{ "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 Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.", "b": { "slug": "cohere-embed", "name": "Cohere Embed and Rerank", "vendor": "Cohere", "vendorUrl": "https://cohere.com", "kind": "http-api", "category": "embeddings", "summary": "Cohere's Embed API turns text, images and mixed text-and-image inputs such as PDF pages into vectors with Embed 5 Pro and Fast, and its Rerank API reorders search results with Rerank 4.", "url": "https://www.anchorterminal.com/tools/cohere-embed", "markdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cohere-embed.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cohere-embed.json", "repo": "https://github.com/cohere-ai/cohere-python", "license": "MIT (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.cohere.com/v2/embed", "packages": [ { "registry": "pypi", "name": "cohere" }, { "registry": "npm", "name": "cohere-ai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a trial or production key from the dashboard. Trial keys are free, rate limited and not for commercial use. Production keys bill monthly.", "pricing": "freemium", "pricingNotes": "Embed 5 Pro $0.12 and Embed 5 Fast $0.08 per million text tokens (https://cohere.com/pricing), $0.40 per million image tokens on both (https://cohere.com/blog/embed-5). Rerank 4 Fast $2.00 and Rerank 4 Pro $2.50 per 1,000 searches. A search is one query with up to 100 documents, and a document over 500 tokens is split into chunks that each count as a document. Model Vault dedicated instances run $3 to $10 an hour or $2,000 to $6,500 a month (Embed 5 $3 to $5 an hour, Rerank 4 $5 to $10). Trial keys are free, need no card, and are capped at 1,000 calls a month. Bills issue monthly or at $250 outstanding (https://cohere.com/pricing).", "priceSummary": "$2 / 1k req", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 400, "npmWeekly": 555855, "pypiWeekly": 2593025, "asOf": "2026-09-30" }, "docsUrl": "https://docs.cohere.com/docs/embeddings", "llmsTxt": "https://docs.cohere.com/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "llms-txt", "python", "typescript", "enterprise", "closed-source" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 72.5, "grade": "BB", "agentReady": true, "rank": 100, "ranked": true, "rankOf": 842, "categoryRank": 3, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 87, "maintenance": 90, "payments": 40, "reliability": 73, "schema": 92, "security": 55, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-05" }, "negative": 0, "verdict": "Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.", "bestFor": "Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.", "strengths": [ "Embed 5 Pro and Fast share one embedding space, so a Pro index answers Fast queries", "128K context with six output sizes and int8, binary and base64 output", "Rerank 4 Pro and Fast with 32K context, top_n and published per-search prices", "Free trial keys at signup with no card", "Public OpenAPI file, llms.txt, Markdown docs and a dated changelog" ], "weaknesses": [ "Terms, training notice and security page disagree on whether API data trains models or goes to third parties", "A Google Cloud outage degraded embed and rerank for about four hours on 1 September 2026, and Embed 5 isn't yet a status component", "96 inputs a call, and input_type is required", "One unscoped key reaches every Cohere endpoint, including delete operations", "No security.txt and no SLA for self-serve use" ], "agentNotes": [ "Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it", "Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (the Python SDK merge drops types missing from the first response)", "Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. One query with up to 100 documents is one search, and a document over 500 tokens counts as several", "Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models", "Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 72.5 } ], "editorialScores": { "ergonomics": 87, "maintenance": 90, "payments": 40, "reliability": 73, "schema": 92, "security": 55, "transparency": 62 }, "provenanceScore": 81 }, "connect": { "install": "pip install cohere # or: npm i cohere-ai", "http": "curl -X POST https://api.cohere.com/v2/rerank \\\n -H \"Authorization: Bearer $COHERE_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"rerank-v4.0-fast\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Embed 5 Fast is $0.08 per million tokens.\",\"Toronto is in Ontario.\"],\"top_n\":1}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/cohere-embed" }, "sameCompany": [ "cohere-north" ], "area": "models", "unitPrices": [ { "item": "Embed 5 Pro", "unit": "1m-tokens", "usd": 0.12 }, { "item": "Embed 5 Fast", "unit": "1m-tokens", "usd": 0.08 }, { "item": "Embed 5 image input", "unit": "1m-tokens", "usd": 0.4, "note": "Pro and Fast, per the launch post" }, { "item": "Rerank 4 Fast", "unit": "1k-requests", "usd": 2, "note": "Per 1,000 searches, one query with up to 100 documents" }, { "item": "Rerank 4 Pro", "unit": "1k-requests", "usd": 2.5, "note": "Per 1,000 searches, one query with up to 100 documents" } ], "provenance": { "legalEntity": "Cohere Inc.", "domain": "cohere.com", "domainRegistered": "2000-03-07", "domainNote": "cohere.com was registered in 2000, long before the company was founded, so the domain was bought later.", "endpointOnVendorDomain": true, "terms": "https://cohere.com/terms-of-use", "privacy": "https://cohere.com/privacy", "statusPage": "https://status.cohere.com", "changelog": "https://docs.cohere.com/v2/changelog", "securityTxt": "none", "checked": "2026-09-30", "notes": [ "The privacy policy gives 171 John Street, Suite 200, Toronto, ON M5T 1X3. The terms are governed by Ontario law with Toronto courts.", "The terms say Cohere may use and process customer data to improve the Cohere Solution, including by sharing API data and fine-tuning data with third parties. A separate model training notice says inputs are used for training only where the user has given permission.", "Trial keys aren't meant for personal information. The privacy policy says to email privacy@cohere.com to delete anything sent by mistake.", "Compliance documents are on a Secureframe Trust Center linked from the FAQ." ], "score": 81 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-embed.json", "live": { "slug": "cohere-embed", "probe": { "target": "https://api.cohere.com/v2/embed", "method": "get", "lastAt": "2026-10-09T11:46:25.65762426Z", "lastOk": true, "lastStatus": 401, "lastMs": 125, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 142, "p95ms24h": 229, "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.cohere.