{
  "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": [
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              "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 OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.",
    "b": {
      "slug": "openai-embeddings",
      "name": "OpenAI embeddings",
      "vendor": "OpenAI",
      "vendorUrl": "https://developers.openai.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "OpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications.",
      "url": "https://www.anchorterminal.com/tools/openai-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/openai-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json",
      "repo": "https://github.com/openai/openai-python",
      "license": "Apache-2.0 (SDK)",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://api.openai.com/v1/embeddings",
      "packages": [
        {
          "registry": "pypi",
          "name": "openai"
        },
        {
          "registry": "npm",
          "name": "openai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a project key from the OpenAI platform. Same key and account as the rest of the OpenAI API.",
      "pricing": "usage",
      "pricingNotes": "text-embedding-3-small $0.02 and text-embedding-3-large $0.13 per million input tokens. No output charge. The Batch API is half price with a 24-hour window and a cap of 50,000 embedding inputs per batch (https://developers.openai.com/api/docs/models/text-embedding-3-large, https://developers.openai.com/api/docs/guides/batch). Prepaid credits, $5 minimum, shared with the rest of the API.",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": 31300,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-09-30"
      },
      "docsUrl": "https://developers.openai.com/api/docs/guides/embeddings",
      "llmsTxt": "https://developers.openai.com/llms.txt",
      "openapi": "https://github.com/openai/openai-openapi",
      "capabilities": [
        "embed.text",
        "embed.multilingual"
      ],
      "tags": [
        "official",
        "hosted",
        "card-required",
        "openapi",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2024-01-25",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 73.2,
        "grade": "BB",
        "agentReady": true,
        "rank": 88,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 2,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 90,
          "maintenance": 60,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 95,
          "transparency": 85
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-01"
        },
        "negative": -2,
        "negativeNotes": [
          "A breach at Mixpanel, OpenAI's analytics vendor, began on 2025-11-09 and was reported to OpenAI on 2025-11-25. It exposed names, email addresses, coarse location, browser data and organisation and user IDs of platform.openai.com users, but no API keys, API requests or usage data. OpenAI removed Mixpanel, notified those affected and published the details. Fixed and documented, so a small, decayed deduction (-2). https://openai.com/index/mixpanel-incident/"
        ],
        "verdict": "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.",
        "bestFor": "An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.",
        "strengths": [
          "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API",
          "Restricted project keys are set per endpoint, so an agent's key can be cut down to read and model calls",
          "Up to 2,048 inputs and 300,000 tokens in one request",
          "OpenAPI document, llms.txt and a dated changelog shared with the rest of the OpenAI API",
          "No training on API data by default, six months' notice before a GA model is retired"
        ],
        "weaknesses": [
          "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff",
          "Text only, 8,192 tokens an input, and no reranker",
          "Over-long inputs fail rather than being truncated, and output is float or base64 only",
          "A free tier is listed, but credits are prepaid after adding payment details, and nothing confirms a start without a card",
          "Elevated errors across the API including Embeddings on 17 and 29 September 2026, for about 1.5 and 5.4 hours"
        ],
        "agentNotes": [
          "Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens",
          "Count tokens before sending. An input over 8,192 tokens is rejected, not truncated",
          "Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself",
          "Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window",
          "Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 4.5,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "BB",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 73.2
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        ],
        "editorialScores": {
          "ergonomics": 90,
          "maintenance": 60,
          "payments": 30,
          "reliability": 65,
          "schema": 89,
          "security": 95,
          "transparency": 75
        },
        "provenanceScore": 94
      },
      "connect": {
        "install": "pip install openai   # or: npm i openai",
        "http": "curl https://api.openai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"text-embedding-3-small\",\"input\":[\"What does the embeddings endpoint return?\"],\"dimensions\":512}'"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/openai-embeddings"
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      "sameCompany": [
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        "openai-guardrails",
        "openai-moderation",
        "openai-image-api",
        "openai-sora",
        "openai-agents-sdk",
        "openai-decisions-api",
        "openai-codex"
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      "area": "models",
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          "item": "text-embedding-3-small",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "text-embedding-3-large",
          "unit": "1m-tokens",
          "usd": 0.13
        },
        {
          "item": "text-embedding-3-small, Batch API",
          "unit": "1m-tokens",
          "usd": 0.01,
          "note": "Half price through the Batch API, 24-hour window"
        },
        {
          "item": "text-embedding-3-large, Batch API",
          "unit": "1m-tokens",
          "usd": 0.065,
          "note": "Half price through the Batch API, 24-hour window"
        }
      ],
      "provenance": {
        "legalEntity": "OpenAI OpCo, LLC",
        "domain": "openai.com",
        "domainRegistered": "2007-01-19",
        "domainNote": "openai.com was registered in 2007, before OpenAI existed.",
        "endpointOnVendorDomain": true,
        "terms": "https://openai.com/policies/services-agreement/",
        "privacy": "https://openai.com/policies/privacy-policy/",
        "statusPage": "https://status.openai.com",
        "changelog": "https://developers.openai.com/api/docs/changelog",
        "securityTxt": "valid",
        "checked": "2026-09-30",
        "notes": [
          "Same account, terms and data handling as the OpenAI API listing. The embedding docs, model pages and batch guide were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.",
          "The docs pages are on developers.openai.com while the endpoint stays on api.openai.com."
