{ "data": { "a": { "slug": "deepinfra", "name": "DeepInfra", "vendor": "Deep Infra Inc.", "vendorUrl": "https://deepinfra.com", "kind": "model", "category": "inference", "summary": "DeepInfra is a hosted inference API for open-weight and some third-party models, covering chat, embeddings, reranking, image, video and speech. It answers OpenAI-style and Anthropic-style calls at api.deepinfra.com with a Bearer key.", "url": "https://www.anchorterminal.com/tools/deepinfra", "markdownUrl": "https://www.anchorterminal.com/tools/deepinfra.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepinfra.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepinfra.json", "dns": "deepinfra.grade.anchorterminal.com", "repo": "https://github.com/deepinfra/deepinfra-python", "license": "Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT", "transports": [ "http" ], "remoteUrl": "https://api.deepinfra.com/v1/openai", "packages": [ { "registry": "pypi", "name": "deepinfra" }, { "registry": "npm", "name": "deepinfra" } ], "auth": "api-key", "authNotes": "Self-serve Bearer key from the dashboard at https://deepinfra.com/dash/api_keys after a browser sign-up with Google, GitHub, email or Okta SSO. The Anthropic-style routes also take the key in `x-api-key`. Keys can carry an IP allowlist and a monthly spending limit, and a key can mint scoped JWTs limited by model, expiry and spend.", "pricing": "usage", "pricingNotes": "Pay per token, image, audio minute or GPU-hour, with no free tier. An account must add a card or prepay. DeepSeek-V4-Flash-0731 costs $0.06 in and $0.18 out per 1M tokens, the priority tier is 1.5x, flex 0.8x and batch 20 per cent off (https://deepinfra.com/pricing, checked 2026-10-08).", "priceSummary": "from $0.06 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the docs repository, the OpenAPI document or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 21, "npmWeekly": 1250, "pypiWeekly": 50, "asOf": "2026-10-08" }, "docsUrl": "https://docs.deepinfra.com/", "rateLimitsUrl": "https://docs.deepinfra.com/account/rate-limits", "llmsTxt": "https://docs.deepinfra.com/llms.txt", "openapi": "https://api.deepinfra.com/openapi.json", "capabilities": [ "inference.llm", "inference.open-weights", "embed.text", "rerank", "image.generate", "speech.stt", "speech.tts", "compute.batch" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "card-required", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "batch", "prompt-caching", "python", "typescript", "status-page", "soc2" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 63.7, "grade": "B", "agentReady": false, "rank": 501, "ranked": true, "rankOf": 1515, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 77, "maintenance": 61, "payments": 20, "reliability": 75, "schema": 69, "security": 64, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.", "bestFor": "Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.", "strengths": [ "`GET /v1/openai/models` answered without a key on 2 November 2026 with 173 models, each with context length, output cap and per-token prices", "Scoped JWTs limit a token to named models, an expiry and a USD spending limit, and each API key can carry an IP allowlist and a monthly cap", "The terms commit to zero data retention and no training on Customer Data, and say that clause controls over the privacy policy and docs", "Batch API at 20 per cent off, a flex tier at 0.8x, prompt caching with cached-input prices and server-side fallback across up to four models", "Status page with 90-day history for the API, the website and 168 models, plus a dated list of scheduled deprecations" ], "weaknesses": [ "A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026", "The status feed's 50 newest items are automated major-outage observations on single models, several longer than 24 hours, with no written incident notes", "No free tier. The pricing page says an account must add a card or prepay before using the service", "No changelog, SLA, error reference or `Retry-After` header was found in the reviewed documentation", "The sub-processor list names three companies, while the docs say Google and Anthropic receive data for their models" ], "agentNotes": [ "Call `GET https://api.deepinfra.com/v1/openai/models` at start-up for ids, context sizes and prices. No key is needed", "Check the `model` field of each response. After a deprecation date, requests to the old id are served by a replacement model", "Ask the account owner for a scoped JWT limited to the models and spend the task needs, not the full API key", "Stay