{ "data": { "a": { "slug": "hy4", "name": "Tencent Hy4", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "model", "category": "inference", "summary": "Tencent Hy4 is Tencent's flagship language model, released on 4 November 2026 with image understanding and computer use, served on Tencent Cloud TokenHub as hy4 with a 1M-token context, at ¥6 input and ¥18 output per million tokens.", "url": "https://www.anchorterminal.com/tools/hy4", "markdownUrl": "https://www.anchorterminal.com/tools/hy4.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy4.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy4.json", "dns": "hy4.grade.anchorterminal.com", "license": "Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet", "transports": [ "http" ], "packages": [], "auth": "api-key", "authNotes": "A TokenHub bearer key (or `x-api-key` on the Anthropic-style route) from a Tencent Cloud account with real-name verification, per the tencent-tokenhub listing. Keys can be limited by model, service, IP range and token quota.", "pricing": "usage", "pricingNotes": "¥6 input, ¥0.3 cached input and ¥18 output per million tokens on TokenHub, billed at 50% until 31 December 2026 (Beijing time), per the price page updated 9 November 2026. Tencent's release gives about US$0.90, US$0.045 and US$2.70. No free allowance named for hy4 on the price page.", "priceSummary": "Pay per use", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in Tencent's release or TokenHub's price page, 11 November 2026.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-11" }, "docsUrl": "https://cloud.tencent.com/document/product/1823/130051", "llmsTxt": "https://cloud.tencent.com/document/product/1823/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "reasoning", "long-context", "tool-calling", "usage-priced", "openai-compatible", "anthropic-compatible", "chinese", "new" ], "lastRelease": "2026-11-04", "graded": true, "anchor": { "graded": true, "score": 64, "grade": "B", "agentReady": false, "rank": 595, "ranked": true, "rankOf": 1524, "categoryRank": 25, "methodology": "0.8", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 73, "payments": 45, "reliability": 46, "schema": 73, "security": 71, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-11" }, "negative": 0, "verdict": "Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.", "bestFor": "Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.", "strengths": [ "1M-token context with 960K input and 64K output, per TokenHub's model list", "Image understanding, function calling, structured output, thinking and caching are marked for hy4", "Prices published: ¥6 input, ¥0.3 cached, ¥18 output per million tokens, billed at 50% until 31 December 2026", "OpenAI- and Anthropic-compatible endpoints on TokenHub, so existing SDKs work with a changed base URL", "Tencent says it's the same price as Hy4 preview despite the upgrade" ], "weaknesses": [ "Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November", "Computer use, which Tencent's release names, isn't marked in TokenHub's model list", "TokenHub needs real-name verification, and the international site answers with a bot check", "No public status page for TokenHub", "Every benchmark in the release is Tencent's own or a partner's, several of them internal" ], "agentNotes": [ "Use model id hy4 on TokenHub's OpenAI- or Anthropic-compatible endpoints; hy4-preview is a separate, older model", "Keep input under 960K tokens and expect at most 64K output", "Reuse prompt prefixes: cached input is ¥0.3 against ¥6", "Check the price page for the end of the 50% discount on 31 December 2026 before budgeting", "Read rate limits from the console or DescribeModelQuota; the public docs don't give them for chat models" ], "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 } ], "editorialScores": { "ergonomics": 86, "maintenance": 73, "payments": 45, "reliability": 46, "schema": 73, "security": 71, "transparency": 55 }, "provenanceScore": 49, "checked": { "categories": { "ergonomics": "2026-11-11", "maintenance": "2026-11-11", "payments": "2026-11-11", "reliability": "2026-11-11", "schema": "2026-11-11", "security": "2026-11-11", "transparency": "2026-11-11" }, "last": "2026-11-11", "lastMoved": "2026-11-13", "oldest": "2026-11-11", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://tokenhub.tencentmaas.com/v1/chat/completions \\\n -H \"Authorization: Bearer $TOKENHUB_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"hy4\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/hy4" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hunyuanocr", "octop", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "sameModel": [ "tencent-tokenhub" ], "area": "models", "unitPrices": [ { "item": "Hy4 input", "unit": "1m-tokens", "usd": 0.9, "note": "¥6 list, about US$0.90 per Tencent; billed at 50% until 31 December 2026" }, { "item": "Hy4 cached input", "unit": "1m-tokens", "usd": 0.045, "note": "¥0.3 list" }, { "item": "Hy4 output", "unit": "1m-tokens", "usd": 2.7, "note": "¥18 list" } ], "provenance": { "legalEntity": "Tencent Cloud Computing (Beijing) Co., Ltd. (腾讯云计算（北京）有限责任公司)", "domain": "tencentmaas.com", "domainRegistered": "2026-03-13", "endpointOnVendorDomain": false, "terms": "https://cloud.tencent.com/document/product/301/129852", "privacy": "https://privacy.qq.com/document/preview/91d5fd3c98ce4468ae462ac59233bba2", "statusPage": "", "changelog": "https://cloud.tencent.com/document/product/1823/130675", "securityTxt": "none", "checked": "2026-11-11", "notes": [ "The TokenHub Terms of Service (effective 14 September 2026), the SLA (effective 23 April 2026) and the special note on third-party deployed models (effective 14 September 2026) name 腾讯云计算（北京）有限责任公司. The docs footer also names 深圳市腾讯计算机系统有限公司 as ICP licence holder.", "The TokenHub privacy policy is linked from the docs to privacy.qq.com, which renders only with JavaScript, so it was not read. privacy.qq.com has no robots.txt (404).", "No public status page was found. status.cloud.tencent.com did not answer, and the Tencent Cloud health dashboard documentation answered with a bot check.", "security.txt answered 404 on cloud.tencent.com, www.tencent.com and tokenhub.tencentmaas.com. Tencent's bug bounty policy at en.security.tencent.com lists Tencent Cloud in scope.", "RDAP at Verisign gives tencentmaas.com a registration date of 13 March 2026 with no registrant. The API answers at tokenhub.tencentmaas.com and tokenhub-intl.tencentmaas.com, named in Tencent Cloud's API usage page.", "cloud.tencent.com allows these docs in robots.txt. Its pages answered some requests with a JavaScript bot check, which was not passed. The pages were read when a later plain request returned them. www.tencentcloud.com answered robots.txt with a bot check, so the international site was not read." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy4.json", "live": { "slug": "hy4", "securityTxt": { "url": "https://tencentmaas.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-13T16:16:35.489580858Z" }, "llmsTxt": { "url": "https://cloud.tencent.com/document/product/1823/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:54:34.939672287Z" }, "pages": [ { "url": "https://cloud.tencent.com/document/product/1823/130675", "kind": "changelog", "status": 200, "checkedAt": "2026-11-13T18:11:34.856577424Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" }, { "url": "https://privacy.qq.com/document/preview/91d5fd3c98ce4468ae462ac59233bba2", "kind": "privacy", "status": 304, "checkedAt": "2026-11-13T18:28:00.062507799Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" }, { "url": "https://cloud.tencent.com/document/product/301/129852", "kind": "terms", "status": 200, "checkedAt": "2026-11-13T18:11:36.909670614Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e3b0c44298fc" } ], "updatedAt": "2026-11-13T18:28:00.062507799Z" } }, "answer": "Tencent Hy4 scores 64 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories.", "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": 27, "category": "maintenance", "leads": "hy4", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Added on 4 November 2026 per the release notes; the model list and price page were updated on 9 November (30). Retirement notices of 21 to 33 days, no minimum written (7 of 12). Heavy retirement churn on the platform (3 of 8). Dated release notes and announcements, round-the-clock support (13 of 15). CLI replies not sampled (3 of 10). Control-plane SDKs and CLI, no inference SDK of its own (12 of 15). CLI runtime stated, CI not checked (5 of 10). 30 + 7 + 3 + 13 + 3 + 12 + 5 = 73. The model is a week old, so these lines are TokenHub's habits more than Hy4's record.", "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": 73, "b": 46 } }, { "by": 15, "category": "payments", "leads": "hy4", "name": "Payments \u0026 pricing", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The price page, updated 9 November, lists ¥6 input, ¥0.3 cached and ¥18 output per million tokens, with Hy4 billed at 50% until 31 December 2026 (20). TokenHub's campaign of 1M free tokens per language model, read on 1 November, isn't confirmed for hy4 on the price page, so half (10 of 20, a judgement call). A person signs up with real-name verification (0). 