{
  "data": {
    "a": {
      "slug": "minimax-api",
      "name": "MiniMax API",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "model",
      "category": "inference",
      "summary": "MiniMax's API for its M-series text models, M3.1 Flash and M3, in the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses formats, with 1M-token context and automatic prompt caching.",
      "url": "https://www.anchorterminal.com/tools/minimax-api",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-api.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-api.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-api.json",
      "dns": "minimax-api.grade.anchorterminal.com",
      "repo": "https://github.com/MiniMax-AI/MiniMax-M3",
      "license": "Proprietary API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.minimax.io",
      "packages": [],
      "auth": "api-key",
      "authNotes": "Bearer key from platform.minimax.io, charged to a prepaid balance. M Plan subscribers get a separate Subscription Key that draws on the plan's usage windows, and the two kinds of key can't be swapped. Mainland China accounts use api.minimaxi.com with a separate key. MiniMax says it may disable a key that leaks publicly.",
      "pricing": "usage",
      "pricingNotes": "Prepaid balance, pay as you go. MiniMax-M3.1-Flash is $0.15 per million input tokens, $0.60 per million output tokens and $0.015 per million cache-read tokens at any input length. MiniMax-M3 is $0.30 in and $1.20 out up to 512K input tokens and $0.60 and $2.40 above, with cache reads at $0.06 and $0.12, and a Priority tier at 1.5 times that M3.1 Flash doesn't take. Caching is automatic. The web_search server tool (beta) is $0.01 a request. M Plan subscriptions for coding tools are Go $22, Explore $55 and Build $132 a month, and annual billing pays for 10 months (https://platform.minimax.io/docs/pricing/overview, https://platform.minimax.io/docs/m-plan/monthly-offer, checked 19 October 2026).",
      "priceSummary": "from $0.15 / 1M in",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the docs, llms.txt or pricing page. Prepaid balance top-up or an M Plan subscription (checked 2026-10-19).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 493,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-19"
      },
      "docsUrl": "https://platform.minimax.io/docs/guides/text-generation",
      "rateLimitsUrl": "https://platform.minimax.io/docs/guides/rate-limits",
      "llmsTxt": "https://platform.minimax.io/docs/llms.txt",
      "openapi": "https://platform.minimax.io/docs/api-reference/text/api/openapi-chat-anthropic.json",
      "capabilities": [
        "inference.llm",
        "inference.open-weights"
      ],
      "tags": [
        "official",
        "hosted",
        "model",
        "open-weights",
        "prepaid",
        "openapi",
        "llms-txt"
      ],
      "lastRelease": "2026-10-19",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 55.3,
        "grade": "C",
        "agentReady": false,
        "rank": 879,
        "ranked": true,
        "rankOf": 1515,
        "categoryRank": 26,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 59,
          "maintenance": 45,
          "payments": 20,
          "reliability": 75,
          "schema": 82,
          "security": 30,
          "transparency": 65
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-11-04"
        },
        "negative": 0,
        "verdict": "M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour.",
        "bestFor": "Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.",
        "strengths": [
          "M3.1 Flash at $0.15 in, $0.60 out and $0.015 for cache reads per million tokens, flat at any input length",
          "Anthropic Messages, OpenAI Chat Completions and OpenAI Responses formats, each with an OpenAPI 3.1 file",
          "1M-token context and up to 524,288 output tokens on M3.1 Flash and M3",
          "Automatic prompt caching and a token-count endpoint",
          "MiniMax-M3 and, since 23 October 2026, MiniMax-M3.1-Flash weights on Hugging Face under the MiniMax community licence"
        ],
        "weaknesses": [
          "The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found",
          "No free tier, SLA, batch API, JSON Schema output mode or security.txt",
          "212 'Elevated errors' incidents on the LLM component from 6 August to 3 November 2026, one of 65 minutes and the rest under an hour",