under 200 concurrent requests per model. On 429 `engine_overloaded`, retry after a delay, or send `models` with up to four fallbacks", "Never inspect a JWT with `GET /v1/scoped-jwt?jwtoken=`, which puts the token in the URL. Keep credentials in the `Authorization` header" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.7 } ], "editorialScores": { "ergonomics": 77, "maintenance": 61, "payments": 20, "reliability": 75, "schema": 69, "security": 64, "transparency": 60 }, "provenanceScore": 75, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai # or: npm install openai", "http": "curl \"https://api.deepinfra.com/v1/openai/chat/completions\" \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $DEEPINFRA_API_KEY\" \\\n -d '{\"model\":\"deepseek-ai/DeepSeek-V4-Flash-0731\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/deepinfra" }, "area": "models", "provenance": { "legalEntity": "Deep Infra Inc.", "domain": "deepinfra.com", "domainRegistered": "2017-12-08", "endpointOnVendorDomain": true, "terms": "https://deepinfra.com/terms", "privacy": "https://deepinfra.com/privacy", "statusPage": "https://status.deepinfra.com", "changelog": "", "securityTxt": "none", "checked": "2026-11-02", "notes": [ "The Terms of Service, last modified 15 October 2026, name Deep Infra Inc., a Delaware corporation, with California law and JAMS arbitration in San Francisco. They are written around Service Orders and govern the services, the API included.", "The Privacy Policy, last modified 15 August 2026, names DeepInfra, Inc., a Delaware corporation, covers the website and the APIs, and has a section on data sent to and returned by the inference service.", "No changelog or release notes page was found in the docs index or the site map.", "https://deepinfra.com/.well-known/security.txt returns 404.", "RDAP for deepinfra.com gives a registration date of 2017-12-08.", "The API answers at api.deepinfra.com, the docs at docs.deepinfra.com and the status page at status.deepinfra.com.", "No SLA was found. The terms incorporate a data processing agreement made available at trust.deepinfra.com, where it is listed among documents available on request." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepinfra.json", "live": { "slug": "deepinfra", "probe": { "target": "https://api.deepinfra.com/v1/openai", "method": "get", "lastAt": "2026-11-09T20:49:47.696641439Z", "lastOk": true, "lastStatus": 404, "lastMs": 374, "authRequired": false, "uptime24h": 100, "uptime30d": 99.92, "p50ms24h": 388, "p95ms24h": 559, "samples24h": 244, "samples30d": 7800, "days": [ { "date": "2026-10-09", "probes": 169, "ok": 169 }, { "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": 243 }, { "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 } ], "outages": [ { "start": "2026-11-01T03:07:06.786817404Z", "end": "2026-11-01T03:30:15.593945224Z", "note": "dial tcp 38.101.151.21:443: i/o timeout" } ], "since": "2026-10-09T00:00:00Z" }, "vendorStatus": { "page": "https://status.deepinfra.com", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-09T20:23:33.440821752Z" }, "versions": [ { "registry": "github", "name": "deepinfra/deepinfra-python", "version": "v0.2.1", "released": "2026-08-05", "seenAt": "2026-11-09T02:12:43.979025789Z" }, { "registry": "npm", "name": "deepinfra", "version": "2.0.2", "seenAt": "2026-11-09T02:12:42.979192463Z" }, { "registry": "pypi", "name": "deepinfra", "version": "0.3.0", "released": "2026-08-12", "seenAt": "2026-11-09T02:12:26.597424561Z" } ], "githubStars": 1, "npmWeekly": 449, "pypiWeekly": 125, "securityTxt": { "url": "https://deepinfra.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-09T16:04:37.11672261Z" }, "llmsTxt": { "url": "https://docs.deepinfra.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-09T15:39:18.714661761Z" }, "domain": { "domain": "deepinfra.com", "registered": "2017-12-08", "source": "https://rdap.verisign.com/com/v1/domain/deepinfra.com", "checkedAt": "2026-11-08T14:50:31.504723812Z" }, "pages": [ { "url": "https://status.deepinfra.com/", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-09T15:51:47.740465878Z", "changedAt": "2026-11-09T15:51:47.740465878Z", "fingerprint": "8fdbe7fe4676" }, { "url": "https://deepinfra.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-09T15:33:54.233553129Z", "changedAt": "2026-11-09T15:33:54.233553129Z", "fingerprint": "cdc053a494df" }, { "url": "https://deepinfra.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-09T15:33:56.30551711Z", "changedAt": "2026-11-09T15:33:56.30551711Z", "fingerprint": "7981f01e58eb" }, { "url": "https://deepinfra.