0 + 20 + 10 + 0 = 30. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 30 is 45.", "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": 45, "b": 30 } }, { "by": 14, "category": "ergonomics", "leads": "hy4", "name": "Agent ergonomics", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling is marked for hy4 in the model list (18 of 20). Structured output marked (13 of 15). Cache-hit price published, ¥0.3 against ¥6 input (14 of 15). 1M-token context with 960K input and 64K output (15). Batch at half price covered Hy3 and Hy4 preview on 1 November; whether it covers hy4 wasn't re-read (3 of 10). OpenAI and Anthropic SDKs with a changed base URL, plus Tencent Cloud's SDK and CLI for the control plane (8 of 10). Errors with code, messages, upstream status and request ID (15 of 15). 18 + 13 + 14 + 15 + 3 + 8 + 15 = 86.", "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": 86, "b": 72 } }, { "by": 13, "category": "schema", "leads": "hy4", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No OpenAPI file for inference; field pages for the OpenAI and Anthropic formats as TokenHub implements them (12 of 25). llms.txt with Markdown copies (10). The model list, updated 9 November, gives hy4's context, input and output limits and marks thinking, structured output, function calling, caching and image understanding; computer use, which Tencent's release names, isn't marked (13 of 20). Typed field pages (10 of 15). Examples in five languages and an error page of 30 codes (14 of 15). Dated release notes, with hy4 added on 4 November (14 of 15). 12 + 10 + 13 + 10 + 14 + 14 = 73.", "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": 73, "b": 60 } }, { "by": 7, "category": "security", "leads": "hy4", "name": "Security \u0026 auth", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Bearer keys limited by model, service, IP range and token quota, long-lived with no rotation (26 of 30). The terms say inputs aren't used to train Tencent Cloud's own models, and Hy4 is first party, so no third party's terms apply (14 of 20). Retention only as long as the service needs, no periods (7 of 15). Per-call audit logs and optional full logs to the customer's CLS (14 of 15). No security.txt; Tencent's TSRC bounty covers Tencent Cloud; no certification checked (10 of 20). 26 + 14 + 7 + 14 + 10 = 71.", "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": 71, "b": 64 } }, { "by": 6, "category": "reliability", "leads": "hy4", "name": "Reliability", "reasons": { "a": "Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No public status page (0) and so no incident record to read (5 of 30). Per-model rate limits only in the console and DescribeModelQuota (8 of 15). Six 429 codes, Retry-After and backoff advice (13 of 15). The TokenHub SLA promises 99.5% a month, and Hy4 is Tencent's own model, so the third-party exclusion doesn't apply (10). Released as hy4 on 4 November, a separate id from hy4-preview, with no preview label (10). 0 + 5 + 8 + 13 + 10 + 10 = 46.", "b": "Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40." }, "scores": { "a": 46, "b": 40 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Tencent (Hy team)", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "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": "Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet", "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": "2026-11-04", "b": "2026-11-09", "name": "Last release" }, { "a": "couldn't be read", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "couldn't be read", "b": "yes", "name": "Customer content may train models" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "couldn't be read", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "couldn't be read", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "couldn't be read", "b": "yes", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Tencent Hy4 scores 64 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories.", "question": "Which is better for AI agents, Tencent Hy4 or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do Tencent Hy4 and Muse Ember need an API key?" }, { "answer": "No hosted endpoint is listed for Tencent Hy4. Muse Ember has a hosted endpoint at https://api.meta.ai/v1.", "question": "Can an agent call Tencent Hy4 and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 46 against 40", "Schema \u0026 documentation, 73 against 60", "Agent ergonomics, 86 against 72", "Security \u0026 auth, 71 against 64", "Payments \u0026 pricing, 45 against 30", "Maintenance \u0026 community, 73 against 