [Gemini 4 Hydrogen vs OpenAI API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openai-api.md)\n- [Gemini 4 Hydrogen vs OpenRouter](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-openrouter.md)\n- [Gemini 4 Hydrogen vs Prism Inference](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-prism-inference.md)\n- [Gemini 4 Hydrogen vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-ramp-router.md)\n- [Gemini 4 Hydrogen vs Beam 1.0](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-reflection-beam.md)\n- [Gemini 4 Hydrogen vs RouterPlus](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-routerplus.md)\n- [Gemini 4 Hydrogen vs SambaCloud](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-sambanova.md)\n- [Gemini 4 Hydrogen vs SiliconFlow](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-siliconflow.md)\n- [Gemini 4 Hydrogen vs Simplismart](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-simplismart.md)\n- [Gemini 4 Hydrogen vs StepFun API](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-stepfun-api.md)\n- [Gemini 4 Hydrogen vs SWE-2 Flash](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-swe-2-flash.md)\n- [Gemini 4 Hydrogen vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-tencent-tokenhub.md)\n- [Gemini 4 Hydrogen vs Together AI Inference](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-together-inference.md)\n- [Gemini 4 Hydrogen vs Vercel AI Gateway](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vercel-ai-gateway.md)\n- [Gemini 4 Hydrogen vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-vertex-ai.md)\n- [Gemini 4 Hydrogen vs xAI API (Grok)](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-xai-api.md)\n- [Gemini Developer API vs Muse Ember](https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.md)\n- [GroqCloud vs Muse Ember](https://www.anchorterminal.com/compare/groq-vs-muse-ember.md)\n- [Hugging Face Inference Providers vs Muse Ember](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.md)\n- [Tencent Hy4 vs Muse Ember](https://www.anchorterminal.com/compare/hy4-vs-muse-ember.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Meta Model API vs Muse Ember](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Ember vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.md)\n- [Muse Ember vs Novita AI](https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.md)\n- [Muse Ember vs OpenAI API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism Inference](https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.md)\n- [Muse Ember vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.md)\n- [Muse Ember vs Beam 1.0](https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md)\n- [Muse Ember vs RouterPlus](https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.md)\n- [Muse Ember vs SambaCloud](https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md)\n- [Muse Ember vs SiliconFlow](https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.md)\n- [Muse Ember vs Simplismart](https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md)\n- [Muse Ember vs StepFun API](https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.md)\n- [Muse Ember vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.md)\n- [Muse Ember vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.md)\n- [Muse Ember vs Together AI Inference](https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.md)\n- [Muse Ember vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Ember vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-14", "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 Muse Ember", "url": "" } ], "description": "Gemini 4 Hydrogen scores 64.9 (B) to Muse Ember's 53.1 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Gemini 4 Hydrogen B 64.9", "Muse Ember D 53.1", "scores" ], "h1": "Gemini 4 Hydrogen vs Muse Ember", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/gemini-4-hydrogen-vs-muse-ember", "published": "2026-10-01", "section": "tools", "title": "Gemini 4 Hydrogen vs Muse Ember for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-11-13", "url": "https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-muse-ember" }, "tokens": { "markdown": 6600, "slim": 930 }, "version": 1 }