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-09T15:33:58.480242907Z", "changedAt": "2026-11-09T15:33:58.480242907Z", "fingerprint": "e841a8ee949f" } ], "github": { "repo": "deepinfra/deepinfra-python", "checkedAt": "2026-11-09T10:19:11.303046248Z", "stars": 1, "archived": false, "issuesEnabled": true, "pushedAt": "2026-08-12T08:03:27Z", "openIssues": 7, "openPRs": 2, "issuesOpened90d": 2, "issuesClosed90d": 0, "prsMerged90d": 1, "ci": { "branch": "main", "runs": 7, "success": 6, "failure": 1, "other": 0, "latest": "success", "latestAt": "2026-08-12T06:36:17Z" }, "replies": { "sampled": 13, "unanswered": 7, "medianDays": 3 } }, "updatedAt": "2026-11-09T20:49:47.696641439Z" }, "streak": { "days": 6, "from": "2026-11-03", "last": { "kind": "shutdown", "date": "2026-11-03", "what": "`Meta-Llama-3.1-70B-Instruct-Turbo` scheduled to redirect to `meta-llama/Llama-3.3-70B-Instruct-Turbo`", "source": "https://status.deepinfra.com/" }, "best": 13 } }, "answer": "DeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 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": 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": 35, "category": "reliability", "leads": "deepinfra", "name": "Reliability", "reasons": { "a": "Hosted reading. Own status page at status.deepinfra.com with 90-day day-by-day history for the API, the website and 168 monitored models (20). On 2 November 2026 the API component shows 100 per cent over 86.7 measured days and the website 99.99 per cent, with one 8-minute degradation on 19 August 2026. The Atom feed's 50 newest items, 20 August to 30 October 2026, are all automated major-outage observations on single models, \"not an announced incident\", among them MiMo-V2.5 from 27 August to 4 September, Qwen3-TTS for 37 and 32 hours, Nemotron-3-Ultra for 25 hours and a run of short Nemotron-3.5-Lightning outages in early September. No written incident touched the API. Each observation is one model, partial under this run's rule, as together-inference's single-model monitor downtime was read, so minor only (20). The 8 October reading of 15 sat between checklist values. 200 concurrent requests per model, published (15). 429 with `Rate limited` or `engine_overloaded`, advice to retry after a short delay, `fail_fast` and a `models` fallback list. No `Retry-After` header or backoff figures were found (10 of 15). No SLA found. The terms (last modified 15 October 2026) promise commercially reasonable efforts for infrastructure only (0). Generally available (10). 20 + 20 + 15 + 10 + 0 + 10 = 75.", "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": 75, "b": 40 } }, { "by": 15, "category": "maintenance", "leads": "deepinfra", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. The docs repository changed on 26 October 2026, and on 22 October documented Nemotron 3.5 image embeddings and reranker scores (30). Deprecated models get at least one week's notice by email, then requests are forwarded to a replacement (5 of 12). Churn. In the 90 days to 2 November four redirect dates passed (8, 9, 12 and 25 October, five models), Kimi-K2.7-Code was deprecated before 28 September, and three more are scheduled for 2 and 3 November. Three or more removal dates in 90 days give 0, as groq and fireworks-inference were read (0 of 8, was 4 when the five were only scheduled). No changelog. Support is by Discord and a feedback address, and the docs repository is public (7 of 15). Replies on the SDK repositories were not examined (3 of 10). Python `deepinfra` 0.3.0 from 12 August 2026 with a changelog. The Node repository tagged 2.1.0 on 1 September 2026, and npm couldn't be read on 2 November, so its 8 October reading of 2.0.2 from 8 May 2024 stands (9 of 15). CI workflows in both SDK repositories (7 of 10). 30 + 5 + 0 + 7 + 3 + 9 + 7 = 61.", "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": 61, "b": 46 } }, { "by": 15, "category": "transparency", "leads": "deepinfra", "name": "Transparency \u0026 trust", "reasons": { "a": "Closed service with terms that name the entity and California law, and an MIT licence on the SDKs and docs (15). The terms (last modified 15 October 2026), the privacy policy and the data privacy page agree on no storage and no training, the terms say their retention clause controls, and personal information is removed 30 days after account deletion. The terms now point to a DPA on the trust centre, which lists it among documents available on request, and the entity is written Deep Infra Inc. in the terms and DeepInfra, Inc. in