46" ], "also": null, "goodFor": "Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.", "slug": "hy4", "watchFor": "Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November" }, { "aheadOn": null, "also": [ "A hosted endpoint, with nothing to install" ], "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": { "hy4": [ { "causes": [ "method" ], "date": "2026-11-13", "delta": 1.8, "from": 62.2, "fromGrade": "B", "to": 64, "toGrade": "B" } ], "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-hy4.json", "title": "Amazon Bedrock vs Tencent Hy4", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-hy4" }, { "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": 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"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": 6, "edge": "hy4", "hy4": 46, "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 13, "edge": "hy4", "hy4": 73, "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 14, "edge": "hy4", "hy4": 86, "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 7, "edge": "hy4", "hy4": 71, "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 15, "edge": "hy4", "hy4": 45, "key": "payments", "muse-ember": 30, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 27, "edge": "hy4", "hy4": 73, "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 1, "edge": "muse-ember", "hy4": 52, "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Tencent Hy4 scores 64 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "hy4": "Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.", "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/hy4-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy4-vs-muse-ember.min.md" }, "markdown": "Tencent Hy4 scores 64 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories. Both do llm inference.\n\n- Tencent Hy4: grade B, 64/100, rank #595 of 1524. Markdown https://www.anchorterminal.com/tools/hy4.md · JSON https://www.anchorterminal.com/api/v1/tools/hy4.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### Tencent Hy4 (B)\n\nGood for: Long-context agent work, coding and office or finance tasks on a 1M-token context with images, for teams already on Tencent Cloud or happy to verify an account there.\n\nAhead on:\n- Reliability, 46 against 40\n- Schema \u0026 documentation, 73 against 60\n- Agent ergonomics, 86 against 72\n- Security \u0026 auth, 71 against 64\n- Payments \u0026 pricing, 45 against 30\n- Maintenance \u0026 community, 73 against 46\n\nWatch for: Weights, licence and system card are promised but not published; tencent/Hy4 didn't answer on Hugging Face on 11 November\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\nAlso in its favour:\n- A hosted endpoint, with nothing to install\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: Tencent Hy4 +27\n\n- Tencent Hy4 (73): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Added on 4 November 2026 per the release notes; the model list and price page were updated on 9 November (30). Retirement notices of 21 to 33 days, no minimum written (7 of 12). Heavy retirement churn on the platform (3 of 8). Dated release notes and announcements, round-the-clock support (13 of 15). CLI replies not sampled (3 of 10). Control-plane SDKs and CLI, no inference SDK of its own (12 of 15). CLI runtime stated, CI not checked (5 of 10). 30 + 7 + 3 + 13 + 3 + 12 + 5 = 73. The model is a week old, so these lines are TokenHub's habits more than Hy4's record.\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### Payments \u0026 pricing: Tencent Hy4 +15\n\n- Tencent Hy4 (45): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No machine payment protocol (0). The price page, updated 9 November, lists ¥6 input, ¥0.3 cached and ¥18 output per million tokens, with Hy4 billed at 50% until 31 December 2026 (20). TokenHub's campaign of 1M free tokens per language model, read on 1 November, isn't confirmed for hy4 on the price page, so half (10 of 20, a judgement call). A person signs up with real-name verification (0). 0 + 20 + 10 + 0 = 30. Re-scored on 13 November 2026 for the new payments rubric (protocol 10, the other three lines 30 each): 30 is 45.\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### Agent ergonomics: Tencent Hy4 +14\n\n- Tencent Hy4 (86): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling is marked for hy4 in the model list (18 of 20). Structured output marked (13 of 15). Cache-hit price published, ¥0.3 against ¥6 input (14 of 15). 1M-token context with 960K input and 64K output (15). Batch at half price covered Hy3 and Hy4 preview on 1 November; whether it covers hy4 wasn't re-read (3 of 10). OpenAI and Anthropic SDKs with a changed base URL, plus Tencent Cloud's SDK and CLI for the control plane (8 of 10). Errors with code, messages, upstream status and request ID (15 of 15). 