{ "data": { "category": { "area": "models", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "description": "Models that turn text, images and code into vectors for search, and rerankers that reorder search results by relevance. Compared on retrieval quality, dimensions and context length, multilingual support and price per million tokens.", "indexed": [ { "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/elopstudio-ai-wave.json", "kind": "mcp", "name": "AI Wave", "slug": "elopstudio-ai-wave", "url": "https://www.anchorterminal.com/tools/elopstudio-ai-wave" }, { "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apple-rag-mcp-server.json", "kind": "mcp", "name": "apple-rag.com MCP server", "slug": "apple-rag-mcp-server", "url": "https://www.anchorterminal.com/tools/apple-rag-mcp-server" }, { "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voxell-forge.json", "kind": "mcp", "name": "forge", "slug": "voxell-forge", "url": "https://www.anchorterminal.com/tools/voxell-forge" } ], "indexedCount": 3, "json": "https://www.anchorterminal.com/categories/embeddings.json", "name": "Embeddings \u0026 rerankers", "slug": "embeddings", "test": "The same corpus and queries embedded with each model, then the top results reranked. We check retrieval quality against labelled answers, latency and the cost per million tokens.", "title": "Embedding and reranking APIs for AI agents", "toolCount": 7, "tools": [ "openai-embeddings", "cohere-embed", "gemini-embedding", "jina-embeddings", "voyage-ai", "mistral-embeddings", "zeroentropy" ], "url": "https://www.anchorterminal.com/categories/embeddings" }, "tools": [ { "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": 73.4, "grade": "BB", "agentReady": true, "rank": 59, "ranked": true, "rankOf": 452, "categoryRank": 1, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 90, "maintenance": 60, "payments": 30, "reliability": 65, "schema": 89, "security": 95, "transparency": 88 }, "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.", "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": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 73.4 } ], "editorialScores": { "ergonomics": 90, "maintenance": 60, "payments": 30, "reliability": 65, "schema": 89, "security": 95, "transparency": 75 }, "provenanceScore": 100 }, "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" }, "sameCompany": [ "openai-api", "openai-moderation", "openai-image-api", "openai-sora", "openai-agents-sdk", "openai-codex" ], "area": "models", "unitPrices": [ { "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": 100 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/openai-embeddings.json", "live": { "slug": "openai-embeddings", "probe": { "target": "https://api.openai.com/v1/embeddings", "method": "get", "lastAt": "2026-10-04T22:35:28.124861329Z", "lastOk": true, "lastStatus": 401, "lastMs": 135, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 112, "p95ms24h": 177, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "vendorStatus": { "page": "https://status.openai.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T22:34:01.15976325Z" }, "versions": [ { "registry": "github", "name": "openai/openai-python", "version": "v3.24.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:35:31.371334587Z" }, { "registry": "npm", "name": "openai", "version": "7.27.0", "seenAt": "2026-10-04T16:35:31.320816589Z" }, { "registry": "pypi", "name": "openai", "version": "3.24.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:35:31.204685983Z" } ], "githubStars": 31742, "npmWeekly": 50351921, "pypiWeekly": 72949998, "securityTxt": { "url": "https://openai.com/.well-known/security.txt", "state": "valid", "checkedAt": "2026-10-04T15:15:58.86463118Z" }, "llmsTxt": { "url": "https://developers.openai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:06.146857182Z" }, "domain": { "domain": "openai.com", "registered": "2007-01-19", "source": "https://rdap.verisign.com/com/v1/domain/openai.com", "checkedAt": "2026-10-04T13:05:02.32020521Z" }, "updatedAt": "2026-10-04T22:35:28.124861329Z" } }, { "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": "2026-10-04T22:35:21.178195823Z", "lastOk": true, "lastStatus": 401, "lastMs": 138, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 138, "p95ms24h": 230, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "vendorStatus": { "page": "https://status.cohere.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T22:33:50.162650042Z" }, "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": "2026-10-04T22:35:21.178195823Z" } }, { "slug": "gemini-embedding", "name": "Gemini Embedding", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "http-api", "category": "embeddings", "summary": "gemini-embedding-2, Google's multimodal embedding model, takes text, images, video, audio and PDFs into one 3072-dimension space (truncatable to 128) at 8,192 input tokens in 100+ languages.", "url": "https://www.anchorterminal.com/tools/gemini-embedding", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-embedding.