the privacy policy (21 of 30). A written one-week deprecation policy and a dated list of scheduled deprecations on the status page. No record of past removals was found (14 of 20). The sub-processor list names Stripe, Amazon Web Services and Google Cloud Platform, as does the trust centre. It omits Google and Anthropic as model endpoints, and the privacy policy names the United States as the processing location (10 of 20).", "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": 68, "b": 53 } }, { "by": 9, "category": "schema", "leads": "deepinfra", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading. Public OpenAPI 3.1.0 document at api.deepinfra.com/openapi.json with 131 paths, 157 operations and 257 schemas on 2 November 2026. `llms.txt` now links that document, though the docs host's own copy still returns Mintlify's sample plant store document, and the spec lists chat at `/v1/chat/completions` while every docs example uses `/v1/openai/chat/completions` (22 of 25). `llms.txt` with 224 links and a Markdown copy of every docs page (10). Guide pages for tool calling, structured output, caching, batch and service tiers say when to use each. Many reference operations have a name and no description (14 of 20). Typed parameters with 49 enums, 31 chat properties and two required (11 of 15). Curl, Python and JavaScript examples on each page. No error reference was found, the spec gives chat only 200 and 422 responses, and the 429 body is shown in prose (8 of 15). No changelog was found. The spec version reads 1.0.0, and the docs repository on GitHub has a public commit history (4 of 15).", "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": 69, "b": 60 } }, { "by": 5, "category": "ergonomics", "leads": "deepinfra", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` of none, auto, required or a named function, and streaming. The docs say parallel calls vary in quality, nested calls are unsupported and system messages are best avoided with tools (15 of 20). `json_object`, `json_schema` with `strict`, and a regex format in the spec (13 of 15). Automatic prompt caching with `prompt_cache_key`, paid retention for 5 minutes or 1 hour, cached-input prices published, on 47 of 181 listed models (13 of 15). Context of 1,048,576 tokens on DeepSeek V4, Kimi K3 and GLM 5.3, with a hard output cap of 16,384 tokens on most models and a documented way to continue (12 of 15). Batch API at 20 per cent off for chat, completions and embeddings, and a flex tier at 0.8x (10). The OpenAI and Anthropic clients work with a changed base URL. The Python package `deepinfra` 0.3.0 dates from 12 August 2026, and npm listed 2.0.2 from May 2024 on 8 October (7 of 10). 429 carries an `engine_overloaded` code. Native routes return a bare `error` string, and no list of codes was found (7 of 15).", "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": 77, "b": 72 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Deep Infra Inc.", "b": "Meta", "name": "Vendor" }, { "a": "https://api.deepinfra.com/v1/openai", "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": "Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT", "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": "2026-10-26", "b": "2026-11-09", "name": "Last release" }, { "a": "2026-08-17", "b": "2026-10-02", "name": "Terms last updated" }, { "a": "2026-08-15", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "21 stars, 1.2k npm/wk, 50 PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories.", "question": "Which is better for AI agents, DeepInfra or Muse Ember?" }, { "answer": "Both need an API key.", "question": "Do DeepInfra and Muse Ember need an API key?" }, { "answer": "Yes. DeepInfra has a hosted endpoint at https://api.deepinfra.com/v1/openai and Muse Ember at https://api.meta.ai/v1.", "question": "Can an agent call DeepInfra and Muse Ember without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 75 against 40", "Schema \u0026 documentation, 69 against 60", "Agent ergonomics, 77 against 72", "Maintenance \u0026 community, 61 against 46", "Transparency \u0026 trust, 68 against 53" ], "also": null, "goodFor": "Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.", "slug": "deepinfra", "watchFor": "A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026" }, { "aheadOn": null, "also": null, "goodFor": "Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model.", "slug": "muse-ember", "watchFor": "No weights and no parameter count published, so it can't be run locally" } ], "history": { "deepinfra": [ { "causes": [ "product", "correction" ], "date": "2026-11-02", "delta": 0.6, "from": 63.1, "fromGrade": "B", "to": 63.7, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 63, "fromGrade": "B", "to": 63.1, "toGrade": "B" } ], "muse-ember": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepinfra.json", "title": "Amazon Bedrock vs DeepInfra", "url": 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"https://www.anchorterminal.com/compare/jais-3-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.json", "title": "Meta Model API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.json", "title": "MiniMax API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.json", "title": "Mistral AI API vs Muse Ember", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.json", "title": "Muse Ember vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.json", "title": "Muse Ember vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.json", "title": "Muse Ember vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json", "title": "Muse Ember vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.json", "title": "Muse Ember vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.json", "title": "Muse Ember vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.json", "title": "Muse Ember vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.json", "title": "Muse Ember vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.json", "title": "Muse Ember vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.json", "title": "Muse Ember vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.json", "title": "Muse Ember vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.json", "title": "Muse Ember vs Simplismart", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.json", "title": "Muse Ember vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.json", "title": "Muse Ember vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.json", "title": "Muse Ember vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.json", "title": "Muse Ember vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.json", "title": "Muse Ember vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.json", "title": "Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.json", "title": "Muse Ember vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/muse-ember-vs-xai-api" } ], "panel": null, "scores": [ { "by": 35, "deepinfra": 75, "edge": "deepinfra", "key": "reliability", "muse-ember": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 9, "deepinfra": 69, "edge": "deepinfra", "key": "schema", "muse-ember": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 5, "deepinfra": 77, "edge": "deepinfra", "key": "ergonomics", "muse-ember": 72, "name": "Agent ergonomics", "weight": 13 }, { "by": 0, "deepinfra": 64, "edge": "", "key": "security", "muse-ember": 64, "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "deepinfra": 20, "edge": "", "key": "payments", "muse-ember": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 15, "deepinfra": 61, "edge": "deepinfra", "key": "maintenance", "muse-ember": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 15, "deepinfra": 68, "edge": "deepinfra", "key": "transparency", "muse-ember": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "DeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "deepinfra": "The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.", "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/deepinfra-vs-muse-ember", "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.min.md" }, "markdown": "DeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Both do llm inference.\n\n- DeepInfra: grade B, 63.7/100, rank #501 of 1515. Markdown https://www.anchorterminal.com/tools/deepinfra.md · JSON https://www.anchorterminal.com/api/v1/tools/deepinfra.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### DeepInfra (B)\n\nGood for: Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.