18 + 13 + 14 + 15 + 3 + 8 + 15 = 86.\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### Schema \u0026 documentation: Tencent Hy4 +13\n\n- Tencent Hy4 (73): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No OpenAPI file for inference; field pages for the OpenAI and Anthropic formats as TokenHub implements them (12 of 25). llms.txt with Markdown copies (10). The model list, updated 9 November, gives hy4's context, input and output limits and marks thinking, structured output, function calling, caching and image understanding; computer use, which Tencent's release names, isn't marked (13 of 20). Typed field pages (10 of 15). Examples in five languages and an error page of 30 codes (14 of 15). Dated release notes, with hy4 added on 4 November (14 of 15). 12 + 10 + 13 + 10 + 14 + 14 = 73.\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### Security \u0026 auth: Tencent Hy4 +7\n\n- Tencent Hy4 (71): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. Bearer keys limited by model, service, IP range and token quota, long-lived with no rotation (26 of 30). The terms say inputs aren't used to train Tencent Cloud's own models, and Hy4 is first party, so no third party's terms apply (14 of 20). Retention only as long as the service needs, no periods (7 of 15). Per-call audit logs and optional full logs to the customer's CLS (14 of 15). No security.txt; Tencent's TSRC bounty covers Tencent Cloud; no certification checked (10 of 20). 26 + 14 + 7 + 14 + 10 = 71.\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### Reliability: Tencent Hy4 +6\n\n- Tencent Hy4 (46): Model reading for hy4 on Tencent Cloud TokenHub. Platform facts are the tencent-tokenhub listing's (checked 1 November), carried over as RULES.md allows for a model on an already graded platform. No public status page (0) and so no incident record to read (5 of 30). Per-model rate limits only in the console and DescribeModelQuota (8 of 15). Six 429 codes, Retry-After and backoff advice (13 of 15). The TokenHub SLA promises 99.5% a month, and Hy4 is Tencent's own model, so the third-party exclusion doesn't apply (10). Released as hy4 on 4 November, a separate id from hy4-preview, with no preview label (10). 0 + 5 + 8 + 13 + 10 + 10 = 46.\n- Muse Ember (40): Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.\n\n## Score by category\n\n| Category | Weight | Tencent Hy4 | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 46 | 40 | Tencent Hy4 +6 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 73 | 60 | Tencent Hy4 +13 |\n| Agent ergonomics | 13% (16.2 this run) | 86 | 72 | Tencent Hy4 +14 |\n| Security \u0026 auth | 14% (17.5 this run) | 71 | 64 | Tencent Hy4 +7 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 45 | 30 | Tencent Hy4 +15 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 73 | 46 | Tencent Hy4 +27 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 52 | 53 | Muse Ember +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **64 · B** | **53.1 · D** | |\n\n## Facts side by side\n\n| Fact | Tencent Hy4 | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Tencent (Hy team) | Meta |\n| Hosted endpoint | no (local only) | `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 | Hosted model under the Tencent Cloud TokenHub terms. Tencent says weights and a system card will follow; no licence is stated yet | 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 | 2026-11-04 | 2026-11-09 |\n| Terms last updated | couldn't be read | 2026-10-02 |\n| Privacy policy last updated | couldn't be read | couldn't be read |\n| Customer content may train models | couldn't be read | yes |\n| Terms restrict automated access | couldn't be read | not found in the text |\n| Terms restrict benchmarking | couldn't be read | yes |\n| Terms or service can change without notice | couldn't be read | not found in the text |\n| Arbitration or class-action waiver | couldn't be read | yes |\n\n## How the scores have moved\n\n### Tencent Hy4\n\n- +1.8 on 13 November 2026: checklist\n\n### Muse Ember\n\n- +1.3 on 13 November 2026: checklist\n\n## Verdicts\n\n**Tencent Hy4.** Tencent's flagship model is on TokenHub as hy4, with a 1M-token context, image understanding, function calling and caching, at ¥6 input and ¥18 output per million tokens, half price until the end of 2026. The weights it is called open source for aren't published yet, and every benchmark is Tencent's own.