{
  "data": {
    "a": {
      "slug": "amazon-nova-embeddings",
      "name": "Amazon Nova Multimodal Embeddings",
      "vendor": "Amazon Web Services",
      "vendorUrl": "https://aws.amazon.com/nova/",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Amazon Nova Multimodal Embeddings is an AWS model on Amazon Bedrock that turns text, images, document images, video and audio into vectors in one space, at 256, 384, 1024 or 3072 dimensions, through synchronous and asynchronous calls.",
      "url": "https://www.anchorterminal.com/tools/amazon-nova-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json",
      "license": "Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://bedrock-runtime.us-east-1.amazonaws.com",
      "packages": [
        {
          "registry": "pypi",
          "name": "boto3"
        },
        {
          "registry": "npm",
          "name": "@aws-sdk/client-bedrock-runtime"
        }
      ],
      "auth": "api-key",
      "authNotes": "AWS Signature Version 4 with IAM credentials or a role, or an Amazon Bedrock API key sent as a bearer token (`AWS_BEARER_TOKEN_BEDROCK`). Short-term keys last up to 12 hours and inherit the caller's IAM permissions. Long-term keys create an IAM user and AWS recommends them for exploration only. Access is self-serve once a person has an AWS account, and asynchronous calls also need write access to an S3 bucket (https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys.html).",
      "pricing": "usage",
      "pricingNotes": "On demand in US East (N. Virginia), text input is $0.135 per million tokens, a standard image $0.00006, a document image $0.0006, video $0.0007 a second and audio $0.00014 a second. Batch is $0.0675 per million text tokens, with lower media rates. GovCloud prices are 20 per cent higher. There is no model-specific free tier. New AWS accounts can receive up to $200 in Free Tier credits, which lets an agent's owner start without a contract (https://aws.amazon.com/bedrock/pricing/).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the Nova guide, the Bedrock model card or the pricing page (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": 18066100,
        "pypiWeekly": 573748207,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.aws.amazon.com/nova/latest/userguide/nova-embeddings.html",
      "llmsTxt": "https://docs.aws.amazon.com/nova/latest/userguide/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal"
      ],
      "tags": [
        "official",
        "hosted",
        "usage-priced",
        "closed-source",
        "python",
        "typescript",
        "llms-txt",
        "batch",
        "async-jobs",
        "enterprise"
      ],
      "lastRelease": "2025-10-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 75,
        "grade": "BB",
        "agentReady": true,
        "rank": 59,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 1,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 78,
          "maintenance": 50,
          "payments": 30,
          "reliability": 95,
          "schema": 76,
          "security": 91,
          "transparency": 79
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.",
        "bestFor": "Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.",
        "strengths": [
          "Text, images, document images, video and audio share one vector space, with four output sizes from 256 to 3072",
          "`embeddingPurpose` has nine documented values, with separate settings for indexing and for each retrieval type",
          "Published quotas of 2,000 requests a minute and 30 concurrent asynchronous jobs per Region",
          "Bedrock stores no model inputs or outputs by default, and this model is not on the abuse-detection retention list",
          "The asynchronous API segments long text, audio and video itself and writes one embedding per segment to S3"
        ],
        "weaknesses": [
          "In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile",
          "A synchronous request embeds one item, with at most 8,192 characters of inline text or 30 seconds of audio or video",