          "M2.5 and M2.7 retire on 18 November 2026, 30 days after new users lost them, and plan requests to them already go to M3.1 Flash",
          "The model list calls M2.5 and M2.7 retired while the notice on the same page gives 18 November 2026"
        ],
        "agentNotes": [
          "Use the Anthropic-format base URL `https://api.minimax.io/anthropic` for tool calls. The OpenAI-format schema has no `tool_choice` or `response_format`",
          "Set `output_config.effort` (or `reasoning_effort`) below `max` for routine calls. M3.1 Flash defaults to max, can't turn thinking off, and counts thinking against `max_tokens`",
          "Don't rely on `stop_sequences` or `top_k`. The Anthropic-format endpoint ignores them",
          "Back off on 429, 529 and the retry-later codes (1002, 2064) yourself. No Retry-After header is documented",
          "Use a pay-as-you-go key for production. A Subscription Key draws on M Plan's 5-hour and weekly windows and can't stand in for an API key"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 2.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 55.3
          }
        ],
        "editorialScores": {
          "ergonomics": 59,
          "maintenance": 45,
          "payments": 20,
          "reliability": 75,
          "schema": 82,
          "security": 30,
          "transparency": 47
        },
        "provenanceScore": 82,
        "checked": {
          "categories": {
            "ergonomics": "2026-11-04",
            "maintenance": "2026-11-04",
            "payments": "2026-11-04",
            "reliability": "2026-11-04",
            "schema": "2026-11-04",
            "security": "2026-11-04",
            "transparency": "2026-11-04"
          },
          "last": "2026-11-04",
          "lastMoved": "2026-10-28",
          "oldest": "2026-11-04",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "http": "curl https://api.minimax.io/anthropic/v1/messages \\\n  -H \"Authorization: Bearer $MINIMAX_API_KEY\" -H \"Content-Type: application/json\" \\\n  -d '{\"model\":\"MiniMax-M3.1-Flash\",\"output_config\":{\"effort\":\"low\"},\"max_tokens\":4096,\"messages\":[{\"role\":\"user\",\"content\":\"hello\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/inference.llm",
        "tool": "https://letme.dev/minimax-api"
      },
      "sameCompany": [
        "minimax-video",
        "minimax-stt",
        "minimax-speech",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-5",
        "minimax-m2-7",
        "minimax-m3-1-flash"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "web_search server tool (beta)",
          "unit": "1k-requests",
          "usd": 10
        },
        {
          "item": "M Plan Go",
          "unit": "month",
          "usd": 22,
          "note": "M3.1 Flash, image and audio, no video. Annual billing pays for 10 months"
        },
        {
          "item": "M Plan Explore",
          "unit": "month",
          "usd": 55,
          "note": "3 times Go's usage, adds H3 video"
        },
        {
          "item": "M Plan Build",
          "unit": "month",
          "usd": 132,
          "note": "7.5 times Go's usage, adds H3 video"
        }
      ],
      "provenance": {
        "legalEntity": "Nanonoble Pte. Ltd.",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": true,
        "terms": "https://platform.minimax.io/protocol/terms-of-service",
        "privacy": "https://platform.minimax.io/protocol/privacy-policy",
        "statusPage": "https://status.minimax.io",
        "changelog": "https://platform.minimax.io/docs/release-notes/models",
        "securityTxt": "none",
        "checked": "2026-11-04",
        "notes": [
          "Nanonoble Pte. Ltd., 152 Beach Road, #14-02 Gateway East, Singapore, is named in the terms (effective 30 March 2026) and the privacy policy (updated 30 March 2026), read on 19 October and again on 4 November 2026 from the document text the pages carry for the browser. The policy reader recorded both pages as unreadable on 8 October, and that reading is kept.",
          "RDAP for minimax.io gives a registration date of 2021-03-27 and a transfer on 2025-01-31, as recorded on the MiniMax Video API listing.",
          "platform.minimax.io answers 404 for /.well-known/security.txt (4 November 2026). api.minimax.io answered 404 and www.minimax.io redirected it to the home page on 19 October.",
          "The API release notes page has no entry after 28 October 2025. Model releases are dated on a separate page, whose newest entry on 4 November 2026 is 19 October 2026."