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent", "packages": [ { "registry": "pypi", "name": "google-genai" }, { "registry": "npm", "name": "@google/genai" } ], "auth": "api-key", "authNotes": "`x-goog-api-key` header with a key from AI Studio on the Gemini Developer API. On Vertex AI it's a Google Cloud OAuth token and a project.", "pricing": "freemium", "pricingNotes": "On Vertex AI, Gemini Embedding 2 text input is $0.20 per million tokens online and $0.10 in batch, image input $0.45 per million tokens, video $12.00 and audio $6.50 per million tokens, with no output charge (https://cloud.google.com/vertex-ai/generative-ai/pricing). The Gemini Developer API pricing page lists the embedding models further down a page too long for our fetch to read, so we quote Vertex. Google's blog puts the Batch API at 50 per cent of the standard embedding price (https://developers.googleblog.com/building-with-gemini-embedding-2/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3992, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://ai.google.dev/gemini-api/docs/embeddings", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual" ], "tags": [ "official", "hosted", "freemium", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2026-04-22", "graded": true, "anchor": { "graded": true, "score": 71, "grade": "BB", "agentReady": true, "rank": 90, "ranked": true, "rankOf": 452, "categoryRank": 3, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 80 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.", "strengths": [ "Text, images, video, audio and PDFs interleaved in one request and one vector space", "Any output size from 128 to 3072, with truncated vectors returned normalised", "Batch API at half the standard embedding price", "Keys can be restricted to the Gemini API and to IPs or apps, and Vertex AI adds IAM roles and audit logs", "llms.txt with Markdown copies of every docs page, and a public Discovery document" ], "weaknesses": [ "$0.20 per million text tokens, against $0.02 for OpenAI's small model", "8,192 input tokens and float output only", "No reranker on the Gemini API", "Rate limits for embedding models are only visible in the AI Studio dashboard", "Free-tier data is used to improve Google products, and zero retention is Vertex-only" ], "agentNotes": [ "Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents", "Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised", "Use batchEmbedContents for indexing, and the Batch API for anything large, at half price", "Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first", "Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 71 } ], "editorialScores": { "ergonomics": 86, "maintenance": 75, "payments": 30, "reliability": 65, "schema": 89, "security": 70, "transparency": 60 }, "provenanceScore": 100 }, "connect": { "install": "pip install google-genai # or: npm i @google/genai", "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"content\":{\"parts\":[{\"text\":\"task: search result | query: What does the embeddings endpoint return?\"}]},\"output_dimensionality\":768}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/gemini-embedding" }, "sameCompany": [ "gemini-api", "vertex-ai-tuning", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-adk", "google-secret-manager", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "google-drive-api", "gemini-cli" ], "area": "models", "unitPrices": [ { "item": "gemini-embedding-2 text input (Vertex AI)", "unit": "1m-tokens", "usd": 0.2 }, { "item": "gemini-embedding-2 text input, batch (Vertex AI)", "unit": "1m-tokens", "usd": 0.1 }, { "item": "gemini-embedding-2 image input (Vertex AI)", "unit": "1m-tokens", "usd": 0.45 }, { "item": "gemini-embedding-2 audio input (Vertex AI)", "unit": "1m-tokens", "usd": 6.5 }, { "item": "gemini-embedding-2 video input (Vertex AI)", "unit": "1m-tokens", "usd": 12 } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same terms, privacy and data handling as the Gemini Developer API listing. The embedding docs, model page, rate-limit page and the Vertex pricing page were checked on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "Prices quoted are Vertex AI's. The Developer API pricing page couldn't be read to the embedding section.", "The Vertex AI pricing page still labels Gemini Embedding 2 as preview while Google's blog announced GA on 2026-04-30." ], "score": 100 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-embedding.json", "live": { "slug": "gemini-embedding", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent", "method": "get", "lastAt": "2026-10-04T22:35:23.807036596Z", "lastOk": true, "lastStatus": 404, "lastMs": 26, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 34, "p95ms24h": 69, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:27:52.25299697Z" }, { "registry": "npm", "name": "@google/genai", "version": "2.27.0", "seenAt": "2026-10-04T16:27:52.000233538Z" }, { "registry": "pypi", "name": "google-genai", "version": "2.28.0", "released": "2026-10-02", "seenAt": "2026-10-04T16:27:51.891499993Z" } ], "githubStars": 