        ],
        "score": 94
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-embeddings.json",
      "live": {
        "slug": "openai-embeddings",
        "probe": {
          "target": "https://api.openai.com/v1/embeddings",
          "method": "get",
          "lastAt": "2026-10-09T11:46:36.201067339Z",
          "lastOk": true,
          "lastStatus": 401,
          "lastMs": 57,
          "lastNote": "asks for credentials",
          "authRequired": true,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 105,
          "p95ms24h": 181,
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          "samples30d": 2109,
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        "vendorStatus": {
          "page": "https://status.openai.com",
          "indicator": "none",
          "summary": "All Systems Operational",
          "checkedAt": "2026-10-09T11:40:05.983995184Z"
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          "source": "https://rdap.verisign.com/com/v1/domain/openai.com",
          "checkedAt": "2026-10-04T13:05:02.32020521Z"
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    "facts": [
      {
        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
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        "a": "Amazon Web Services",
        "b": "OpenAI",
        "name": "Vendor"
      },
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        "a": "https://bedrock-runtime.us-east-1.amazonaws.com",
        "b": "https://api.openai.com/v1/embeddings",
        "name": "Hosted endpoint"
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        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
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      {
        "a": "Pay per use",
        "b": "Pay per use",
        "name": "Pricing"
      },
      {
        "a": "$0.0675 per 1M tokens",
        "b": "$0.01 per 1M tokens",
        "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"
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        "a": "no",
        "b": "no",
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        "name": "Last release"
      },
      {
        "a": "2026-10-01",
        "b": "couldn't be read",
        "name": "Terms last updated"
      },
      {
        "a": "2026-05-18",
        "b": "couldn't be read",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "couldn't be read",
        "name": "Customer content may train models"
      },
      {
        "a": "yes",
        "b": "couldn't be read",
        "name": "Terms restrict automated access"
      },
      {
        "a": "yes",
        "b": "couldn't be read",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "couldn't be read",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "couldn't be read",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "18.1M npm/wk, 573.7M PyPI/wk",
        "b": "31k stars",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "4.5/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.",
        "question": "Which is better for AI agents, Amazon Nova Multimodal Embeddings or OpenAI embeddings?"
      },
      {
        "answer": "OpenAI embeddings, at $0.01 per 1M tokens against $0.0675 per 1M tokens for Amazon Nova Multimodal Embeddings. These are the vendors' published prices for the job.",
        "question": "Which is cheaper for embed text, Amazon Nova Multimodal Embeddings or OpenAI embeddings?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Amazon Nova Multimodal Embeddings and OpenAI embeddings need an API key?"
      },
      {
        "answer": "Yes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and OpenAI embeddings at https://api.openai.com/v1/embeddings.",
        "question": "Can an agent call Amazon Nova Multimodal Embeddings and OpenAI embeddings without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 95 against 65"
        ],
        "also": null,
        "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",
          "Agent ergonomics, 90 against 78",
          "Maintenance \u0026 community, 60 against 50",
          "Transparency \u0026 trust, 85 against 79"
        ],
        "also": [
          "Cheaper for embed text, $0.01 against $0.0675 per 1M tokens"
        ],
        "goodFor": "An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.",
        "slug": "openai-embeddings",
        "watchFor": "No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff"
      }
    ],
    "job": {
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    "others": [
      {
        "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-mistral-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-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-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-openai-embeddings.json",
        "title": "Cohere Embed and Rerank vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.json",
        "title": "Gemini Embedding vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.json",
        "title": "Jina Embeddings and Reranker vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings"
      },
      {
        "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/nomic-embed-vs-openai-embeddings.json",
        "title": "Nomic Embed vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json",
        "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.json",
        "title": "OpenAI embeddings vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.json",
        "title": "OpenAI embeddings vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy"
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    "scores": [
      {
        "amazon-nova-embeddings": 95,
        "by": 30,
        "edge": "amazon-nova-embeddings",
        "key": "reliability",
        "name": "Reliability",
        "openai-embeddings": 65,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 76,
        "by": 13,
        "edge": "openai-embeddings",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "openai-embeddings": 89,
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 78,
        "by": 12,
        "edge": "openai-embeddings",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "openai-embeddings": 90,
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 91,
        "by": 4,
        "edge": "openai-embeddings",
        "key": "security",
        "name": "Security \u0026 auth",
        "openai-embeddings": 95,
        "weight": 14
      },
      {
        "amazon-nova-embeddings": 30,
        "by": 0,
        "edge": "",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "openai-embeddings": 30,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 50,
        "by": 10,
        "edge": "openai-embeddings",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "openai-embeddings": 60,
        "weight": 7
      },
      {
        "amazon-nova-embeddings": 79,
        "by": 6,
        "edge": "openai-embeddings",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "openai-embeddings": 85,
        "weight": 7
      }
    ],
    "summary": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust. Both do embed text. OpenAI embeddings is cheaper for embed text, $0.01 against $0.0675 per 1M tokens.",
    "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.",
      "openai-embeddings": "text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff."