        ],
        "score": 82
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-api.json",
      "live": {
        "slug": "minimax-api",
        "probe": {
          "target": "https://api.minimax.io",
          "method": "get",
          "lastAt": "2026-11-09T20:50:01.634826924Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 202,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 248,
          "p95ms24h": 519,
          "samples24h": 244,
          "samples30d": 5217,
          "days": [
            {
              "date": "2026-10-19",
              "probes": 93,
              "ok": 93
            },
            {
              "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": "2026-10-19T00:00:00Z"
        },
        "vendorStatus": {
          "page": "https://status.minimax.io",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-11-09T20:46:24.170899424Z"
        },
        "githubStars": 500,
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-11-09T16:05:03.409893194Z"
        },
        "llmsTxt": {
          "url": "https://platform.minimax.io/docs/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-11-09T15:40:05.594964662Z"
        },
        "domain": {
          "domain": "minimax.io",
          "checkedAt": "2026-11-08T14:54:44.54388672Z"
        },
        "pages": [
          {
            "url": "https://platform.minimax.io/docs/faq/models",
            "kind": "deprecations",
            "status": 200,
            "checkedAt": "2026-11-09T15:47:45.24467087Z",
            "changedAt": "2026-11-06T13:38:25.889680746Z",
            "fingerprint": "8abebc4199ab"
          },
          {
            "url": "https://platform.minimax.io/docs/release-notes/models",
            "kind": "deprecations",
            "status": 200,
            "checkedAt": "2026-11-09T15:47:51.522815342Z",
            "changedAt": "2026-11-05T12:39:38.489181558Z",
            "fingerprint": "3b7661f72594"
          },
          {
            "url": "https://platform.minimax.io/docs/pricing/overview",
            "kind": "pricing",
            "status": 200,
            "checkedAt": "2026-11-09T15:47:47.571293297Z",
            "changedAt": "2026-11-09T15:47:47.571293297Z",
            "fingerprint": "6bfc1791acd4"
          },
          {
            "url": "https://platform.minimax.io/protocol/privacy-policy",
            "kind": "privacy",
            "status": 200,
            "checkedAt": "2026-11-09T15:47:53.439846845Z",
            "changedAt": "2026-10-31T06:18:23.585835081Z",
            "fingerprint": "1ae59257a3a9"
          },
          {
            "url": "https://platform.minimax.io/protocol/terms-of-service",
            "kind": "terms",
            "status": 200,
            "checkedAt": "2026-11-09T15:47:55.367451099Z",
            "changedAt": "2026-10-31T06:18:25.544630345Z",
            "fingerprint": "f98c6c8b7950"
          }
        ],
        "github": {
          "repo": "minimax-ai/minimax-m3",
          "checkedAt": "2026-11-09T10:02:02.118636196Z",
          "stars": 500,
          "archived": false,
          "issuesEnabled": true,
          "pushedAt": "2026-10-26T07:05:04Z",
          "openIssues": 29,
          "openPRs": 0,
          "issuesOpened90d": 8,
          "issuesClosed90d": 0,
          "prsMerged90d": 1,
          "replies": {
            "sampled": 25,
            "unanswered": 23,
            "medianDays": 8.8
          }
        },
        "updatedAt": "2026-11-09T20:50:01.634826924Z"
      },
      "streak": {
        "days": 21,
        "from": "2026-10-19",
        "sinceWatching": true,
        "next": {
          "kind": "shutdown",
          "date": "2026-11-18",
          "what": "MiniMax-M2.5, M2.5-highspeed, M2.7 and M2.7-highspeed retire, replacement MiniMax-M3.1-Flash",
          "source": "https://platform.minimax.io/docs/faq/models"
        },
        "best": 21
      }
    },
    "answer": "MiniMax API scores 55.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 3 of 7 scored categories. Muse Ember leads on agent ergonomics and security \u0026 auth.",
    "b": {
      "slug": "muse-ember",
      "name": "Muse Ember",
      "vendor": "Meta",
      "vendorUrl": "https://dev.meta.ai",
      "kind": "model",
      "category": "inference",
      "summary": "Muse Ember is Meta's smallest and most efficient hosted model, built for sub-agent tasks, tool calls, routing and work over large collections of files and images, taking text, images, video, audio and PDF with a 1M-token context.",
      "url": "https://www.anchorterminal.com/tools/muse-ember",
      "markdownUrl": "https://www.anchorterminal.com/tools/muse-ember.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-ember.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-ember.json",
      "dns": "muse-ember.grade.anchorterminal.com",
      "license": "Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer",
      "transports": [],
      "remoteUrl": "https://api.meta.ai/v1",
      "packages": [],
      "auth": "api-key",
      "authNotes": "A bearer key from the API keys tab of the Model API dashboard, read from MODEL_API_KEY in Meta's examples, after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team and share its limits, with no scopes found, per the meta-model-api listing.",
      "pricing": "usage",
      "pricingNotes": "$0.10 per million input tokens, $0.004 cached and $0.50 output for requests up to 200K input tokens, and $0.30, $0.012 and $1.50 for the whole request above that. Standard mode only, no Contributor discount. Web search grounding $2.50 per 1,000 queries. Included in every Muse Code plan. No free API allowance found (checked 2026-11-09).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 on Meta's pricing page, which bills tokens to a team account with a card (checked 2026-11-09).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-09"
      },
      "docsUrl": "https://dev.meta.ai/docs/muse-ember",
      "llmsTxt": "https://dev.meta.ai/llms.txt",
      "capabilities": [
        "inference.llm"
      ],
      "tags": [
        "official",
        "hosted",
        "model",
        "multimodal",
        "vision",
        "reasoning",
        "long-context",
        "tool-calling",
        "openai-compatible",
        "anthropic-compatible",
        "usage-priced",
        "card-required",
        "closed-source",
        "llms-txt",