          "The Bedrock model card marks Invoke as unsupported while the Nova guide documents `InvokeModel` for synchronous calls",
          "No dated change to the model was found after its launch on 28 October 2025",
          "Both quotas are marked not adjustable through Service Quotas"
        ],
        "agentNotes": [
          "Call `bedrock-runtime` in us-east-1 with model ID `amazon.nova-2-multimodal-embeddings-v1:0`. No other commercial Region serves it",
          "Index with `embeddingPurpose` `GENERIC_INDEX`, then embed queries with the retrieval value that matches the index, such as `TEXT_RETRIEVAL` or `GENERIC_RETRIEVAL`",
          "Always send `truncationMode` with text. It is required, and `NONE` fails the request when the text is too long",
          "Use `StartAsyncInvoke` with an S3 output bucket for anything over 30 seconds or 8,192 characters, and pass `clientRequestToken` so a retry doesn't start a second job",
          "Keep one `embeddingDimension` per index. The default is 3072"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 75
          }
        ],
        "editorialScores": {
          "ergonomics": 78,
          "maintenance": 50,
          "payments": 30,
          "reliability": 95,
          "schema": 76,
          "security": 91,
          "transparency": 69
        },
        "provenanceScore": 88
      },
      "connect": {
        "install": "pip install boto3"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/amazon-nova-embeddings"
      },
      "sameCompany": [
        "amazon-bedrock-guardrails",
        "amazon-transcribe",
        "amazon-polly",
        "agentcore-memory",
        "agentcore-identity",
        "aws-secrets-manager",
        "aws-mcp-servers",
        "amazon-ses",
        "amazon-location",
        "amazon-translate",
        "amazon-ads-api"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Text input, on demand",
          "unit": "1m-tokens",
          "usd": 0.135,
          "note": "US East (N. Virginia)"
        },
        {
          "item": "Text input, batch",
          "unit": "1m-tokens",
          "usd": 0.0675
        },
        {
          "item": "Standard image input",
          "unit": "image",
          "usd": 0.00006,
          "note": "Batch $0.00003"
        },
        {
          "item": "Document image input",
          "unit": "image",
          "usd": 0.0006,
          "note": "Batch $0.00048"
        },
        {
          "item": "Video input",
          "unit": "video-second",
          "usd": 0.0007,
          "note": "Batch $0.00056"
        },
        {
          "item": "Audio input",
          "unit": "audio-minute",
          "usd": 0.0084,
          "note": "$0.00014 a second. Batch $0.000112 a second"
        }
      ],
      "provenance": {
        "legalEntity": "Amazon Web Services, Inc.",
        "domain": "amazon.com",
        "domainRegistered": "1994-11-01",
        "domainNote": "The endpoint is on amazonaws.com, an AWS domain registered on 2005-08-18. The security.txt on aws.amazon.com passed its Expires date on 2026-09-24.",
        "endpointOnVendorDomain": true,
        "terms": "https://aws.amazon.com/service-terms/",
        "privacy": "https://aws.amazon.com/privacy/",
        "statusPage": "https://health.aws.amazon.com/health/status",
        "changelog": "https://docs.aws.amazon.com/bedrock/latest/userguide/doc-history.html",
        "securityTxt": "expired",
        "checked": "2026-10-08",
        "notes": [
          "The AWS Service Terms were last updated on 1 October 2026. Section 50.12 covers Amazon Bedrock, and Bedrock is not among the services section 50.3 lists for use of content to improve AWS services.",
          "RDAP gives 1994-11-01 for amazon.com and 2005-08-18 for amazonaws.com.",
          "aws.amazon.com/.well-known/security.txt has Contact and Policy fields. Its Expires value is 2026-09-24T16:25:03Z, which had passed on 2026-10-08.",
          "The pricing page draws its tables by script. Prices were read from the price feed the page loads, for US East (N. Virginia).",
          "The legal entity is as the existing AWS listings record it. The AWS Customer Agreement and the privacy notice were not re-read in this run."