\n\nAhead on:\n- Reliability, 75 against 40\n- Schema \u0026 documentation, 69 against 60\n- Agent ergonomics, 77 against 72\n- Maintenance \u0026 community, 61 against 46\n- Transparency \u0026 trust, 68 against 53\n\nWatch for: A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026\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\nWatch for: No weights and no parameter count published, so it can't be run locally\n\n\n## Why the edge\n\n### Reliability: DeepInfra +35\n\n- DeepInfra (75): Hosted reading. Own status page at status.deepinfra.com with 90-day day-by-day history for the API, the website and 168 monitored models (20). On 2 November 2026 the API component shows 100 per cent over 86.7 measured days and the website 99.99 per cent, with one 8-minute degradation on 19 August 2026. The Atom feed's 50 newest items, 20 August to 30 October 2026, are all automated major-outage observations on single models, \"not an announced incident\", among them MiMo-V2.5 from 27 August to 4 September, Qwen3-TTS for 37 and 32 hours, Nemotron-3-Ultra for 25 hours and a run of short Nemotron-3.5-Lightning outages in early September. No written incident touched the API. Each observation is one model, partial under this run's rule, as together-inference's single-model monitor downtime was read, so minor only (20). The 8 October reading of 15 sat between checklist values. 200 concurrent requests per model, published (15). 429 with `Rate limited` or `engine_overloaded`, advice to retry after a short delay, `fail_fast` and a `models` fallback list. No `Retry-After` header or backoff figures were found (10 of 15). No SLA found. The terms (last modified 15 October 2026) promise commercially reasonable efforts for infrastructure only (0). Generally available (10). 20 + 20 + 15 + 10 + 0 + 10 = 75.\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### Maintenance \u0026 community: DeepInfra +15\n\n- DeepInfra (61): Model reading. The docs repository changed on 26 October 2026, and on 22 October documented Nemotron 3.5 image embeddings and reranker scores (30). Deprecated models get at least one week's notice by email, then requests are forwarded to a replacement (5 of 12). Churn. In the 90 days to 2 November four redirect dates passed (8, 9, 12 and 25 October, five models), Kimi-K2.7-Code was deprecated before 28 September, and three more are scheduled for 2 and 3 November. Three or more removal dates in 90 days give 0, as groq and fireworks-inference were read (0 of 8, was 4 when the five were only scheduled). No changelog. Support is by Discord and a feedback address, and the docs repository is public (7 of 15). Replies on the SDK repositories were not examined (3 of 10). Python `deepinfra` 0.3.0 from 12 August 2026 with a changelog. The Node repository tagged 2.1.0 on 1 September 2026, and npm couldn't be read on 2 November, so its 8 October reading of 2.0.2 from 8 May 2024 stands (9 of 15). CI workflows in both SDK repositories (7 of 10). 30 + 5 + 0 + 7 + 3 + 9 + 7 = 61.\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### Transparency \u0026 trust: DeepInfra +15\n\n- DeepInfra (68): Closed service with terms that name the entity and California law, and an MIT licence on the SDKs and docs (15). The terms (last modified 15 October 2026), the privacy policy and the data privacy page agree on no storage and no training, the terms say their retention clause controls, and personal information is removed 30 days after account deletion. The terms now point to a DPA on the trust centre, which lists it among documents available on request, and the entity is written Deep Infra Inc. in the terms and DeepInfra, Inc. in the privacy policy (21 of 30). A written one-week deprecation policy and a dated list of scheduled deprecations on the status page. No record of past removals was found (14 of 20). The sub-processor list names Stripe, Amazon Web Services and Google Cloud Platform, as does the trust centre. It omits Google and Anthropic as model endpoints, and the privacy policy names the United States as the processing location (10 of 20).\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: DeepInfra +9\n\n- DeepInfra (69): Model reading. Public OpenAPI 3.1.0 document at api.deepinfra.com/openapi.json with 131 paths, 157 operations and 257 schemas on 2 November 2026. `llms.txt` now links that document, though the docs host's own copy still returns Mintlify's sample plant store document, and the spec lists chat at `/v1/chat/completions` while every docs example uses `/v1/openai/chat/completions` (22 of 25). `llms.txt` with 224 links and a Markdown copy of every docs page (10). Guide pages for tool calling, structured output, caching, batch and service tiers say when to use each. Many reference operations have a name and no description (14 of 20). Typed parameters with 49 enums, 31 chat properties and two required (11 of 15). Curl, Python and JavaScript examples on each page. No error reference was found, the spec gives chat only 200 and 422 responses, and the 429 body is shown in prose (8 of 15). No changelog was found. The spec version reads 1.0.0, and the docs repository on GitHub has a public commit history (4 of 15).