        "new"
      ],
      "lastRelease": "2026-11-09",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 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": "minimax-api",
        "name": "Reliability",
        "reasons": {
          "a": "Atlassian Statuspage at status.minimax.io with Large Language Models, Text-to-Speech and Video Generation components (20). Its history pages, re-read on 4 November, now cover the whole 90 days from 6 August. They list 212 incidents on the LLM component, every one titled 'Elevated errors on Large Language Models (LLM)' and labelled major, and none states a full outage. The longest ran 65 minutes (01:15 to 02:20 UTC on 20 October, 'We are currently investigating this issue' and then resolved), and the rest closed within the hour. This run reads elevated errors with no fuller scope as partial, so minor only, frequent as they are (20). The rate-limits page publishes 200 requests and 10,000,000 tokens a minute for M3.1 Flash and M3 (15). The Anthropic-format OpenAPI file documents 429 for rate limits and 529 as an overload that can be retried, and the error-code page says to retry later on 1002, 2064 and other codes, with no Retry-After header or backoff schedule found (10). No SLA found (0). M3.1 Flash and M3 are generally available (10). Total 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": 34,
        "category": "security",
        "leads": "muse-ember",
        "name": "Security \u0026 auth",
        "reasons": {
          "a": "Model reading (credential, training, retention, operator visibility, programme). One bearer key per account on the pay-as-you-go side and a separate Subscription Key for M Plan, with no scopes found. MiniMax says it may disable keys that leak publicly (20 of 30). The terms, effective 30 March 2026 and unchanged on 4 November, say MiniMax may use input and generated content to provide, maintain, develop and improve its services, and no opt-out was found. The privacy policy says only that personal data isn't used for training to profile or target consumers (0 of 20). Data is kept as long as necessary for its purpose, with no period stated and no zero-retention route (5 of 15). A usage page and balance alerts, no audit log found (5 of 15). No security.txt on platform.minimax.io (404 again on 4 November) or api.minimax.io (per the 19 October check), and no bug bounty or certification found (0 of 20). Total 30.",
          "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": 30,
          "b": 64
        }
      },
      {
        "by": 22,
        "category": "schema",
        "leads": "minimax-api",
        "name": "Schema \u0026 documentation",
        "reasons": {
          "a": "OpenAPI 3.1 files for the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses endpoints, each listing MiniMax-M3.1-Flash and MiniMax-M3 (25). llms.txt indexes the docs, and each page is served as Markdown (10). Field descriptions say which model each setting applies to, such as `service_tier` not being supported on M3.1 Flash and `thinking: disabled` returning 400 there, and a compatibility table marks `top_k`, `stop_sequences`, `mcp_servers` and `container` as ignored (15 of 20). Enums for model, `effort`, `service_tier` and thinking type, with required fields marked. The OpenAI-format schema has no `tool_choice` or `response_format` (11 of 15). The Anthropic-format file documents typed errors for 400, 401, 403, 404, 413, 429, 500 and 529 with example bodies, and a page lists 45 error codes with a fix for each (the 19 October note counted 23; 2069, model no longer available, is among them on 4 November), but the OpenAI and Responses files document only 200 (11 of 15). A dated model release page with the 19 October entry as its newest on 4 November, while the API release notes still stop at 28 October 2025 (10 of 15). Total 82.",
          "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": 82,
          "b": 60
        }
      },
      {
        "by": 13,
        "category": "ergonomics",
        "leads": "muse-ember",
        "name": "Agent ergonomics",
        "reasons": {
          "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use on all three formats, with `tool_choice` on the Anthropic format only (17 of 20). No JSON Schema output mode documented on any format (0 of 15). Caching is automatic, with cache reads at a tenth of the input price on M3.1 Flash and a token-count endpoint for sizing a request (15). 1,000,000-token context and up to 524,288 output tokens (15). No batch API found (0). No official SDK. The docs point to the Anthropic and OpenAI SDKs and the AI SDK (0). Typed error bodies with a request id on the Anthropic format and a code table, though `stop_sequences` is ignored without an error (12 of 15). M3.1 Flash can't turn thinking off and defaults to the deepest `effort`, which costs output tokens unless the caller lowers it. Total 59.",
          "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": 59,
          "b": 72
        }
      },
      {
        "by": 12,
        "category": "transparency",
        "leads": "minimax-api",
        "name": "Transparency \u0026 trust",
        "reasons": {
          "a": "Closed service with clear terms under Singapore law and SIAC arbitration. MiniMax-M3's weights, and since 23 October 2026 MiniMax-M3.1-Flash's, are on Hugging Face under the MiniMax community licence, which isn't an OSI licence and doesn't change the hosted service (15). The terms and privacy policy, both still dated 30 March 2026 on 4 November, agree on the contracting entity, that inputs and outputs are processed and may be used to improve services, and that data sits in a United States data centre, but give no retention period and no DPA was read (10 of 30). A dated retirement notice for M2.5 and M2.7 and a retired-models table, undercut by the table contradicting the notice and API release notes a year stale (12 of 20). Data location stated as the United States and an EU representative named, with third parties named only by category (10 of 20). MiniMax publishes an EU AI Act training-data summary for M3. Total 47.",
          "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
        }
      }
    ],
    "facts": [
      {
        "a": "Model API",
        "b": "Model API",
        "name": "Kind"
      },
      {
        "a": "MiniMax",
        "b": "Meta",
        "name": "Vendor"