        ],
        "score": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.json",
      "live": {
        "slug": "amazon-nova-embeddings",
        "probe": {
          "target": "https://bedrock-runtime.us-east-1.amazonaws.com",
          "method": "get",
          "lastAt": "2026-10-09T11:46:21.229746191Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 249,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 256,
          "p95ms24h": 279,
          "samples24h": 44,
          "samples30d": 44,
          "days": [
            {
              "date": "2026-10-09",
              "probes": 44,
              "ok": 44
            }
          ]
        },
        "updatedAt": "2026-10-09T11:46:21.229746191Z"
      }
    },
    "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing.",
    "b": {
      "slug": "mistral-embeddings",
      "name": "Mistral Embed and Codestral Embed",
      "vendor": "Mistral AI",
      "vendorUrl": "https://mistral.ai",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Mistral's API for generating text and code embeddings.",
      "url": "https://www.anchorterminal.com/tools/mistral-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json",
      "repo": "https://github.com/mistralai/client-python",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.mistral.ai/v1/embeddings",
      "packages": [
        {
          "registry": "pypi",
          "name": "mistralai"
        },
        {
          "registry": "npm",
          "name": "@mistralai/mistralai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a key from La Plateforme. Same key as the chat models. Regional EU and US endpoints are opt-in at 1.1 times the price.",
      "pricing": "freemium",
      "pricingNotes": "mistral-embed $0.10 and codestral-embed $0.15 per million input tokens (https://mistral.ai/pricing/api/). Batch processing at half price, regional endpoints 1.1x. The free Experiment tier needs a phone number, no card, and its data may be used for training (https://docs.mistral.ai/admin/user-management-finops/tier).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 769,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://docs.mistral.ai/capabilities/embeddings/overview",
      "llmsTxt": "https://docs.mistral.ai/llms.txt",
      "openapi": "https://docs.mistral.ai/openapi.yaml",
      "capabilities": [
        "embed.text",
        "embed.code"
      ],
      "tags": [
        "official",
        "hosted",
        "freemium",
        "free-tier",
        "eu",
        "openapi",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2025-05-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.9,
        "grade": "C",
        "agentReady": false,
        "rank": 535,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 8,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 78,
          "maintenance": 40,
          "payments": 40,
          "reliability": 38,
          "schema": 89,
          "security": 45,
          "transparency": 78
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.",
        "bestFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.",
        "strengths": [
          "EU and US regional endpoints and a French legal entity",
          "codestral-embed with up to 3072 dimensions, first-n truncation and int8 or binary output",
          "OpenAPI document and llms.txt for the whole API",
          "Free Experiment tier with no card, and batch at half price",
          "Same key, billing and SDKs as Mistral's chat models"
        ],
        "weaknesses": [
          "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026",
          "8k context on both models, and text or code only",
          "mistral-embed dates from December 2023 with fixed 1024-dimension float output, and nothing new since May 2025",
          "No reranker, no published rate limits and no language list for the embedding models",
          "Free-tier data may be used for training"
        ],
        "agentNotes": [
          "Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options",
          "Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else",
          "Keep chunks under 8k tokens. There's no long-context embedding model on this API",
          "Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026",
          "Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 3.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.9
          }
        ],
        "editorialScores": {
          "ergonomics": 78,
          "maintenance": 40,
          "payments": 40,
          "reliability": 38,
          "schema": 89,
          "security": 45,
          "transparency": 65
        },
        "provenanceScore": 91
      },
      "connect": {
        "install": "pip install mistralai   # or: npm i @mistralai/mistralai",
        "http": "curl -X POST https://api.mistral.ai/v1/embeddings \\\n  -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"codestral-embed\",\"input\":[\"def two_sum(nums, target): ...\"],\"output_dimension\":512,\"output_dtype\":\"int8\"}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/mistral-embeddings"
      },
      "sameCompany": [
        "mistral-api",
        "mistral-moderation",
        "mistral-voxtral-transcribe",
        "mistral-ocr"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "mistral-embed",
          "unit": "1m-tokens",
          "usd": 0.1
        },
        {
          "item": "codestral-embed",
          "unit": "1m-tokens",
          "usd": 0.15
        }
      ],
      "provenance": {
        "legalEntity": "Mistral AI (RCS Paris 952 418 325)",
        "domain": "mistral.ai",
        "domainRegistered": "2019-05-15",
        "endpointOnVendorDomain": true,
        "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service",
        "privacy": "https://legal.mistral.ai/terms/privacy-policy",
        "statusPage": "https://status.mistral.ai",
        "changelog": "https://docs.mistral.ai/resources/changelogs",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Same account, terms and data handling as the Mistral AI API listing. The embedding docs and the public docs repository were read on 2026-09-30; the legal documents and security.txt are as checked for that listing.",
          "The model catalogue in the docs repository (mistralai/platform-docs-public) is the source for context length, release dates and prices."