4002, "npmWeekly": 29048793, "pypiWeekly": 34122162, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-10-04T15:15:53.387118101Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:50.111667415Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-10-04T13:05:50.737985829Z" }, "pages": [ { "url": "https://cloud.google.com/vertex-ai/generative-ai/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-04T15:42:05.896276003Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "793e43bfda77" } ], "updatedAt": "2026-10-04T22:35:23.807036596Z" } }, { "slug": "jina-embeddings", "name": "Jina Embeddings and Reranker", "vendor": "Jina AI (Elastic)", "vendorUrl": "https://jina.ai", "kind": "http-api", "category": "embeddings", "summary": "jina-embeddings-v5 in text and omni (text, image, audio, video, PDF) variants at up to 32,768 tokens, plus the jina-reranker-v3.5 at 131,072 tokens a call.", "url": "https://www.anchorterminal.com/tools/jina-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jina-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json", "repo": "https://github.com/jina-ai/MCP", "license": "Apache-2.0 (MCP server)", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://api.jina.ai/v1/embeddings", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a `jina_...` key. A new account gets a key with free tokens, and the same key works for Reader, Search, Embeddings, Reranker and the MCP server.", "pricing": "freemium", "pricingNotes": "Prepaid tokens, topped up through Stripe (cards, Google Pay, PayPal) and shared across every Jina API. A new key comes with free tokens. Non-text inputs are converted to tokens by the encoder, about 363 tokens an image on v5-omni, 4,840 on v4 and 16,000 on jina-clip-v2. Jina changed its pricing model on 2025-05-06, and the public pages don't state a US dollar price per token, so we don't list one (https://jina.ai/embeddings/).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": 12, "popularity": { "githubStars": 841, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://jina.ai/embeddings/", "llmsTxt": "https://jina.ai/models/llms.txt", "openapi": "https://api.jina.ai/openapi.json", "capabilities": [ "embed.text", "embed.multimodal", "embed.code", "embed.multilingual", "rerank" ], "tags": [ "hosted", "freemium", "free-tier", "no-card", "mcp", "prepaid", "eu" ], "lastRelease": "2026-09-18", "graded": true, "anchor": { "graded": true, "score": 61.3, "grade": "C", "agentReady": false, "rank": 230, "ranked": true, "rankOf": 452, "categoryRank": 4, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.", "strengths": [ "jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap", "v5-omni embeds text, images, audio, video and PDFs into one space", "OpenAPI 3.1 file with enums for model, task and embedding_type, and error responses from 400 to 504", "Hosted MCP server with rerank and dedupe tools, filterable per client", "Doesn't train on inputs, per the terms" ], "weaknesses": [ "No price per token in any currency on the public pages", "One prepaid balance shared with Reader and Search, so a scraping job can drain the embedding budget", "26 automated incidents on the status feed from 15 September to 1 October 2026, and no status component for v5-omni or reranker v3.5", "No security.txt, no SLA and no API changelog", "The MCP server has no CI or tests, and current weights are CC BY-NC 4.0" ], "agentNotes": [ "Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks", "On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise", "Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls", "Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings", "Count image tokens before a big multimodal job, about 363 an image on v5-omni" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61.3 } ], "editorialScores": { "ergonomics": 86, "maintenance": 62, "payments": 30, "reliability": 65, "schema": 84, "security": 35, "transparency": 45 }, "provenanceScore": 76 }, "connect": { "http": "curl https://api.jina.ai/v1/rerank \\\n -H \"Authorization: Bearer $JINA_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"jina-reranker-v3.5\",\"query\":\"embedding price per million tokens\",\"documents\":[\"Tokens are prepaid and shared across APIs.\",\"Berlin is in Germany.