      },
      {
        "a": "https://api.minimax.io",
        "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 API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence",
        "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": "2026-10-19",
        "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": "493 stars",
        "b": "none",
        "name": "Popularity"
      },
      {
        "a": "2.8/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "MiniMax API scores 55.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 3 of 7 scored categories. Muse Ember leads on agent ergonomics and security \u0026 auth.",
        "question": "Which is better for AI agents, MiniMax API or Muse Ember?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do MiniMax API and Muse Ember need an API key?"
      },
      {
        "answer": "Yes. MiniMax API has a hosted endpoint at https://api.minimax.io and Muse Ember at https://api.meta.ai/v1.",
        "question": "Can an agent call MiniMax API and Muse Ember without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 75 against 40",
          "Schema \u0026 documentation, 82 against 60",
          "Transparency \u0026 trust, 65 against 53"
        ],
        "also": null,
        "goodFor": "Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.",
        "slug": "minimax-api",
        "watchFor": "The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found"
      },
      {
        "aheadOn": [
          "Agent ergonomics, 72 against 59",
          "Security \u0026 auth, 64 against 30"
        ],
        "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": {
      "minimax-api": [
        {
          "causes": [
            "method"
          ],
          "date": "2026-10-28",
          "delta": 0.1,
          "from": 55.2,
          "fromGrade": "C",
          "to": 55.3,
          "toGrade": "C"
        }
      ],
      "muse-ember": null
    },
    "job": {
      "capability": "inference.llm",
      "name": "LLM inference"
    },
    "others": [
      {
        "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-minimax-api.json",
        "title": "Amazon Bedrock vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.json",
        "title": "Amazon Bedrock vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-minimax-api.json",
        "title": "Claude API vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.json",
        "title": "Claude API vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/antseed-vs-minimax-api.json",
        "title": "Antseed vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/antseed-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember.json",
        "title": "Antseed vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.json",
        "title": "Azure OpenAI in Foundry Models vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember.json",
        "title": "Azure OpenAI in Foundry Models vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/azure-openai-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-minimax-api.json",
        "title": "BlockRun.AI vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.json",
        "title": "BlockRun.AI vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-minimax-api.json",
        "title": "Cerebras Inference vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember.json",
        "title": "Cerebras Inference vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-minimax-api.json",
        "title": "Cloudflare AI Gateway vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember.json",
        "title": "Cloudflare AI Gateway vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-minimax-api.json",
        "title": "Cloudflare Workers AI vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember.json",
        "title": "Cloudflare Workers AI vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-minimax-api.json",
        "title": "Cohere Chat API (Command models) vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember.json",
        "title": "Cohere Chat API (Command models) vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepinfra-vs-minimax-api.json",
        "title": "DeepInfra vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/deepinfra-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.json",
        "title": "DeepInfra vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-minimax-api.json",
        "title": "DeepSeek API vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember.json",
        "title": "DeepSeek API vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api.json",
        "title": "Eden AI vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember.json",
        "title": "Eden AI vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-minimax-api.json",
        "title": "Glimpse vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember.json",
        "title": "Glimpse vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-minimax-api.json",
        "title": "Fireworks AI Inference vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember.json",
        "title": "Fireworks AI Inference vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api.json",
        "title": "Gemini Developer API vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember.json",
        "title": "Gemini Developer API vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/groq-vs-minimax-api.json",
        "title": "GroqCloud vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/groq-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/groq-vs-muse-ember.json",
        "title": "GroqCloud vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/groq-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-minimax-api.json",
        "title": "Hugging Face Inference Providers vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember.json",
        "title": "Hugging Face Inference Providers vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/hugging-face-inference-providers-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api.json",
        "title": "Inkling 2 Small vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember.json",
        "title": "Inkling 2 Small vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api.json",
        "title": "Jais 3 vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/jais-3-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember.json",