        ],
        "score": 91
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json",
      "live": {
        "slug": "mistral-embeddings",
        "probe": {
          "target": "https://api.mistral.ai/v1/embeddings",
          "method": "get",
          "lastAt": "2026-10-09T11:46:34.61700632Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 58,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 58,
          "p95ms24h": 103,
          "samples24h": 259,
          "samples30d": 2109,
          "days": [
            {
              "date": "2026-10-01",
              "probes": 109,
              "ok": 109
            },
            {
              "date": "2026-10-02",
              "probes": 248,
              "ok": 248
            },
            {
              "date": "2026-10-03",
              "probes": 271,
              "ok": 271
            },
            {
              "date": "2026-10-04",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-05",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-06",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-07",
              "probes": 272,
              "ok": 272
            },
            {
              "date": "2026-10-08",
              "probes": 268,
              "ok": 268
            },
            {
              "date": "2026-10-09",
              "probes": 125,
              "ok": 125
            }
          ]
        },
        "vendorStatus": {
          "page": "https://status.mistral.ai",
          "indicator": "unknown",
          "summary": "no machine-readable status found",
          "checkedAt": "2026-10-08T19:38:47.713113131Z"
        },
        "versions": [
          {
            "registry": "github",
            "name": "mistralai/client-python",
            "version": "v3.1.0",
            "released": "2026-10-06",
            "seenAt": "2026-10-08T16:21:17.2631398Z"
          },
          {
            "registry": "npm",
            "name": "@mistralai/mistralai",
            "version": "2.7.0",
            "seenAt": "2026-10-08T16:21:16.999265692Z"
          },
          {
            "registry": "pypi",
            "name": "mistralai",
            "version": "3.1.0",
            "released": "2026-10-06",
            "seenAt": "2026-10-08T16:21:16.88093713Z"
          }
        ],
        "githubStars": 773,
        "npmWeekly": 9359288,
        "pypiWeekly": 3274123,
        "securityTxt": {
          "url": "https://mistral.ai/.well-known/security.txt",
          "state": "valid",
          "expires": "2027-05-05T23:59:59.000Z",
          "checkedAt": "2026-10-08T15:38:55.944328005Z"
        },
        "llmsTxt": {
          "url": "https://docs.mistral.ai/llms.txt",
          "ok": true,
          "status": 200,
          "checkedAt": "2026-10-08T14:00:39.19683216Z"
        },
        "domain": {
          "domain": "mistral.ai",
          "registered": "2019-05-15",
          "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai",
          "checkedAt": "2026-10-04T13:08:59.683466691Z"
        },
        "updatedAt": "2026-10-09T11:46:34.61700632Z"
      }
    },
    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
      },
      {
        "a": "Amazon Web Services",
        "b": "Mistral AI",
        "name": "Vendor"
      },
      {
        "a": "https://bedrock-runtime.us-east-1.amazonaws.com",
        "b": "https://api.mistral.ai/v1/embeddings",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Pay per use",
        "b": "Freemium",
        "name": "Pricing"
      },
      {
        "a": "$0.0675 per 1M tokens",
        "b": "not published",
        "name": "Price for embed text"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0",
        "b": "Apache-2.0 (SDK)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2025-10-28",
        "b": "2025-05-28",
        "name": "Last release"
      },
      {
        "a": "2026-10-01",
        "b": "2026-09-25",
        "name": "Terms last updated"
      },
      {
        "a": "2026-05-18",
        "b": "2026-09-03",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "yes, with an opt-out",
        "name": "Customer content may train models"
      },
      {
        "a": "yes",
        "b": "not found in the text",
        "name": "Terms restrict automated access"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "not found in the text",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "18.1M npm/wk, 573.7M PyPI/wk",
        "b": "769 stars",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "3.5/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing.",
        "question": "Which is better for AI agents, Amazon Nova Multimodal Embeddings or Mistral Embed and Codestral Embed?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Amazon Nova Multimodal Embeddings and Mistral Embed and Codestral Embed need an API key?"
