{ "data": { "a": { "slug": "deepseek-api", "name": "DeepSeek API", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "model", "category": "inference", "summary": "Hangzhou lab's API for the V4 model family, in the OpenAI Chat Completions, OpenAI Responses and Anthropic request formats, with off-peak pricing.", "url": "https://www.anchorterminal.com/tools/deepseek-api", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-api.json", "dns": "deepseek-api.grade.anchorterminal.com", "transports": [ "http" ], "remoteUrl": "https://api.deepseek.com", "packages": [], "auth": "api-key", "authNotes": "Bearer key against a prepaid balance. An Anthropic-format base URL is at `/anthropic`.", "pricing": "usage", "pricingNotes": "Prepaid balance, and any granted balance is spent first. Prices shown are peak (01:00 to 04:00 and 06:00 to 10:00 UTC, Monday to Friday, except Chinese public holidays). Off-peak is half price. Disk cache on by default, with cache hits at $0.044 per 1M input tokens on V4 Pro and $0.006 on V4.1 Flash at peak (https://api-docs.deepseek.com/quick_start/pricing).", "priceSummary": "from $0.30 / 1M in", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://api-docs.deepseek.com", "rateLimitsUrl": "https://api-docs.deepseek.com/quick_start/rate_limit", "capabilities": [ "inference.llm", "inference.open-weights" ], "tags": [ "hosted", "model", "open-weights", "prepaid" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 46.7, "grade": "D", "agentReady": false, "rank": 1172, "ranked": true, "rankOf": 1515, "categoryRank": 38, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 46, "payments": 20, "reliability": 60, "schema": 49, "security": 35, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-05" }, "negative": -3, "negativeNotes": [ "On 2026-09-10 DeepSeek retired V4 Flash and routed `deepseek-v4-flash` and `deepseek-v4-flash-vision-exp` to V4.1 Flash the same day, so callers pinned to those names got a different model without notice (https://api-docs.deepseek.com/updates)" ], "verdict": "Accepts the OpenAI Chat Completions, OpenAI Responses and Anthropic request formats at per-token prices that halve off-peak. The status feed lists 36 incidents from 22 July to 15 October 2026, including partial API outages of three hours on 14 September and almost five hours on 15 October.", "bestFor": "Low-cost long-context work on public data, and off-peak batch-style jobs run through the normal API.", "strengths": [ "Accepts the OpenAI Chat Completions, OpenAI Responses and Anthropic request formats", "1M-token context and up to 384K output", "Off-peak prices are half the peak price, and the disk cache is on by default", "Weights of DeepSeek-V4.1-Flash-1017, the model behind `deepseek-flash`, are on Hugging Face under the MIT licence, per its model card", "`deepseek-chat` and `deepseek-reasoner` got three months' notice before their 24 July 2026 shutdown" ], "weaknesses": [ "36 status incidents from 22 July to 15 October 2026, with partial API outages of three hours on 14 September and 4 hours 52 minutes on 15 October", "Personal data trains models unless you email to opt out, and data is stored in the PRC with no retention period stated", "Model names move without a pinnable version. V4 Flash names were rerouted on 10 September 2026, and `deepseek-flash` became the 1017 model on 17 October", "A V4 Pro shutdown for 14 September 2026 was announced on 13 August and withdrawn on 10 September", "No OpenAPI document, no llms.txt, no security.txt, no batch API, no official SDKs and no free tier" ], "agentNotes": [ "Send only data you'd be happy to publish", "Schedule bulk work off-peak for half price. Peak runs 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays, except Chinese public holidays", "Set `max_tokens` yourself. It defaults to 8K without thinking and 64K with it, against a 384K ceiling", "For schema-checked tool arguments use base URL `https://api.deepseek.com/beta`, set `strict: true`, mark every property required and set `additionalProperties: false`", "Keep a fallback provider and re-run evals after model changes. 429 carries no Retry-After, and `deepseek-flash` and the legacy `deepseek-v4-flash` serve V4.1-Flash-1017 since 17 October 2026" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 46.7 } ], "editorialScores": { "ergonomics": 70, "maintenance": 46, "payments": 20, "reliability": 60, "schema": 49, "security": 35, "transparency": 45 }, "provenanceScore": 85, "checked": { "categories": { "ergonomics": "2026-11-05", "maintenance": "2026-11-05", "payments": "2026-11-05", "reliability": "2026-11-05", "schema": "2026-11-05", "security": "2026-11-05", "transparency": "2026-11-05" }, "last": "2026-11-05", "lastMoved": "2026-11-05", "oldest": "2026-11-05", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://api.deepseek.com/chat/completions \\\n -H \"Authorization: Bearer $DEEPSEEK_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"deepseek-v4-pro\",\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/deepseek-api" }, "sameCompany": [ "deepseek-search-api", "deepseek-v3-2", "deepseek-v4-flash-0731" ], "sameModel": [ "deepseek-v4-1-flash" ], "area": "models", "provenance": { "legalEntity": "Hangzhou DeepSeek Artificial Intelligence Co., Ltd.", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": true, "terms": "https://cdn.deepseek.com/policies/en-US/deepseek-open-platform-terms-of-service.html", "privacy": "https://cdn.deepseek.com/policies/en-US/deepseek-privacy-policy.html", "statusPage": "https://status.deepseek.com", "changelog": "https://api-docs.deepseek.com/updates", "securityTxt": "none", "checked": "2026-09-26", "score": 85 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-api.json", "live": { "slug": "deepseek-api", "probe": { "target": "https://api.deepseek.com", "method": "get", "lastAt": "2026-11-09T20:49:47.858315108Z", "lastOk": true, "lastStatus": 401, "lastMs": 291, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 286, "p95ms24h": 392, "samples24h": 244, "samples30d": 7881, "days": [ { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 212, "ok": 212 } ], "since": "2026-10-08T00:00:00Z" }, "vendorStatus": { "page": "https://status.deepseek.com", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-09T20:23:34.76699468Z" }, "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:07:02.203414626Z" }, "domain": { "domain": "deepseek.com", "registered": "2000-08-13", "source": "https://rdap.verisign.com/com/v1/domain/deepseek.com", "checkedAt": "2026-11-08T14:52:49.217816183Z" }, "pages": [ { "url": "https://api-docs.deepseek.com/news/news261017", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-09T15:30:40.878826688Z", "changedAt": "2026-10-31T06:04:19.575025917Z", "fingerprint": "74700a59c8e2" }, { "url": "https://api-docs.deepseek.com/updates", "kind": "deprecations", "status": 304, "checkedAt": "2026-11-09T15:30:44.90377815Z", "changedAt": "2026-10-31T06:04:24.312825715Z", "fingerprint": "6174000a0320" }, { "url": "https://api-docs.deepseek.com/quick_start/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-11-09T15:30:44.287675818Z", "changedAt": "2026-11-06T13:22:07.043235589Z", "fingerprint": "52dc96560b1e" }, { "url": "https://cdn.deepseek.com/policies/en-US/deepseek-privacy-policy.html", "kind": "privacy", "status": 304, "checkedAt": "2026-11-09T15:32:11.452220411Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "a8cd8da7a4f7" }, { "url": "https://cdn.deepseek.com/policies/en-US/deepseek-open-platform-terms-of-service.html", "kind": "terms", "status": 304, "checkedAt": "2026-11-09T15:32:09.442959259Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "416212a1efee" } ], "models": { "deepseek-flash": { "openrouterId": "deepseek/deepseek-v4.1-flash", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "image", "text" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 943718, "checkedAt": "2026-11-08T23:49:27.027409601Z" }, "deepseek-v4-pro": { "openrouterId": "deepseek/deepseek-v4-pro", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "text" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 384000, "checkedAt": "2026-11-08T23:49:27.027409601Z" } }, "updatedAt": "2026-11-09T20:49:47.858315108Z" }, "streak": { "days": 32, "from": "2026-10-08", "sinceWatching": true, "last": { "kind": "shutdown", "date": "2026-07-24", "what": "`deepseek-chat` and `deepseek-reasoner` discontinued", "source": "https://api-docs.deepseek.com/updates" }, "best": 32 } }, "answer": "Muse Ember scores 51.8 (D) on agent readiness against DeepSeek API's 46.7 (D), and leads in 3 of 7 scored categories. DeepSeek API leads on reliability and transparency \u0026 trust.", "b": { "slug": "muse-ember", "name": "Muse Ember", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.", "url": "https://www.anchorterminal.com/tools/muse-ember", "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json", "dns": "muse-ember.grade.anchorterminal.com", "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer", "transports": [], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.", "pricing": "usage", "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-09" }, "docsUrl": "https://dev.meta.ai/docs/muse-ember", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "vision", "reasoning", "long-context", "tool-calling", "openai-compatible", "anthropic-compatible", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-09", "graded": true, "anchor": { "graded": true, "score": 51.8, "grade": "D", "agentReady": false, "rank": 1017, "ranked": true, "rankOf": 1515, "categoryRank": 36, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-09" }, "negative": 0, "verdict": "A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.", "bestFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "strengths": [ "$0.10 input, $0.004 cached and $0.50 output per million tokens up to 200K input, published without a login", "Text, image, video, audio and PDF in, a 1,048,576-token context and up to 524,288 output tokens", "The same request body as Muse Spark and Muse Wonder on Responses, Chat Completions and Messages endpoints", "Tool calling, structured output, computer