        "title": "Jais 3 vs Muse Ember",
        "url": "https://www.anchorterminal.com/compare/jais-3-vs-muse-ember"
      },
      {
        "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-api.json",
        "title": "Meta Model API vs MiniMax API",
        "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-minimax-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-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-mistral-api.json",
        "title": "MiniMax API vs Mistral AI API",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-mistral-api"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open.json",
        "title": "MiniMax API vs Muse Spark Open",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-muse-spark-open"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-nemotron-3-5-super-vl.json",
        "title": "MiniMax API vs Nemotron 3.5 Super VL",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-nemotron-3-5-super-vl"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-novita-ai.json",
        "title": "MiniMax API vs Novita AI",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-novita-ai"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api.json",
        "title": "MiniMax API vs OpenAI API",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-openai-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-openrouter.json",
        "title": "MiniMax API vs OpenRouter",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-openrouter"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-prism-inference.json",
        "title": "MiniMax API vs Prism Inference",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-prism-inference"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-ramp-router.json",
        "title": "MiniMax API vs Router.com (Ramp Router)",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-ramp-router"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-reflection-beam.json",
        "title": "MiniMax API vs Beam 1.0",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-reflection-beam"
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      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-routerplus.json",
        "title": "MiniMax API vs RouterPlus",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-routerplus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-sambanova.json",
        "title": "MiniMax API vs SambaCloud",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-sambanova"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow.json",
        "title": "MiniMax API vs SiliconFlow",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-siliconflow"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart.json",
        "title": "MiniMax API vs Simplismart",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-simplismart"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-stepfun-api.json",
        "title": "MiniMax API vs StepFun API",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-stepfun-api"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-swe-2-flash.json",
        "title": "MiniMax API vs SWE-2 Flash",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-swe-2-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-tencent-tokenhub.json",
        "title": "MiniMax API vs Tencent Cloud TokenHub",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-tencent-tokenhub"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-together-inference.json",
        "title": "MiniMax API vs Together AI Inference",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-together-inference"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-vercel-ai-gateway.json",
        "title": "MiniMax API vs Vercel AI Gateway",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-vercel-ai-gateway"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai.json",
        "title": "MiniMax API vs Vertex AI (Gemini Enterprise Agent Platform)",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-vertex-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-api-vs-xai-api.json",
        "title": "MiniMax API vs xAI API (Grok)",
        "url": "https://www.anchorterminal.com/compare/minimax-api-vs-xai-api"
      },
      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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      {
        "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"
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    ],
    "panel": null,
    "scores": [
      {
        "by": 35,
        "edge": "minimax-api",
        "key": "reliability",
        "minimax-api": 75,
        "muse-ember": 40,
        "name": "Reliability",
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "by": 22,
        "edge": "minimax-api",
        "key": "schema",
        "minimax-api": 82,
        "muse-ember": 60,
        "name": "Schema \u0026 documentation",
        "weight": 13
      },
      {
        "by": 13,
        "edge": "muse-ember",
        "key": "ergonomics",
        "minimax-api": 59,
        "muse-ember": 72,
        "name": "Agent ergonomics",
        "weight": 13
      },
      {
        "by": 34,
        "edge": "muse-ember",
        "key": "security",
        "minimax-api": 30,
        "muse-ember": 64,
        "name": "Security \u0026 auth",
        "weight": 14
      },
      {
        "by": 0,
        "edge": "",
        "key": "payments",
        "minimax-api": 20,
        "muse-ember": 20,
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "by": 1,
        "edge": "muse-ember",
        "key": "maintenance",
        "minimax-api": 45,
        "muse-ember": 46,
        "name": "Maintenance \u0026 community",
        "weight": 7
      },
      {
        "by": 12,
        "edge": "minimax-api",
        "key": "transparency",
        "minimax-api": 65,
        "muse-ember": 53,
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
    ],
    "summary": "MiniMax API scores 55.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 3 of 7 scored categories. Muse Ember leads on agent ergonomics and security \u0026 auth. Both do llm inference.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "minimax-api": "M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour.",
      "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."