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:38:41.238121495Z" }, "versions": [ { "registry": "github", "name": "cohere-ai/cohere-python", "version": "7.1.0", "released": "2026-08-26", "seenAt": "2026-10-08T16:06:15.111834364Z" }, { "registry": "npm", "name": "cohere-ai", "version": "8.1.0", "seenAt": "2026-10-08T16:06:11.612457837Z" }, { "registry": "pypi", "name": "cohere", "version": "7.2.0", "released": "2026-09-28", "seenAt": "2026-10-08T16:06:11.492608044Z" } ], "githubStars": 404, "npmWeekly": 555731, "pypiWeekly": 2518004, "securityTxt": { "url": "https://cohere.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-08T15:38:31.944177972Z" }, "llmsTxt": { "url": "https://docs.cohere.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:00:14.303467154Z" }, "domain": { "domain": "cohere.com", "registered": "2000-03-07", "source": "https://rdap.verisign.com/com/v1/domain/cohere.com", "checkedAt": "2026-10-04T13:10:19.711644168Z" }, "pages": [ { "url": "https://docs.cohere.com/v2/changelog", "kind": "changelog", "status": 304, "checkedAt": "2026-10-08T18:18:28.297174847Z", "changedAt": "2026-10-07T18:04:51.510283642Z", "fingerprint": "f82c6d389ba7" }, { "url": "https://cohere.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:16:30.031300821Z", "changedAt": "2026-10-08T18:16:30.031300821Z", "fingerprint": "be8236a5e9f1" }, { "url": "https://cohere.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-08T18:16:32.121838469Z", "changedAt": "2026-10-08T18:16:32.121838469Z", "fingerprint": "5ed49b0909fd" }, { "url": "https://cohere.com/terms-of-use", "kind": "terms", "status": 200, "checkedAt": "2026-10-08T18:16:34.209834686Z", "changedAt": "2026-10-08T18:16:34.209834686Z", "fingerprint": "01b4543073a0" } ], "updatedAt": "2026-10-09T11:46:25.65762426Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "Cohere", "name": "Vendor" }, { "a": "https://bedrock-runtime.us-east-1.amazonaws.com", "b": "https://api.cohere.com/v2/embed", "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": "MIT (SDK)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2025-10-28", "b": "2026-09-30", "name": "Last release" }, { "a": "2026-10-01", "b": "2022-09-07", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "2026-05-01", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "yes", "name": "Customer content may train models" }, { "a": "yes", "b": "yes", "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": "400 stars, 556k npm/wk, 2.6M PyPI/wk", "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 Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.", "question": "Which is better for AI agents, Amazon Nova Multimodal Embeddings or Cohere Embed and Rerank?" }, { "answer": "Both need an API key.", "question": "Do Amazon Nova Multimodal Embeddings and Cohere Embed and Rerank need an API key?" }, { "answer": "Yes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Cohere Embed and Rerank at https://api.cohere.com/v2/embed.", "question": "Can an agent call Amazon Nova Multimodal Embeddings and Cohere Embed and Rerank without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 73", "Security \u0026 auth, 91 against 55", "Transparency \u0026 trust, 79 against 72" ], "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, 92 against 76", "Agent ergonomics, 87 against 78", "Payments \u0026 pricing, 40 against 30", "Maintenance \u0026 community, 90 against 50" ], "also": [ "Free to start without a card" ], "goodFor": "Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.", "slug": "cohere-embed", "watchFor": "Terms, training notice and security page disagree on whether API data trains models or goes to third parties" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "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-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-gemini-embedding.json", "title": "Cohere Embed and Rerank vs Gemini Embedding", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.json", "title": "Cohere Embed and Rerank vs Jina Embeddings and Reranker", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings" }, { "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/cohere-embed-vs-nomic-embed.json", "title": "Cohere Embed and Rerank vs Nomic Embed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.json", "title": "Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever" }, { "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/cohere-embed-vs-voyage-ai.json", "title": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai" }, { "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.json", "title": "Cohere Embed and Rerank vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy" } ], "scores": [ { "amazon-nova-embeddings": 95, "by": 22, "cohere-embed": 73, "edge": "amazon-nova-embeddings", "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-nova-embeddings": 76, "by": 16, "cohere-embed": 92, "edge": "cohere-embed", "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "amazon-nova-embeddings": 78, "by": 9, "cohere-embed": 87, "edge": "cohere-embed", "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "amazon-nova-embeddings": 91, "by": 36, "cohere-embed": 55, "edge": "amazon-nova-embeddings", "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "amazon-nova-embeddings": 30, "by": 10, "cohere-embed": 40, "edge": "cohere-embed", "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-nova-embeddings": 50, "by": 40, "cohere-embed": 90, "edge": "cohere-embed", "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "amazon-nova-embeddings": 79, "by": 7, "cohere-embed": 72, "edge": "amazon-nova-embeddings", "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. 