\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### Agent ergonomics: DeepInfra +5\n\n- DeepInfra (77): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` of none, auto, required or a named function, and streaming. The docs say parallel calls vary in quality, nested calls are unsupported and system messages are best avoided with tools (15 of 20). `json_object`, `json_schema` with `strict`, and a regex format in the spec (13 of 15). Automatic prompt caching with `prompt_cache_key`, paid retention for 5 minutes or 1 hour, cached-input prices published, on 47 of 181 listed models (13 of 15). Context of 1,048,576 tokens on DeepSeek V4, Kimi K3 and GLM 5.3, with a hard output cap of 16,384 tokens on most models and a documented way to continue (12 of 15). Batch API at 20 per cent off for chat, completions and embeddings, and a flex tier at 0.8x (10). The OpenAI and Anthropic clients work with a changed base URL. The Python package `deepinfra` 0.3.0 dates from 12 August 2026, and npm listed 2.0.2 from May 2024 on 8 October (7 of 10). 429 carries an `engine_overloaded` code. Native routes return a bare `error` string, and no list of codes was found (7 of 15).\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## Score by category\n\n| Category | Weight | DeepInfra | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 75 | 40 | DeepInfra +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 69 | 60 | DeepInfra +9 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 72 | DeepInfra +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 64 | even |\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) | 61 | 46 | DeepInfra +15 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 68 | 53 | DeepInfra +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.7 · B** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | DeepInfra | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Deep Infra Inc. | Meta |\n| Hosted endpoint | `https://api.deepinfra.com/v1/openai` | `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 | Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT | 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 | 2026-10-26 | 2026-11-09 |\n| Terms last updated | 2026-08-17 | 2026-10-02 |\n| Privacy policy last updated | 2026-08-15 | couldn't be read |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | yes |\n| Popularity | 21 stars, 1.2k npm/wk, 50 PyPI/wk | none |\n| Agent reviews | 3.5/5 (4) | none |\n\n## How the scores have moved\n\n### DeepInfra\n\n- +0.6 on 2 November 2026: product, correction\n- +0.1 on 27 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\n\n**DeepInfra.** The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.\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### DeepInfra\n\n1. Call `GET https://api.deepinfra.com/v1/openai/models` at start-up for ids, context sizes and prices. No key is needed\n2. Check the `model` field of each response. After a deprecation date, requests to the old id are served by a replacement model\n3. Ask the account owner for a scoped JWT limited to the models and spend the task needs, not the full API key\n4. Stay under 200 concurrent requests per model. On 429 `engine_overloaded`, retry after a delay, or send `models` with up to four fallbacks\n5. Never inspect a JWT with `GET /v1/scoped-jwt?jwtoken=`, which puts the token in the URL. Keep credentials in the `Authorization` header\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, DeepInfra or Muse Ember?\n\nDeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories.\n\n### Do DeepInfra and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call DeepInfra and Muse Ember without installing anything?\n\nYes. DeepInfra has a hosted endpoint at https://api.deepinfra.com/v1/openai 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/deepinfra-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepinfra\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare deepinfra muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepinfra.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with DeepInfra or Muse Ember\n\n- [Amazon Bedrock vs DeepInfra](https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepinfra.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs DeepInfra](https://www.anchorterminal.com/compare/anthropic-api-vs-deepinfra.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs DeepInfra](https://www.anchorterminal.com/compare/antseed-vs-deepinfra.