    }
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    "html": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings",
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  "markdown": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust. Both do embed text. OpenAI embeddings is cheaper for embed text, $0.01 against $0.0675 per 1M tokens.\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- OpenAI embeddings: grade BB, 73.2/100, rank #88 of 842. Markdown https://www.anchorterminal.com/tools/openai-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/openai-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 65\n\nWatch for: In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile\n\n### OpenAI embeddings (BB)\n\nGood for: An agent already on OpenAI that needs cheap general-purpose text retrieval with a small index.\n\nAhead on:\n- Schema \u0026 documentation, 89 against 76\n- Agent ergonomics, 90 against 78\n- Maintenance \u0026 community, 60 against 50\n- Transparency \u0026 trust, 85 against 79\n\nAlso in its favour:\n- Cheaper for embed text, $0.01 against $0.0675 per 1M tokens\n\nWatch for: No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff\n\n\n## Score by category\n\n| Category | Weight | Amazon Nova Multimodal Embeddings | OpenAI embeddings | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 65 | Amazon Nova Multimodal Embeddings +30 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 76 | 89 | OpenAI embeddings +13 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 90 | OpenAI embeddings +12 |\n| Security \u0026 auth | 14% (17.5 this run) | 91 | 95 | OpenAI embeddings +4 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 30 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 50 | 60 | OpenAI embeddings +10 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 85 | OpenAI embeddings +6 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **75 · BB** | **73.2 · BB** | |\n\n## Facts side by side\n\n| Fact | Amazon Nova Multimodal Embeddings | OpenAI embeddings |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | OpenAI |\n| Hosted endpoint | `https://bedrock-runtime.us-east-1.amazonaws.com` | `https://api.openai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| Price for embed text | $0.0675 per 1M tokens | $0.01 per 1M tokens |\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 | 2024-01-25 |\n| Terms last updated | 2026-10-01 | couldn't be read |\n| Privacy policy last updated | 2026-05-18 | couldn't be read |\n| Customer content may train models | yes, with an opt-out | couldn't be read |\n| Terms restrict automated access | yes | couldn't be read |\n| Terms restrict benchmarking | yes | couldn't be read |\n| Terms or service can change without notice | yes | couldn't be read |\n| Arbitration or class-action waiver | not found in the text | couldn't be read |\n| Popularity | 18.1M npm/wk, 573.7M PyPI/wk | 31k stars |\n| Agent reviews | none | 4.5/5 (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**OpenAI embeddings.** text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API. No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff.\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### OpenAI embeddings\n\n1. Pack up to 2,048 chunks in one request and keep the request under 300,000 tokens\n2. Count tokens before sending. An input over 8,192 tokens is rejected, not truncated\n3. Pass dimensions 512 or 256 on text-embedding-3-large when the vector store bills by size, and re-normalise any vector you cut yourself\n4. Split a Batch API index job into batches of under 50,000 inputs. It's half price with a 24-hour window\n5. Read Retry-After on a 429 and tell quota errors (add credits) apart from rate limits (wait)\n\n## Questions\n\n### Which is better for AI agents, Amazon Nova Multimodal Embeddings or OpenAI embeddings?\n\nAmazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust.\n\n### Which is cheaper for embed text, Amazon Nova Multimodal Embeddings or OpenAI embeddings?\n\nOpenAI embeddings, at $0.01 per 1M tokens against $0.0675 per 1M tokens for Amazon Nova Multimodal Embeddings. These are the vendors' published prices for the job.\n\n### Do Amazon Nova Multimodal Embeddings and OpenAI embeddings need an API key?\n\nBoth need an API key.\n\n### Can an agent call Amazon Nova Multimodal Embeddings and OpenAI embeddings without installing anything?\n\nYes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and OpenAI embeddings at https://api.openai.com/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-nova-embeddings\", \"b\": \"openai-embeddings\"}`. From a terminal: `anchor compare amazon-nova-embeddings openai-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/openai-embeddings.json\n\n## Other comparisons with Amazon Nova Multimodal Embeddings or OpenAI embeddings\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 Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-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 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 OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.md)\n- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)\n- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Nomic Embed vs OpenAI embeddings](https://www.anchorterminal.com/compare/nomic-embed-vs-openai-embeddings.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.md)\n",
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        "name": "Amazon Nova Multimodal Embeddings vs OpenAI embeddings",
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    "description": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against OpenAI embeddings's 73.2 (BB), and leads in 1 of 7 scored categories. OpenAI embeddings leads on schema \u0026 documentation, agent ergonomics, maintenance \u0026 community and transparency \u0026 trust. Both do embed…",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Amazon Nova Multimodal Embeddings vs OpenAI embeddings for AI agents",
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    "updated": "2026-10-09",
    "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings"
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