      },
      {
        "answer": "Yes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Mistral Embed and Codestral Embed at https://api.mistral.ai/v1/embeddings.",
        "question": "Can an agent call Amazon Nova Multimodal Embeddings and Mistral Embed and Codestral Embed without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 95 against 38",
          "Security \u0026 auth, 91 against 45",
          "Maintenance \u0026 community, 50 against 40"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.",
        "slug": "amazon-nova-embeddings",
        "watchFor": "In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile"
      },
      {
        "aheadOn": [
          "Schema \u0026 documentation, 89 against 76",
          "Payments \u0026 pricing, 40 against 30"
        ],
        "also": null,
        "goodFor": "EU data residency, Mistral-only stacks and code retrieval with small vectors.",
        "slug": "mistral-embeddings",
        "watchFor": "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026"
      }
    ],
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        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.json",
        "title": "Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding.json",
        "title": "Amazon Nova Multimodal Embeddings vs Gemini Embedding",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed.json",
        "title": "Amazon Nova Multimodal Embeddings vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.json",
        "title": "Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy.json",
        "title": "Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.json",
        "title": "Cohere Embed and Rerank vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.json",
        "title": "Gemini Embedding vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.json",
        "title": "Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.json",
        "title": "Mistral Embed and Codestral Embed vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.json",
        "title": "Mistral Embed and Codestral Embed vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.json",
        "title": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.json",
        "title": "Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy"
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    "scores": [
      {
        "amazon-nova-embeddings": 95,
        "by": 57,
        "edge": "amazon-nova-embeddings",
        "key": "reliability",
        "mistral-embeddings": 38,
        "name": "Reliability",
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 76,
        "by": 13,
        "edge": "mistral-embeddings",
        "key": "schema",
        "mistral-embeddings": 89,
        "name": "Schema \u0026 documentation",
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 78,
        "by": 0,
        "edge": "",
        "key": "ergonomics",
        "mistral-embeddings": 78,
        "name": "Agent ergonomics",
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 91,
        "by": 46,
        "edge": "amazon-nova-embeddings",
        "key": "security",
        "mistral-embeddings": 45,
        "name": "Security \u0026 auth",
        "weight": 14
      },
      {
        "amazon-nova-embeddings": 30,
        "by": 10,
        "edge": "mistral-embeddings",
        "key": "payments",
        "mistral-embeddings": 40,
        "name": "Payments \u0026 pricing",
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 50,
        "by": 10,
        "edge": "amazon-nova-embeddings",
        "key": "maintenance",
        "mistral-embeddings": 40,
        "name": "Maintenance \u0026 community",
        "weight": 7
      },
      {
        "amazon-nova-embeddings": 79,
        "by": 1,
        "edge": "amazon-nova-embeddings",
        "key": "transparency",
        "mistral-embeddings": 78,
        "name": "Transparency \u0026 trust",
        "weight": 7
      }
    ],
    "summary": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing. Both do embed text.",
    "verdicts": {
      "amazon-nova-embeddings": "One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.",
      "mistral-embeddings": "EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026."