    }
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  "markdown": "MiniMax API scores 55.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 3 of 7 scored categories. Muse Ember leads on agent ergonomics and security \u0026 auth. Both do llm inference.\n\n- MiniMax API: grade C, 55.3/100, rank #879 of 1515. Markdown https://www.anchorterminal.com/tools/minimax-api.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-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### MiniMax API (C)\n\nGood for: Cheap long-context agent and coding calls through an Anthropic-compatible client, on data you can share with the vendor.\n\nAhead on:\n- Reliability, 75 against 40\n- Schema \u0026 documentation, 82 against 60\n- Transparency \u0026 trust, 65 against 53\n\nWatch for: The terms let MiniMax use inputs and outputs to develop and improve its services, and no opt-out was found\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- Agent ergonomics, 72 against 59\n- Security \u0026 auth, 64 against 30\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: MiniMax API +35\n\n- MiniMax API (75): Atlassian Statuspage at status.minimax.io with Large Language Models, Text-to-Speech and Video Generation components (20). Its history pages, re-read on 4 November, now cover the whole 90 days from 6 August. They list 212 incidents on the LLM component, every one titled 'Elevated errors on Large Language Models (LLM)' and labelled major, and none states a full outage. The longest ran 65 minutes (01:15 to 02:20 UTC on 20 October, 'We are currently investigating this issue' and then resolved), and the rest closed within the hour. This run reads elevated errors with no fuller scope as partial, so minor only, frequent as they are (20). The rate-limits page publishes 200 requests and 10,000,000 tokens a minute for M3.1 Flash and M3 (15). The Anthropic-format OpenAPI file documents 429 for rate limits and 529 as an overload that can be retried, and the error-code page says to retry later on 1002, 2064 and other codes, with no Retry-After header or backoff schedule found (10). No SLA found (0). M3.1 Flash and M3 are generally available (10). Total 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### Security \u0026 auth: Muse Ember +34\n\n- MiniMax API (30): Model reading (credential, training, retention, operator visibility, programme). One bearer key per account on the pay-as-you-go side and a separate Subscription Key for M Plan, with no scopes found. MiniMax says it may disable keys that leak publicly (20 of 30). The terms, effective 30 March 2026 and unchanged on 4 November, say MiniMax may use input and generated content to provide, maintain, develop and improve its services, and no opt-out was found. The privacy policy says only that personal data isn't used for training to profile or target consumers (0 of 20). Data is kept as long as necessary for its purpose, with no period stated and no zero-retention route (5 of 15). A usage page and balance alerts, no audit log found (5 of 15). No security.txt on platform.minimax.io (404 again on 4 November) or api.minimax.io (per the 19 October check), and no bug bounty or certification found (0 of 20). Total 30.\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### Schema \u0026 documentation: MiniMax API +22\n\n- MiniMax API (82): OpenAPI 3.1 files for the Anthropic Messages, OpenAI Chat Completions and OpenAI Responses endpoints, each listing MiniMax-M3.1-Flash and MiniMax-M3 (25). llms.txt indexes the docs, and each page is served as Markdown (10). Field descriptions say which model each setting applies to, such as `service_tier` not being supported on M3.1 Flash and `thinking: disabled` returning 400 there, and a compatibility table marks `top_k`, `stop_sequences`, `mcp_servers` and `container` as ignored (15 of 20). Enums for model, `effort`, `service_tier` and thinking type, with required fields marked. The OpenAI-format schema has no `tool_choice` or `response_format` (11 of 15). The Anthropic-format file documents typed errors for 400, 401, 403, 404, 413, 429, 500 and 529 with example bodies, and a page lists 45 error codes with a fix for each (the 19 October note counted 23; 2069, model no longer available, is among them on 4 November), but the OpenAI and Responses files document only 200 (11 of 15). A dated model release page with the 19 October entry as its newest on 4 November, while the API release notes still stop at 28 October 2025 (10 of 15). Total 82.\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: Muse Ember +13\n\n- MiniMax API (59): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use on all three formats, with `tool_choice` on the Anthropic format only (17 of 20). No JSON Schema output mode documented on any format (0 of 15). Caching is automatic, with cache reads at a tenth of the input price on M3.1 Flash and a token-count endpoint for sizing a request (15). 1,000,000-token context and up to 524,288 output tokens (15). No batch API found (0). No official SDK. The docs point to the Anthropic and OpenAI SDKs and the AI SDK (0). Typed error bodies with a request id on the Anthropic format and a code table, though `stop_sequences` is ignored without an error (12 of 15). M3.1 Flash can't turn thinking off and defaults to the deepest `effort`, which costs output tokens unless the caller lowers it. Total 59.