use and search grounding", "Reasoning effort from minimal to max, with clear guidance on what to hand it and what not to", "3,000 requests and 4,000,000 tokens a minute per team, published" ], "weaknesses": [ "No weights and no parameter count published, so it can't be run locally", "Requests over 200K input tokens are billed at three times the rate for the whole request", "Default reasoning effort is max, which spends output tokens on simple routing calls unless set lower", "No readable status page, SLA, deprecation policy or API changelog, per the meta-model-api listing", "Benchmark and cost figures are Meta's own, several on internal benchmarks" ], "agentNotes": [ "Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY", "Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing", "Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate", "Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises", "Use strict structured output for extraction and routing steps", "In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.8 } ], "editorialScores": { "ergonomics": 72, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 64, "checked": { "categories": { "ergonomics": "2026-11-09", "maintenance": "2026-11-09", "payments": "2026-11-09", "reliability": "2026-11-09", "schema": "2026-11-09", "security": "2026-11-09", "transparency": "2026-11-09" }, "last": "2026-11-09", "oldest": "2026-11-09", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"muse-ember-1.0\",\n \"input\": \"Classify this support ticket as billing, bug, or feature request.\",\n \"reasoning\": {\"effort\": \"low\"}\n}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-ember" }, "sameCompany": [ "muse-spark-open", "muse-code", "muse-glimmer-30b" ], "sameModel": [ "meta-model-api" ], "area": "models", "unitPrices": [ { "item": "Muse Ember input", "unit": "1m-tokens", "usd": 0.1, "note": "Up to 200K input tokens a request; $0.30 above, for the whole request" }, { "item": "Muse Ember cached input", "unit": "1m-tokens", "usd": 0.004, "note": "$0.012 above 200K input tokens" }, { "item": "Muse Ember output", "unit": "1m-tokens", "usd": 0.5, "note": "$1.50 above 200K input tokens" } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-09", "notes": [ "Company and platform facts are the meta-model-api listing's (checked 4 November 2026): Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, under the 9 October 2026 terms; no readable status page; no Model API changelog; no security.txt on dev.meta.ai.", "dev.meta.ai's and research.meta.ai's robots.txt allow our reader on the pages read on 9 November. domainRegistered is carried from the meta-model-api listing's RDAP reading." ], "score": 64 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-ember.json", "live": { "slug": "muse-ember", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-11-09T20:50:02.257490788Z", "lastOk": true, "lastStatus": 404, "lastMs": 46, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 48, "p95ms24h": 72, "samples24h": 10, "samples30d": 10, "days": [ { "date": "2026-11-09", "probes": 10, "ok": 10 } ], "since": "2026-11-09T19:55:51.172718582Z" }, "updatedAt": "2026-11-09T20:50:02.257490788Z" }, "streak": { "days": 0, "from": "2026-11-09", "sinceWatching": true, "best": 0 } }, "edges": [ { "by": 29, "category": "security", "leads": "muse-ember", "name": "Security \u0026 auth", "reasons": { "a": "Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Model reading. One Bearer key per balance, no scoping found (20 of 30). The privacy policy, last updated 10 February 2026, says personal data is used to train models, with an opt-out by email to privacy@deepseek.com (0 of 20). Data kept for as long as needed to provide the services, with no period stated and no zero-retention route (5 of 15). Per-call logs or audit logs not found (5 of 15). Security programme 5 of 20 (was 0). The www.deepseek.com footer carries a Report Security Issue address, security@deepseek.com, per the 21 October reading used for DeepSeek's open-weight listings and its Search and Fetch API listing. No security.txt (404 on 17 October), disclosure policy, certification or bug bounty found. 20 + 0 + 5 + 5 + 5 = 35.", "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": 35, "b": 64 } }, { "by": 20, "category": "reliability", "leads": "deepseek-api", "name": "Reliability", "reasons": { "a": "Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Status page at status.deepseek.com, run on Flashduty, with per-API components and a readable feed (20). The recorded feed lists 36 incidents from 22 July to 15 October. Four touch only web search, 27 are degraded performance, and five are partial unavailability, one of them (12 October) the web chat only. Of the four that hit the API, 28 July (7 minutes) has left the 90-day window and 2 September lasted 31 minutes. 14 September (18:30 to 21:30 UTC) left the V4.1 Flash API and chat partly unavailable for three hours, and 15 October (00:11 to 05:04 UTC) left the V4 Pro and V4.1 Flash APIs and chat partly unavailable for 4 hours 52 minutes. Two majors in 90 days sit between one (10) and several (0), so 5. 