\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### Tencent Hy4\n\n1. Use model id hy4 on TokenHub's OpenAI- or Anthropic-compatible endpoints; hy4-preview is a separate, older model\n2. Keep input under 960K tokens and expect at most 64K output\n3. Reuse prompt prefixes: cached input is ¥0.3 against ¥6\n4. Check the price page for the end of the 50% discount on 31 December 2026 before budgeting\n5. Read rate limits from the console or DescribeModelQuota; the public docs don't give them for chat models\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, Tencent Hy4 or Muse Ember?\n\nTencent Hy4 scores 64 (B) on agent readiness against Muse Ember's 53.1 (D), and leads in 6 of 7 scored categories.\n\n### Do Tencent Hy4 and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call Tencent Hy4 and Muse Ember without installing anything?\n\nNo hosted endpoint is listed for Tencent Hy4. Muse Ember has a hosted endpoint at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy4-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy4-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy4\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare hy4 muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy4.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with Tencent Hy4 or Muse Ember\n\n- [Amazon Bedrock vs Tencent Hy4](https://www.anchorterminal.com/compare/amazon-bedrock-vs-hy4.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs Tencent Hy4](https://www.anchorterminal.com/compare/anthropic-api-vs-hy4.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs Tencent Hy4](https://www.anchorterminal.com/compare/antseed-vs-hy4.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs Tencent Hy4](https://www.anchorterminal.com/compare/azure-openai-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/blockrun-ai-vs-hy4.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [Cerebras Inference vs Tencent Hy4](https://www.anchorterminal.com/compare/cerebras-inference-vs-hy4.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Claude Fable 5.2 vs Tencent Hy4](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/cohere-chat-vs-hy4.md)\n- [Cohere Chat API (Command models) vs Muse Ember](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.md)\n- [DeepInfra vs Tencent Hy4](https://www.anchorterminal.com/compare/deepinfra-vs-hy4.md)\n- [DeepInfra vs Muse Ember](https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md)\n- [DeepSeek API vs Tencent Hy4](https://www.anchorterminal.com/compare/deepseek-api-vs-hy4.md)\n- [DeepSeek API vs Muse Ember](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.md)\n- [Eden AI vs Tencent Hy4](https://www.anchorterminal.com/compare/eden-ai-vs-hy4.md)\n- [Eden AI vs Muse Ember](https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.md)\n- [Glimpse vs Tencent Hy4](https://www.anchorterminal.com/compare/elorian-glimpse-vs-hy4.md)\n- [Glimpse vs Muse Ember](https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.md)\n- [Fireworks AI Inference vs Tencent Hy4](https://www.anchorterminal.com/compare/fireworks-inference-vs-hy4.md)\n- [Fireworks AI Inference vs Muse Ember](https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.md)\n- [Gemini 4 Carbon vs Tencent Hy4](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-hy4.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 Tencent Hy4](https://www.anchorterminal.com/compare/gemini-4-hydrogen-vs-hy4.md)\n- 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API](https://www.anchorterminal.com/compare/hy4-vs-minimax-api.md)\n- [Tencent Hy4 vs Mistral AI API](https://www.anchorterminal.com/compare/hy4-vs-mistral-api.md)\n- [Tencent Hy4 vs Muse Spark Open](https://www.anchorterminal.com/compare/hy4-vs-muse-spark-open.md)\n- [Tencent Hy4 vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/hy4-vs-nemotron-3-5-super-vl.md)\n- [Tencent Hy4 vs Novita AI](https://www.anchorterminal.com/compare/hy4-vs-novita-ai.md)\n- [Tencent Hy4 vs OpenAI API](https://www.anchorterminal.com/compare/hy4-vs-openai-api.md)\n- [Tencent Hy4 vs OpenRouter](https://www.anchorterminal.com/compare/hy4-vs-openrouter.md)\n- [Tencent Hy4 vs Prism Inference](https://www.anchorterminal.com/compare/hy4-vs-prism-inference.md)\n- [Tencent Hy4 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/hy4-vs-ramp-router.md)\n- [Tencent Hy4 vs Beam 1.0](https://www.anchorterminal.com/compare/hy4-vs-reflection-beam.md)\n- [Tencent Hy4 vs 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[Tencent Hy4 vs xAI API (Grok)](https://www.anchorterminal.com/compare/hy4-vs-xai-api.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": 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