{ "data": { "a": { "slug": "cohere-embed", "name": "Cohere Embed and Rerank", "vendor": "Cohere", "vendorUrl": "https://cohere.com", "kind": "http-api", "category": "embeddings", "summary": "Embed 5 (Pro and Fast, released 2026-09-30) embeds text, images and parsed PDFs at 128K context in 100+ languages, at $0.08 to $0.12 per million tokens.", "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, $0.40 per million image tokens on both (https://cohere.com/blog/embed-5). Rerank is billed per search, one query with up to 100 documents, and a document over 500 tokens is split into chunks that each count as a document. The per-search rate on the pricing page renders client-side and we couldn't read it. On Amazon Bedrock, Rerank 3.5 is $2.00 per 1,000 queries (https://aws.amazon.com/bedrock/pricing/). Model Vault dedicated instances run $3 to $10 an hour or $2,000 to $6,500 a month. 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": 69, "ranked": true, "rankOf": 452, "categoryRank": 2, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 87, "maintenance": 87, "payments": 35, "reliability": 83, "schema": 92, "security": 50, "transparency": 69 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.", "strengths": [ "Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page", "Embed 5 at 128K context with six output sizes and int8, binary and base64 output", "Free trial keys at signup with no card", "Public OpenAPI file, llms.txt and a dated changelog", "Embed 5 Fast at $0.08 per million text tokens" ], "weaknesses": [ "Terms, training notice and security page disagree on whether API data trains models or goes to third parties", "The per-search rerank price didn't render on the pricing page", "96 inputs a call, and input_type is required", "One unscoped key reaches every Cohere endpoint, including delete operations", "No security.txt, no published subprocessor list found, no SLA" ], "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 (an open SDK bug drops types missing from the first response)", "Budget rerank by searches. 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", "Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 72.5 } ], "editorialScores": { "ergonomics": 87, "maintenance": 87, "payments": 35, "reliability": 83, "schema": 92, "security": 50, "transparency": 47 }, "provenanceScore": 90 }, "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" }, "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" }, { "item": "Rerank 3.5 on Amazon Bedrock", "unit": "1k-requests", "usd": 2, "note": "Per 1,000 queries on Bedrock. Cohere's own per-search rate wasn't readable" } ], "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": 90 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cohere-embed.json", "live": { "slug": "cohere-embed", "probe": { "target": "https://api.cohere.com/v2/embed", "method": "get", "lastAt": "2026-10-04T23:32:45.580051367Z", "lastOk": true, "lastStatus": 401, "lastMs": 217, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 138, "p95ms24h": 231, "samples24h": 272, "samples30d": 895, "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": 267, "ok": 267 } ] }, "vendorStatus": { "page": "https://status.cohere.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T23:27:43.202253333Z" }, "versions": [ { "registry": "github", "name": "cohere-ai/cohere-python", "version": "7.1.0", "released": "2026-08-26", "seenAt": "2026-10-04T16:24:10.241421769Z" }, { "registry": "npm", "name": "cohere-ai", "version": "8.1.0", "seenAt": "2026-10-04T16:24:09.448504414Z" }, { "registry": "pypi", "name": "cohere", "version": "7.2.0", "released": "2026-09-28", "seenAt": "2026-10-04T16:24:09.333076918Z" } ], "githubStars": 402, "npmWeekly": 552308, "pypiWeekly": 2643637, "securityTxt": { "url": "https://cohere.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:16:04.955115134Z" }, "llmsTxt": { "url": "https://docs.cohere.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:27.688389067Z" }, "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": 200, "checkedAt": "2026-10-04T15:43:24.429982459Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "3307cd014040" }, { "url": "https://cohere.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-10-04T15:42:00.146622278Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "7fa6935e4076" }, { "url": "https://cohere.com/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-04T15:42:02.190831807Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "006a1fc7471b" }, { "url": "https://cohere.com/terms-of-use", "kind": "terms", "status": 304, "checkedAt": "2026-10-04T15:42:04.180850531Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "18e97fee9544" } ], "updatedAt": "2026-10-04T23:32:45.580051367Z" } }, "b": { "slug": "voyage-ai", "name": "Voyage AI embeddings and rerankers", "vendor": "Voyage AI (MongoDB)", "vendorUrl": "https://www.voyageai.com", "kind": "http-api", "category": "embeddings", "summary": "Embedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI.", "url": "https://www.anchorterminal.com/tools/voyage-ai", "markdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voyage-ai.json", "repo": "https://github.com/voyage-ai/voyageai-python", "license": "MIT (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.voyageai.com/v1/embeddings", "packages": [ { "registry": "pypi", "name": "voyageai" }, { "registry": "npm", "name": "voyageai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from the Voyage dashboard. The Python and TypeScript clients read `VOYAGE_API_KEY`.", "pricing": "freemium", "pricingNotes": "Per million tokens. voyage-4-large, voyage-context-4, voyage-code-4 and voyage-multimodal-3.5 $0.12, voyage-4 $0.06, voyage-4-lite $0.02, rerank-3 $0.05, rerank-3-lite $0.02. Multimodal adds $0.60 per billion pixels. Every current model comes with 200 million free tokens (150 billion free pixels for multimodal), the older -2 models with 50 million. The Batch API is 33 per cent cheaper and the free tokens don't apply to it. Files API storage $0.05 per GB a month (https://docs.voyageai.com/docs/pricing).