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs DeepInfra](https://www.anchorterminal.com/compare/azure-openai-vs-deepinfra.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 DeepInfra](https://www.anchorterminal.com/compare/blockrun-ai-vs-deepinfra.md)\n- [BlockRun.AI vs Muse Ember](https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md)\n- [Cerebras Inference vs DeepInfra](https://www.anchorterminal.com/compare/cerebras-inference-vs-deepinfra.md)\n- [Cerebras Inference vs Muse Ember](https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.md)\n- [Cloudflare AI Gateway vs DeepInfra](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-deepinfra.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 DeepInfra](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-deepinfra.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 DeepInfra](https://www.anchorterminal.com/compare/cohere-chat-vs-deepinfra.md)\n- [Cohere Chat API (Command models) vs Muse Ember](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.md)\n- [DeepInfra vs DeepSeek API](https://www.anchorterminal.com/compare/deepinfra-vs-deepseek-api.md)\n- [DeepInfra vs Eden AI](https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.md)\n- [DeepInfra vs Glimpse](https://www.anchorterminal.com/compare/deepinfra-vs-elorian-glimpse.md)\n- [DeepInfra vs Fireworks AI Inference](https://www.anchorterminal.com/compare/deepinfra-vs-fireworks-inference.md)\n- [DeepInfra vs Gemini Developer API](https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.md)\n- [DeepInfra vs GroqCloud](https://www.anchorterminal.com/compare/deepinfra-vs-groq.md)\n- [DeepInfra vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/deepinfra-vs-hugging-face-inference-providers.md)\n- [DeepInfra vs 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Ember](https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.md)\n- [GroqCloud vs Muse Ember](https://www.anchorterminal.com/compare/groq-vs-muse-ember.md)\n- [Hugging Face Inference Providers vs Muse Ember](https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.md)\n- [Inkling 2 Small vs Muse Ember](https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.md)\n- [Jais 3 vs Muse Ember](https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.md)\n- [Meta Model API vs Muse Ember](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md)\n- [MiniMax API vs Muse Ember](https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.md)\n- [Mistral AI API vs Muse Ember](https://www.anchorterminal.com/compare/mistral-api-vs-muse-ember.md)\n- [Muse Ember vs Muse Spark Open](https://www.anchorterminal.com/compare/muse-ember-vs-muse-spark-open.md)\n- [Muse Ember vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/muse-ember-vs-nemotron-3-5-super-vl.md)\n- [Muse Ember vs Novita AI](https://www.anchorterminal.com/compare/muse-ember-vs-novita-ai.md)\n- [Muse Ember vs OpenAI API](https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.md)\n- [Muse Ember vs OpenRouter](https://www.anchorterminal.com/compare/muse-ember-vs-openrouter.md)\n- [Muse Ember vs Prism Inference](https://www.anchorterminal.com/compare/muse-ember-vs-prism-inference.md)\n- [Muse Ember vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/muse-ember-vs-ramp-router.md)\n- [Muse Ember vs Beam 1.0](https://www.anchorterminal.com/compare/muse-ember-vs-reflection-beam.md)\n- [Muse Ember vs RouterPlus](https://www.anchorterminal.com/compare/muse-ember-vs-routerplus.md)\n- [Muse Ember vs SambaCloud](https://www.anchorterminal.com/compare/muse-ember-vs-sambanova.md)\n- [Muse Ember vs SiliconFlow](https://www.anchorterminal.com/compare/muse-ember-vs-siliconflow.md)\n- [Muse Ember vs Simplismart](https://www.anchorterminal.com/compare/muse-ember-vs-simplismart.md)\n- [Muse Ember vs StepFun API](https://www.anchorterminal.com/compare/muse-ember-vs-stepfun-api.md)\n- [Muse Ember vs SWE-2 Flash](https://www.anchorterminal.com/compare/muse-ember-vs-swe-2-flash.md)\n- [Muse Ember vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/muse-ember-vs-tencent-tokenhub.md)\n- [Muse Ember vs Together AI Inference](https://www.anchorterminal.com/compare/muse-ember-vs-together-inference.md)\n- [Muse Ember vs Vercel AI Gateway](https://www.anchorterminal.com/compare/muse-ember-vs-vercel-ai-gateway.md)\n- [Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/muse-ember-vs-vertex-ai.md)\n- [Muse Ember vs xAI API (Grok)](https://www.anchorterminal.com/compare/muse-ember-vs-xai-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, 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