\"],\"top_n\":1}'", "claudeCode": "claude mcp add --transport http jina \"https://mcp.jina.ai/v1?include_tags=rerank\" --header \"Authorization: Bearer $JINA_API_KEY\"", "config": { "mcpServers": { "jina": { "headers": { "Authorization": "Bearer ${JINA_API_KEY}" }, "url": "https://mcp.jina.ai/v1?include_tags=rerank" } } } }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/jina-embeddings" }, "sameCompany": [ "jina-reader" ], "area": "models", "provenance": { "legalEntity": "Jina AI GmbH", "domain": "jina.ai", "domainRegistered": "2020-01-20", "domainNote": "Jina AI GmbH is a subsidiary of Elastic N.V. since October 2025, and the privacy statement is Elastic's.", "endpointOnVendorDomain": true, "terms": "https://jina.ai/legal/", "privacy": "https://www.elastic.co/legal/privacy-statement", "statusPage": "https://status.jina.ai", "changelog": "", "securityTxt": "none", "checked": "2026-10-02", "notes": [ "The terms give Prinzessinnenstraße 19-20, 10969 Berlin, Germany, under German law with Berlin courts.", "jina.ai/.well-known/security.txt returns 404. The root jina.ai/llms.txt returns 404, but the embeddings page links llms.txt at jina.ai/models/llms.txt, an OpenAPI 3.1 document at api.jina.ai/openapi.json and API docs at api.jina.ai/scalar.", "The MCP server's source is public under Apache-2.0 (version 1.10.0, last commit 2026-09-18)." ], "score": 76 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jina-embeddings.json", "live": { "slug": "jina-embeddings", "probe": { "target": "https://api.jina.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-04T22:35:25.175439338Z", "lastOk": true, "lastStatus": 401, "lastMs": 193, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 188, "p95ms24h": 253, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "vendorStatus": { "page": "https://status.jina.ai", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-04T22:33:58.517917162Z" }, "githubStars": 869, "securityTxt": { "url": "https://jina.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:16:02.621468742Z" }, "llmsTxt": { "url": "https://jina.ai/models/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:17:54.780870302Z" }, "domain": { "domain": "jina.ai", "registered": "2020-01-20", "source": "https://rdap.identitydigital.services/rdap/domain/jina.ai", "checkedAt": "2026-10-04T13:08:08.912440143Z" }, "pages": [ { "url": "https://www.elastic.co/legal/privacy-statement", "kind": "privacy", "status": 200, "checkedAt": "2026-10-04T15:50:08.614358581Z", "changedAt": "2026-10-01T13:17:07.465912374Z", "fingerprint": "bd620f341674" }, { "url": "https://jina.ai/legal/", "kind": "terms", "status": 200, "checkedAt": "2026-10-04T15:45:10.714958883Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "822c862ff72d" } ], "updatedAt": "2026-10-04T22:35:25.175439338Z" } }, { "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": "2026-10-04T22:35:33.938991636Z", "lastOk": true, "lastStatus": 405, "lastMs": 199, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 197, "p95ms24h": 683, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "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": "2026-10-04T22:35:33.938991636Z" } }, { "slug": "mistral-embeddings", "name": "Mistral Embed and Codestral Embed", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "http-api", "category": "embeddings", "summary": "Mistral's API for generating text and code embeddings.", "url": "https://www.anchorterminal.com/tools/mistral-embeddings", "markdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json", "repo": "https://github.com/mistralai/client-python", "license": "Apache-2.0 (SDK)", "transports": [ "http" ], "remoteUrl": "https://api.mistral.ai/v1/embeddings", "packages": [ { "registry": "pypi", "name": "mistralai" }, { "registry": "npm", "name": "@mistralai/mistralai" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from La Plateforme. Same key as the chat models. Regional EU and US endpoints are opt-in at 1.1 times the price.", "pricing": "freemium", "pricingNotes": "mistral-embed $0.10 and codestral-embed $0.15 per million input tokens (https://mistral.ai/pricing/api/). Batch processing at half price, regional endpoints 1.1x. The free Experiment tier needs a phone number, no card, and its data may be used for training (https://docs.mistral.ai/admin/user-management-finops/tier).", "priceSummary": "Freemium", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 769, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://docs.mistral.ai/capabilities/embeddings/overview", "llmsTxt": "https://docs.mistral.ai/llms.txt", "openapi": "https://docs.mistral.ai/openapi.yaml", "capabilities": [ "embed.text", "embed.code" ], "tags": [ "official", "hosted", "freemium", "free-tier", "eu", "openapi", "llms-txt", "python", "typescript", "batch", "closed-source" ], "lastRelease": "2025-05-28", "graded": true, "anchor": { "graded": true, "score": 58.2, "grade": "C", "agentReady": false, "rank": 283, "ranked": true, "rankOf": 452, "categoryRank": 6, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 81 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.", "strengths": [ "EU and US regional endpoints and a French legal entity", "codestral-embed with up to 3072 dimensions, first-n truncation and int8 or binary output", "OpenAPI document and llms.txt for the whole API", "Free Experiment tier with no card, and batch at half price", "Same key, billing and SDKs as Mistral's chat models" ], "weaknesses": [ "Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026", "8k context on both models, and text or code only", "mistral-embed dates from December 2023 with fixed 1024-dimension float output, and nothing new since May 2025", "No reranker, no published rate limits and