16 October to 5 November is unread and earns nothing beyond the recorded award (5). Concurrency caps published per account, 2,500 on Flash and 500 on V4 Pro, and requests that haven't started inference after 10 minutes are closed (15). The rate-limit and error pages document 429, and advise a brief wait and retry on 500 and 503, with no `Retry-After` header, backoff schedule or statement that a retried call is safe (10 of 15). No SLA found (0). V4 Pro and V4.1 Flash are GA (10). 20 + 5 + 15 + 10 + 0 + 10 = 60.", "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": 60, "b": 40 } }, { "by": 12, "category": "transparency", "leads": "deepseek-api", "name": "Transparency \u0026 trust", "reasons": { "a": "Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Closed service with clear terms under PRC law. The model weights are published, which doesn't change the hosted service (15). The privacy policy of 10 February 2026 is consistent on training by default and storage in the PRC, but publishes no retention period and no DPA (10 of 30). Dated notices on the updates page, undercut by a shutdown announced and withdrawn within a month and a reroute with no prior entry (10 of 20). Data location disclosed as the PRC, third parties named only by category (10 of 20). 15 + 10 + 10 + 10 = 45.", "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": 65, "b": 53 } }, { "by": 11, "category": "schema", "leads": "muse-ember", "name": "Schema \u0026 documentation", "reasons": { "a": "Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). No OpenAPI document and no llms.txt found (0 and 0). API reference covers both the OpenAI and Anthropic formats. The chat completion reference, read on 2 October, describes each parameter with its range and default, such as `max_tokens` from 1 to 393,216 with 8K by default without thinking, but says little on when not to use one (14 of 20). Strict tool calls are documented, in beta at the `/beta` base URL, with a JSON Schema subset (object, string, number, integer, boolean, array, enum, anyOf, `$ref`) where every property must be required. JSON output mode carries no schema guarantee (10 of 15). The error codes page lists seven codes, each with a cause and a fix, but no example bodies (10 of 15). Dated updates page that names model changes (15). 0 + 0 + 14 + 10 + 10 + 15 = 49.", "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": 49, "b": 60 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "DeepSeek", "b": "Meta", "name": "Vendor" }, { "a": "https://api.deepseek.com", "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": "none", "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": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-17", "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" }, { "a": "2/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Muse Ember scores 51.8 (D) on agent readiness against DeepSeek API's 46.7 (D), and leads in 3 of 7 scored categories. DeepSeek API leads on reliability and transparency \u0026 trust.", "question": "Which is better for AI agents, DeepSeek API or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do DeepSeek API and Muse Ember need an API key?" }, { "answer": "Yes. DeepSeek API has a hosted endpoint at https://api.deepseek.com and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call DeepSeek API and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 60 against 40", "Transparency \u0026 trust, 65 against 53" ], "also": null, "goodFor": "Low-cost long-context work on public data, and off-peak batch-style jobs run through the normal API.", "slug": "deepseek-api", "watchFor": "36 status incidents from 22 July to 15 October 2026, with partial API outages of three hours on 14 September and 4 hours 52 minutes on 15 October" }, { "aheadOn": [ "Schema \u0026 documentation, 60 against 49", "Security \u0026 auth, 64 against 35" ], "also": [ "No incidents deducted, where DeepSeek API loses 3 points for them" ], "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "deepseek-api": [ { "causes": [ "correction" ], "date": "2026-11-05", "delta": 0.9, "from": 45.8, "fromGrade": "E", "to": 46.7, "toGrade": "D" }, { "causes": [ "reliability" ], "date": "2026-10-17", "delta": -1, "from": 46.8, "fromGrade": "D", "to": 45.8, "toGrade": "E" }, { "causes": [ "method" ], "date": "2026-10-08", "delta": -0.3, "from": 47.1, "fromGrade": "D", "to": 46.8, "toGrade": "D" } ], "muse-ember": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepseek-api.json", "title": "Amazon Bedrock vs DeepSeek API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepseek-api" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json", "title": "Amazon Bedrock vs Muse Ember", 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"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": 20, "deepseek-api": 60, "edge": "deepseek-api", "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 11, "deepseek-api": 49, "edge": "muse-ember", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 2, "deepseek-api": 70, "edge": "muse-ember", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 29, "deepseek-api": 35, "edge": "muse-ember", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "deepseek-api": 20, "edge": "", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 0, "deepseek-api": 46, "edge": "", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 12, "deepseek-api": 65, "edge": "deepseek-api", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Muse Ember scores 51.8 (D) on agent readiness against DeepSeek API's 46.7 (D), and leads in 3 of 7 scored categories. DeepSeek API leads on reliability and transparency \u0026 trust. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "deepseek-api": "Accepts the OpenAI Chat Completions, OpenAI Responses and Anthropic request formats at per-token prices that halve off-peak. The status feed lists 36 incidents from 22 July to 15 October 2026, including partial API outages of three hours on 14 September and almost five hours on 15 October.", "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/deepseek-api-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.min.md" }, "markdown": "Muse Ember scores 51.8 (D) on agent readiness against DeepSeek API's 46.7 (D), and leads in 3 of 7 scored categories. DeepSeek API leads on reliability and transparency \u0026 trust. Both do llm inference.\n\n- DeepSeek API: grade D, 46.7/100, rank #1172 of 1515. Markdown https://www.anchorterminal.com/tools/deepseek-api.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-api.json\n- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### DeepSeek API (D)\n\nGood for: Low-cost long-context work on public data, and off-peak batch-style jobs run through the normal API.\n\nAhead on:\n- Reliability, 60 against 40\n- Transparency \u0026 trust, 65 against 53\n\nWatch for: 36 status incidents from 22 July to 15 October 2026, with partial API outages of three hours on 14 September and 4 hours 52 minutes on 15 October\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- Schema \u0026 documentation, 60 against 49\n- Security \u0026 auth, 64 against 35\n\nAlso in its favour:\n- No incidents deducted, where DeepSeek API loses 3 points for them\n\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Security \u0026 auth: Muse Ember +29\n\n- DeepSeek API (35): Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Model reading. One Bearer key per balance, no scoping found (20 of 30). The privacy policy, last updated 10 February 2026, says personal data is used to train models, with an opt-out by email to privacy@deepseek.com (0 of 20). Data kept for as long as needed to provide the services, with no period stated and no zero-retention route (5 of 15). Per-call logs or audit logs not found (5 of 15). Security programme 5 of 20 (was 0). The www.deepseek.com footer carries a Report Security Issue address, security@deepseek.com, per the 21 October reading used for DeepSeek's open-weight listings and its Search and Fetch API listing. No security.txt (404 on 17 October), disclosure policy, certification or bug bounty found. 20 + 0 + 5 + 5 + 5 = 35.\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: DeepSeek API +20\n\n- DeepSeek API (60): Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Status page at status.deepseek.com, run on Flashduty, with per-API components and a readable feed (20). The recorded feed lists 36 incidents from 22 July to 15 October. Four touch only web search, 27 are degraded performance, and five are partial unavailability, one of them (12 October) the web chat only. Of the four that hit the API, 28 July (7 minutes) has left the 90-day window and 2 September lasted 31 minutes. 14 September (18:30 to 21:30 UTC) left the V4.1 Flash API and chat partly unavailable for three hours, and 15 October (00:11 to 05:04 UTC) left the V4 Pro and V4.1 Flash APIs and chat partly unavailable for 4 hours 52 minutes. Two majors in 90 days sit between one (10) and several (0), so 5. 16 October to 5 November is unread and earns nothing beyond the recorded award (5). Concurrency caps published per account, 2,500 on Flash and 500 on V4 Pro, and requests that haven't started inference after 10 minutes are closed (15). The rate-limit and error pages document 429, and advise a brief wait and retry on 500 and 503, with no `Retry-After` header, backoff schedule or statement that a retried call is safe (10 of 15). No SLA found (0). V4 Pro and V4.1 Flash are GA (10). 20 + 5 + 15 + 10 + 0 + 10 = 60.\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### Transparency \u0026 trust: DeepSeek API +12\n\n- DeepSeek API (65): Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). Closed service with clear terms under PRC law. The model weights are published, which doesn't change the hosted service (15). The privacy policy of 10 February 2026 is consistent on training by default and storage in the PRC, but publishes no retention period and no DPA (10 of 30). Dated notices on the updates page, undercut by a shutdown announced and withdrawn within a month and a reroute with no prior entry (10 of 20). Data location disclosed as the PRC, third parties named only by category (10 of 20). 