    }
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  "links": {
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    "html": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings",
    "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
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  "markdown": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing. Both do embed text.\n\n- Amazon Nova Multimodal Embeddings: grade BB, 75/100, rank #59 of 842. Markdown https://www.anchorterminal.com/tools/amazon-nova-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json\n- Mistral Embed and Codestral Embed: grade C, 57.9/100, rank #535 of 842. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\n\n## Which one, for what\n\n### Amazon Nova Multimodal Embeddings (BB)\n\nGood for: Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.\n\nAhead on:\n- Reliability, 95 against 38\n- Security \u0026 auth, 91 against 45\n- Maintenance \u0026 community, 50 against 40\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile\n\n### Mistral Embed and Codestral Embed (C)\n\nGood for: EU data residency, Mistral-only stacks and code retrieval with small vectors.\n\nAhead on:\n- Schema \u0026 documentation, 89 against 76\n- Payments \u0026 pricing, 40 against 30\n\nWatch for: Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026\n\n\n## Score by category\n\n| Category | Weight | Amazon Nova Multimodal Embeddings | Mistral Embed and Codestral Embed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 38 | Amazon Nova Multimodal Embeddings +57 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 76 | 89 | Mistral Embed and Codestral Embed +13 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 78 | even |\n| Security \u0026 auth | 14% (17.5 this run) | 91 | 45 | Amazon Nova Multimodal Embeddings +46 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Mistral Embed and Codestral Embed +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 50 | 40 | Amazon Nova Multimodal Embeddings +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 78 | Amazon Nova Multimodal Embeddings +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75 · BB** | **57.9 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Nova Multimodal Embeddings | Mistral Embed and Codestral Embed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Mistral AI |\n| Hosted endpoint | `https://bedrock-runtime.us-east-1.amazonaws.com` | `https://api.mistral.ai/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\n| Price for embed text | $0.0675 per 1M tokens | not published |\n| x402 | no | no |\n| Licence | Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0 | Apache-2.0 (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2025-10-28 | 2025-05-28 |\n| Terms last updated | 2026-10-01 | 2026-09-25 |\n| Privacy policy last updated | 2026-05-18 | 2026-09-03 |\n| Customer content may train models | yes, with an opt-out | yes, with an opt-out |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | yes | yes |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | 18.1M npm/wk, 573.7M PyPI/wk | 769 stars |\n| Agent reviews | none | 3.5/5 (2) |\n\n## Verdicts\n\n**Amazon Nova Multimodal Embeddings.** One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.\n\n**Mistral Embed and Codestral Embed.** EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.\n\n## Before you call either\n\n### Amazon Nova Multimodal Embeddings\n\n1. Call `bedrock-runtime` in us-east-1 with model ID `amazon.nova-2-multimodal-embeddings-v1:0`. No other commercial Region serves it\n2. Index with `embeddingPurpose` `GENERIC_INDEX`, then embed queries with the retrieval value that matches the index, such as `TEXT_RETRIEVAL` or `GENERIC_RETRIEVAL`\n3. Always send `truncationMode` with text. It is required, and `NONE` fails the request when the text is too long\n4. Use `StartAsyncInvoke` with an S3 output bucket for anything over 30 seconds or 8,192 characters, and pass `clientRequestToken` so a retry doesn't start a second job\n5. Keep one `embeddingDimension` per index. The default is 3072\n\n### Mistral Embed and Codestral Embed\n\n1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options\n2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else\n3. Keep chunks under 8k tokens. There's no long-context embedding model on this API\n4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026\n5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors\n\n## Questions\n\n### Which is better for AI agents, Amazon Nova Multimodal Embeddings or Mistral Embed and Codestral Embed?\n\nAmazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing.\n\n### Do Amazon Nova Multimodal Embeddings and Mistral Embed and Codestral Embed need an API key?\n\nBoth need an API key.\n\n### Can an agent call Amazon Nova Multimodal Embeddings and Mistral Embed and Codestral Embed without installing anything?\n\nYes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Mistral Embed and Codestral Embed at https://api.mistral.ai/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-nova-embeddings\", \"b\": \"mistral-embeddings\"}`. From a terminal: `anchor compare amazon-nova-embeddings mistral-embeddings`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json and https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\n\n## Other comparisons with Amazon Nova Multimodal Embeddings or Mistral Embed and Codestral Embed\n\n- [Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.md)\n- [Amazon Nova Multimodal Embeddings vs Gemini Embedding](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding.md)\n- [Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs Nomic Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed.md)\n- [Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md)\n- [Amazon Nova Multimodal Embeddings vs OpenAI embeddings](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.md)\n- [Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy.md)\n- [Cohere Embed and Rerank vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.md)\n- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)\n- [Mistral Embed and Codestral Embed vs Nomic Embed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nomic-embed.md)\n- [Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.md)\n",
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      {
        "name": "Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed",
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    "description": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Mistral Embed and Codestral Embed's 57.9 (C), and leads in 4 of 7 scored categories. Mistral Embed and Codestral Embed leads on schema \u0026 documentation and payments \u0026 pricing. Both do embed text. Category…",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed",
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    "updated": "2026-10-09",
    "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings"
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