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.", "cohere-embed": "Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed", "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.md", "slim": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.min.md" }, "markdown": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. 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- Cohere Embed and Rerank: grade BB, 72.5/100, rank #100 of 842. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-embed.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 73\n- Security \u0026 auth, 91 against 55\n- Transparency \u0026 trust, 79 against 72\n\nWatch for: In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile\n\n### Cohere Embed and Rerank (BB)\n\nGood for: Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.\n\nAhead on:\n- Schema \u0026 documentation, 92 against 76\n- Agent ergonomics, 87 against 78\n- Payments \u0026 pricing, 40 against 30\n- Maintenance \u0026 community, 90 against 50\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Terms, training notice and security page disagree on whether API data trains models or goes to third parties\n\n\n## Score by category\n\n| Category | Weight | Amazon Nova Multimodal Embeddings | Cohere Embed and Rerank | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 73 | Amazon Nova Multimodal Embeddings +22 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 76 | 92 | Cohere Embed and Rerank +16 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 87 | Cohere Embed and Rerank +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 91 | 55 | Amazon Nova Multimodal Embeddings +36 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Cohere Embed and Rerank +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 50 | 90 | Cohere Embed and Rerank +40 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 72 | Amazon Nova Multimodal Embeddings +7 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75 · BB** | **72.5 · BB** | |\n\n## Facts side by side\n\n| Fact | Amazon Nova Multimodal Embeddings | Cohere Embed and Rerank |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Cohere |\n| Hosted endpoint | `https://bedrock-runtime.us-east-1.amazonaws.com` | `https://api.cohere.com/v2/embed` |\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 | MIT (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2025-10-28 | 2026-09-30 |\n| Terms last updated | 2026-10-01 | 2022-09-07 |\n| Privacy policy last updated | 2026-05-18 | 2026-05-01 |\n| Customer content may train models | yes, with an opt-out | yes |\n| Terms restrict automated access | yes | yes |\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 | 400 stars, 556k npm/wk, 2.6M PyPI/wk |\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**Cohere Embed and Rerank.** Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.\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### Cohere Embed and Rerank\n\n1. Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it\n2. Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (the Python SDK merge drops types missing from the first response)\n3. Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. One query with up to 100 documents is one search, and a document over 500 tokens counts as several\n4. Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models\n5. Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage\n\n## Questions\n\n### Which is better for AI agents, Amazon Nova Multimodal Embeddings or Cohere Embed and Rerank?\n\nAmazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.\n\n### Do Amazon Nova Multimodal Embeddings and Cohere Embed and Rerank need an API key?\n\nBoth need an API key.\n\n### Can an agent call Amazon Nova Multimodal Embeddings and Cohere Embed and Rerank without installing anything?\n\nYes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Cohere Embed and Rerank at https://api.cohere.com/v2/embed.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-nova-embeddings\", \"b\": \"cohere-embed\"}`. From a terminal: `anchor compare amazon-nova-embeddings cohere-embed`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json and https://www.anchorterminal.com/api/v1/tools/cohere-embed.json\n\n## Other comparisons with Amazon Nova Multimodal Embeddings or Cohere Embed and Rerank\n\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 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 Gemini Embedding](https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding.md)\n- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)\n- [Cohere Embed and Rerank vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-mistral-embeddings.md)\n- [Cohere Embed and Rerank vs Nomic Embed](https://www.anchorterminal.com/compare/cohere-embed-vs-nomic-embed.md)\n- [Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)\n- [Cohere Embed and Rerank vs OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.md)\n- [Cohere Embed and Rerank vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)\n- [Cohere Embed and Rerank vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank", "url": "" } ], "description": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Cohere Embed and Rerank's 72.5 (BB), and leads in 3 of 7 scored categories. Cohere Embed and Rerank leads on schema \u0026 documentation, agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. Both…", "facts": [ "Amazon Nova Multimodal Embeddings BB 75", "Cohere Embed and Rerank BB 72.5", "scores" ], "h1": "Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-nova-embeddings-vs-cohere-embed.png", "path": "/compare/amazon-nova-embeddings-vs-cohere-embed", "published": "2026-10-01", "section": "tools", "title": "Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed" }, "tokens": { "markdown": 2650, "slim": 730 }, "version": 1 }