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 105, "npmWeekly": 306748, "pypiWeekly": 936716, "asOf": "2026-09-30" }, "docsUrl": "https://docs.voyageai.com/docs/introduction", "llmsTxt": "https://docs.voyageai.com/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 59, "grade": "C", "agentReady": false, "rank": 273, "ranked": true, "rankOf": 452, "categoryRank": 5, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 98, "maintenance": 78, "payments": 40, "reliability": 45, "schema": 61, "security": 45, "transparency": 51 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.", "strengths": [ "200 million free tokens per current model, then $0.02 to $0.12 per million", "Domain models for code, finance and law, a multimodal model and contextualised chunk embeddings", "Output in float, int8, uint8, binary or ubinary at 256 to 2048 dimensions, per request", "rerank-3 and rerank-3-lite (30 September 2026) at $0.05 and $0.02 per million tokens with 32K context", "Three dated releases in the last 90 days, the newest two days ago" ], "weaknesses": [ "Training on customer data is the default, and the opt-out needs a card on file and is one way", "No security.txt, and no status page linked or reachable", "Rate-limit tiers only begin once a payment method is added", "No public OpenAPI file, and releases are dated only on the blog", "Python SDK issues from 2024 and 2025 sit without a maintainer reply" ], "agentNotes": [ "Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard", "Set input_type to query or document and keep it consistent between indexing and querying", "Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models", "Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck", "Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 4, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "ergonomics": 98, "maintenance": 78, "payments": 40, "reliability": 45, "schema": 61, "security": 45, "transparency": 25 }, "provenanceScore": 76 }, "connect": { "install": "pip install voyageai # or: npm i voyageai", "http": "curl https://api.voyageai.com/v1/embeddings \\\n -H \"Authorization: Bearer $VOYAGE_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"voyage-4\",\"input\":[\"What does the embeddings endpoint return?\"],\"input_type\":\"query\",\"output_dimension\":1024}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/voyage-ai" }, "sameCompany": [ "mongodb-mcp" ], "area": "models", "unitPrices": [ { "item": "voyage-4-large embeddings", "unit": "1m-tokens", "usd": 0.12, "note": "Also voyage-context-4, voyage-code-4 and voyage-multimodal-3.5" }, { "item": "voyage-4 embeddings", "unit": "1m-tokens", "usd": 0.06 }, { "item": "voyage-4-lite embeddings", "unit": "1m-tokens", "usd": 0.02 }, { "item": "rerank-3", "unit": "1m-tokens", "usd": 0.05 }, { "item": "rerank-3-lite", "unit": "1m-tokens", "usd": 0.02 }, { "item": "Files API storage", "unit": "gb-month", "usd": 0.05 } ], "provenance": { "legalEntity": "Voyage AI Innovations, Inc.", "domain": "voyageai.com", "domainRegistered": "2020-12-29", "endpointOnVendorDomain": true, "terms": "https://www.voyageai.com/tos", "privacy": "https://www.voyageai.com/privacy", "statusPage": "", "changelog": "https://docs.voyageai.com/changelog", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms (updated 2026-05-27) and privacy policy (2025-02-20) name Voyage AI Innovations, Inc. under California law, with no postal address. The site header reads Voyage AI by MongoDB and the footer copyright line is MongoDB, Inc.", "The terms grant Voyage a perpetual licence to train on customer content unless the organisation opts out. Content sent before the opt-out stays covered.", "status.voyageai.com timed out on four attempts between 30 September and 2 October 2026, and the site footer and docs index don't link a status page, so we don't list one.", "The docs changelog shows one undated entry. Model releases are dated on blog.voyageai.com, newest rerank-3 on 2026-09-30.", "SOC 2 and HIPAA reports are on a Vanta trust page linked from the footer." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/voyage-ai.json", "live": { "slug": "voyage-ai", "probe": { "target": "https://api.voyageai.com/v1/embeddings", "method": "get", "lastAt": "2026-10-04T23:32:56.671498442Z", "lastOk": true, "lastStatus": 405, "lastMs": 174, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 196, "p95ms24h": 685, "samples24h": 272, "samples30d": 895, "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": 267, "ok": 267 } ] }, "versions": [ { "registry": "github", "name": "voyage-ai/voyageai-python", "version": "v0.5.0", "released": "2026-07-10", "seenAt": "2026-10-04T16:43:54.223118342Z" }, { "registry": "npm", "name": "voyageai", "version": "0.4.0", "seenAt": "2026-10-04T16:43:53.823742775Z" }, { "registry": "pypi", "name": "voyageai", "version": "0.5.0", "released": "2026-07-10", "seenAt": "2026-10-04T16:43:53.714440219Z" } ], "githubStars": 113, "npmWeekly": 334044, "pypiWeekly": 1007721, "securityTxt": { "url": "https://voyageai.