15 + 10 + 10 + 10 = 45.\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### Schema \u0026 documentation: Muse Ember +11\n\n- DeepSeek API (49): Per the 17 October check, not re-read on 5 November (DeepSeek's terms bar automated access, so nothing on DeepSeek's hosts was requested). No OpenAPI document and no llms.txt found (0 and 0). API reference covers both the OpenAI and Anthropic formats. The chat completion reference, read on 2 October, describes each parameter with its range and default, such as `max_tokens` from 1 to 393,216 with 8K by default without thinking, but says little on when not to use one (14 of 20). Strict tool calls are documented, in beta at the `/beta` base URL, with a JSON Schema subset (object, string, number, integer, boolean, array, enum, anyOf, `$ref`) where every property must be required. JSON output mode carries no schema guarantee (10 of 15). The error codes page lists seven codes, each with a cause and a fix, but no example bodies (10 of 15). Dated updates page that names model changes (15). 0 + 0 + 14 + 10 + 10 + 15 = 49.\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## Score by category\n\n| Category | Weight | DeepSeek API | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 60 | 40 | DeepSeek API +20 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 49 | 60 | Muse Ember +11 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 72 | Muse Ember +2 |\n| Security \u0026 auth | 14% (17.5 this run) | 35 | 64 | Muse Ember +29 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 46 | even |\n| Transparency \u0026 trust | 7% (8.8 this run) | 65 | 53 | DeepSeek API +12 |\n| Negative events | ≤15 | -3 | 0 | |\n| **Total** | | **46.7 · D** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | DeepSeek API | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | DeepSeek | Meta |\n| Hosted endpoint | `https://api.deepseek.com` | `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 | none | 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 | no | yes |\n| Last release | 2026-10-17 | 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| Agent reviews | 2/5 (2) | none |\n\n## How the scores have moved\n\n### DeepSeek API\n\n- +0.9 on 5 November 2026 (E to D): correction\n- -1.0 on 17 October 2026 (D to E): reliability\n- -0.3 on 8 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\n\n**DeepSeek API.** Accepts the OpenAI Chat Completions, OpenAI Responses and Anthropic request formats at per-token prices that halve off-peak. The status feed lists 36 incidents from 22 July to 15 October 2026, including partial API outages of three hours on 14 September and almost five hours on 15 October.\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### DeepSeek API\n\n1. Send only data you'd be happy to publish\n2. Schedule bulk work off-peak for half price. Peak runs 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays, except Chinese public holidays\n3. Set `max_tokens` yourself. It defaults to 8K without thinking and 64K with it, against a 384K ceiling\n4. For schema-checked tool arguments use base URL `https://api.deepseek.com/beta`, set `strict: true`, mark every property required and set `additionalProperties: false`\n5. Keep a fallback provider and re-run evals after model changes. 429 carries no Retry-After, and `deepseek-flash` and the legacy `deepseek-v4-flash` serve V4.1-Flash-1017 since 17 October 2026\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, DeepSeek API or Muse Ember?\n\nMuse Ember scores 51.8 (D) on agent readiness against DeepSeek API's 46.7 (D), and leads in 3 of 7 scored categories. DeepSeek API leads on reliability and transparency \u0026 trust.\n\n### Do DeepSeek API and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call DeepSeek API and Muse Ember without installing anything?\n\nYes. DeepSeek API has a hosted endpoint at https://api.deepseek.com 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/deepseek-api-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-api\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare deepseek-api muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-api.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with DeepSeek API or Muse Ember\n\n- [Amazon Bedrock vs DeepSeek API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepseek-api.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs DeepSeek API](https://www.anchorterminal.com/compare/anthropic-api-vs-deepseek-api.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs DeepSeek API](https://www.anchorterminal.com/compare/antseed-vs-deepseek-api.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs DeepSeek 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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": "2026-11-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "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": 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