\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### Transparency \u0026 trust: MiniMax API +12\n\n- MiniMax API (65): Closed service with clear terms under Singapore law and SIAC arbitration. MiniMax-M3's weights, and since 23 October 2026 MiniMax-M3.1-Flash's, are on Hugging Face under the MiniMax community licence, which isn't an OSI licence and doesn't change the hosted service (15). The terms and privacy policy, both still dated 30 March 2026 on 4 November, agree on the contracting entity, that inputs and outputs are processed and may be used to improve services, and that data sits in a United States data centre, but give no retention period and no DPA was read (10 of 30). A dated retirement notice for M2.5 and M2.7 and a retired-models table, undercut by the table contradicting the notice and API release notes a year stale (12 of 20). Data location stated as the United States and an EU representative named, with third parties named only by category (10 of 20). MiniMax publishes an EU AI Act training-data summary for M3. Total 47.\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## Score by category\n\n| Category | Weight | MiniMax API | Muse Ember | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 75 | 40 | MiniMax API +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 82 | 60 | MiniMax API +22 |\n| Agent ergonomics | 13% (16.2 this run) | 59 | 72 | Muse Ember +13 |\n| Security \u0026 auth | 14% (17.5 this run) | 30 | 64 | Muse Ember +34 |\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) | 45 | 46 | Muse Ember +1 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 65 | 53 | MiniMax API +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.3 · C** | **51.8 · D** | |\n\n## Facts side by side\n\n| Fact | MiniMax API | Muse Ember |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | MiniMax | Meta |\n| Hosted endpoint | `https://api.minimax.io` | `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 API under MiniMax's platform terms. MiniMax-M3 and MiniMax-M3.1-Flash weights are on Hugging Face under the MiniMax community licence | 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 | 2026-10-19 | 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| Popularity | 493 stars | none |\n| Agent reviews | 2.8/5 (4) | none |\n\n## How the scores have moved\n\n### MiniMax API\n\n- +0.1 on 28 October 2026: checklist\n\n### Muse Ember\n\nNo score change since it was listed.\n\n## Verdicts\n\n**MiniMax API.** M3.1 Flash costs $0.15 in and $0.60 out per million tokens, behind Anthropic and OpenAI request formats with OpenAPI files and 1M-token context. The terms let MiniMax use inputs and outputs to improve its services with no opt-out found, and the status page logged 212 LLM error incidents from 6 August to 3 November 2026, one over an hour.\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### MiniMax API\n\n1. Use the Anthropic-format base URL `https://api.minimax.io/anthropic` for tool calls. The OpenAI-format schema has no `tool_choice` or `response_format`\n2. Set `output_config.effort` (or `reasoning_effort`) below `max` for routine calls. M3.1 Flash defaults to max, can't turn thinking off, and counts thinking against `max_tokens`\n3. Don't rely on `stop_sequences` or `top_k`. The Anthropic-format endpoint ignores them\n4. Back off on 429, 529 and the retry-later codes (1002, 2064) yourself. No Retry-After header is documented\n5. Use a pay-as-you-go key for production. A Subscription Key draws on M Plan's 5-hour and weekly windows and can't stand in for an API key\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, MiniMax API or Muse Ember?\n\nMiniMax API scores 55.3 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 3 of 7 scored categories. Muse Ember leads on agent ergonomics and security \u0026 auth.\n\n### Do MiniMax API and Muse Ember need an API key?\n\nBoth need an API key.\n\n### Can an agent call MiniMax API and Muse Ember without installing anything?\n\nYes. MiniMax API has a hosted endpoint at https://api.minimax.io 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/minimax-api-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/minimax-api-vs-muse-ember.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"minimax-api\", \"b\": \"muse-ember\"}`. From a terminal: `anchor compare minimax-api muse-ember`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/minimax-api.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json\n\n## Other comparisons with MiniMax API or Muse Ember\n\n- [Amazon Bedrock vs MiniMax API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-minimax-api.md)\n- [Amazon Bedrock vs Muse Ember](https://www.anchorterminal.com/compare/amazon-bedrock-vs-muse-ember.md)\n- [Claude API vs MiniMax API](https://www.anchorterminal.com/compare/anthropic-api-vs-minimax-api.md)\n- [Claude API vs Muse Ember](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-ember.md)\n- [Antseed vs MiniMax API](https://www.anchorterminal.com/compare/antseed-vs-minimax-api.md)\n- [Antseed vs Muse Ember](https://www.anchorterminal.com/compare/antseed-vs-muse-ember.md)\n- [Azure OpenAI in Foundry Models vs MiniMax API](https://www.anchorterminal.com/compare/azure-openai-vs-minimax-api.md)\n- [Azure 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