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-04T15:15:47.28134032Z" }, "llmsTxt": { "url": "https://docs.voyageai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:21.616441109Z" }, "domain": { "domain": "voyageai.com", "registered": "2020-12-29", "source": "https://rdap.verisign.com/com/v1/domain/voyageai.com", "checkedAt": "2026-10-04T13:07:42.741568711Z" }, "pages": [ { "url": "https://docs.voyageai.com/changelog", "kind": "changelog", "status": 404, "checkedAt": "2026-10-04T15:44:14.753232313Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://docs.voyageai.com/docs/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-04T15:44:17.073382402Z", "changedAt": "2026-10-04T15:44:17.073382402Z", "fingerprint": "e94e011a3e9b" }, { "url": "https://www.voyageai.com/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-04T15:52:50.401389134Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "d71dc9022a14" }, { "url": "https://www.voyageai.com/tos", "kind": "terms", "status": 304, "checkedAt": "2026-10-04T15:52:52.573014262Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "885b45461466" } ], "updatedAt": "2026-10-04T23:32:56.671498442Z" } }, "summary": "Cohere Embed and Rerank has a score of 72.5 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is reliability, 38 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai", "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md", "slim": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.min.md" }, "markdown": "Cohere Embed and Rerank has a score of 72.5 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is reliability, 38 points.\n\n- Cohere Embed and Rerank: grade BB, 72.5/100, rank #69 of 452. Markdown https://www.anchorterminal.com/tools/cohere-embed.md · JSON https://www.anchorterminal.com/api/v1/tools/cohere-embed.json\n- Voyage AI embeddings and rerankers: grade C, 59/100, rank #273 of 452. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Which one, for what\n\nPick Cohere Embed and Rerank for reliability (+38), schema \u0026 documentation (+31), security \u0026 auth (+5), maintenance \u0026 community (+9), transparency \u0026 trust (+18).\n\nPick Voyage AI embeddings and rerankers for agent ergonomics (+11), payments \u0026 pricing (+5).\n\n## Score by category\n\n| Category | Weight | Cohere Embed and Rerank | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 83 | 45 | Cohere Embed and Rerank +38 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 92 | 61 | Cohere Embed and Rerank +31 |\n| Agent ergonomics | 13% (16.2 this run) | 87 | 98 | Voyage AI embeddings and rerankers +11 |\n| Security \u0026 auth | 14% (17.5 this run) | 50 | 45 | Cohere Embed and Rerank +5 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 35 | 40 | Voyage AI embeddings and rerankers +5 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 87 | 78 | Cohere Embed and Rerank +9 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 69 | 51 | Cohere Embed and Rerank +18 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.5 · BB** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Cohere Embed and Rerank | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Cohere | Voyage AI (MongoDB) |\n| Hosted endpoint | `https://api.cohere.com/v2/embed` | `https://api.voyageai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | MIT (SDK) | MIT (SDK) |\n| Tools exposed | none | none |\n| Context cost (tools/list) | n/a | n/a |\n| p95 latency | not measured yet | not measured yet |\n| Availability (30d) | not measured yet | not measured yet |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| MCP registry | not listed | not listed |\n| Last release | 2026-09-30 | 2026-09-30 |\n| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 4/5 (2) |\n\n## Verdicts\n\n**Cohere Embed and Rerank.** Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.\n\n**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.\n\n## Before you call either\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 (an open SDK bug drops types missing from the first response)\n3. Budget rerank by searches. 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. Ask for int8 or binary embedding_types and a smaller output_dimension before scaling the vector store\n\n### Voyage AI embeddings and rerankers\n\n1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard\n2. Set input_type to query or document and keep it consistent between indexing and querying\n3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models\n4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck\n5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens\n\n## Other comparisons with Cohere Embed and Rerank or Voyage AI embeddings and rerankers\n\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 OpenAI embeddings](https://www.anchorterminal.com/compare/cohere-embed-vs-openai-embeddings.md)\n- [Cohere Embed and Rerank vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.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- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-04", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.3", "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": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers", "url": "" } ], "description": "Cohere Embed and Rerank has a score of 72.5 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is reliability, 38 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Cohere Embed and Rerank BB 72.5", "Voyage AI embeddings and rerankers C 59", "scores" ], "h1": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers", "image": "https://www.anchorterminal.com/assets/og/compare-cohere-embed-vs-voyage-ai.png", "path": "/compare/cohere-embed-vs-voyage-ai", "published": "2026-10-01", "section": "tools", "title": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai" }, "tokens": { "markdown": 1700, "slim": 380 }, "version": 1 }