no language list for the embedding models", "Free-tier data may be used for training" ], "agentNotes": [ "Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options", "Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else", "Keep chunks under 8k tokens. There's no long-context embedding model on this API", "Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026", "Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.2 } ], "editorialScores": { "ergonomics": 78, "maintenance": 40, "payments": 40, "reliability": 38, "schema": 89, "security": 45, "transparency": 65 }, "provenanceScore": 96 }, "connect": { "install": "pip install mistralai # or: npm i @mistralai/mistralai", "http": "curl -X POST https://api.mistral.ai/v1/embeddings \\\n -H \"Authorization: Bearer $MISTRAL_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"codestral-embed\",\"input\":[\"def two_sum(nums, target): ...\"],\"output_dimension\":512,\"output_dtype\":\"int8\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/mistral-embeddings" }, "sameCompany": [ "mistral-api", "mistral-moderation", "mistral-ocr" ], "area": "models", "unitPrices": [ { "item": "mistral-embed", "unit": "1m-tokens", "usd": 0.1 }, { "item": "codestral-embed", "unit": "1m-tokens", "usd": 0.15 } ], "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": true, "terms": "https://legal.mistral.ai/terms/commercial-terms-of-service", "privacy": "https://legal.mistral.ai/terms/privacy-policy", "statusPage": "https://status.mistral.ai", "changelog": "https://docs.mistral.ai/resources/changelogs", "securityTxt": "valid", "checked": "2026-09-30", "notes": [ "Same account, terms and data handling as the Mistral AI API listing. The embedding docs and the public docs repository were read on 2026-09-30; the legal documents and security.txt are as checked for that listing.", "The model catalogue in the docs repository (mistralai/platform-docs-public) is the source for context length, release dates and prices." ], "score": 96 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/mistral-embeddings.json", "live": { "slug": "mistral-embeddings", "probe": { "target": "https://api.mistral.ai/v1/embeddings", "method": "get", "lastAt": "2026-10-04T22:35:27.174830024Z", "lastOk": true, "lastStatus": 401, "lastMs": 74, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 52, "p95ms24h": 90, "samples24h": 272, "samples30d": 884, "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": 256, "ok": 256 } ] }, "vendorStatus": { "page": "https://status.mistral.ai", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-04T21:40:15.57957762Z" }, "versions": [ { "registry": "github", "name": "mistralai/client-python", "version": "v3.0.0", "released": "2026-09-28", "seenAt": "2026-10-04T16:33:27.015350974Z" }, { "registry": "npm", "name": "@mistralai/mistralai", "version": "2.7.0", "seenAt": "2026-10-04T16:33:26.736191289Z" }, { "registry": "pypi", "name": "mistralai", "version": "3.0.0", "released": "2026-09-28", "seenAt": "2026-10-04T16:33:26.621263233Z" } ], "githubStars": 770, "npmWeekly": 9114363, "pypiWeekly": 3373641, "securityTxt": { "url": "https://mistral.ai/.well-known/security.txt", "state": "valid", "expires": "2027-05-05T23:59:59.000Z", "checkedAt": "2026-10-04T15:15:48.706102345Z" }, "llmsTxt": { "url": "https://docs.mistral.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-04T15:18:00.896884597Z" }, "domain": { "domain": "mistral.ai", "registered": "2019-05-15", "source": "https://rdap.identitydigital.services/rdap/domain/mistral.ai", "checkedAt": "2026-10-04T13:08:59.683466691Z" }, "updatedAt": "2026-10-04T22:35:27.174830024Z" } }, { "slug": "zeroentropy", "name": "ZeroEntropy zerank and zembed", "vendor": "ZeroEntropy", "vendorUrl": "https://www.zeroentropy.dev", "kind": "http-api", "category": "embeddings", "summary": "Discontinued retrieval API acquired by Notion. Its embedding and reranking models remain available as open weights for self-hosting.", "url": "https://www.anchorterminal.com/tools/zeroentropy", "markdownUrl": "https://www.anchorterminal.com/tools/zeroentropy.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/zeroentropy.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/zeroentropy.json", "repo": "https://github.com/zeroentropy-ai/zeroentropy-python", "license": "Apache-2.0 (SDKs and model weights)", "transports": [ "http" ], "remoteUrl": "https://api.zeroentropy.dev/v1/models/rerank", "packages": [ { "registry": "pypi", "name": "zeroentropy" }, { "registry": "npm", "name": "zeroentropy" } ], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key from dashboard.zeroentropy.dev. The SDKs read `ZEROENTROPY_API_KEY`. An EU endpoint at eu-api.zeroentropy.dev takes the same key.", "pricing": "usage", "pricingNotes": "No longer for sale. The API was supported until 2026-09-04 and new signups closed on 2026-07-24 (https://www.zeroentropy.dev/articles/zeroentropy-is-joining-notion/). The docs and pricing page still show the old self-serve prices, $0.025 per million tokens for zerank models and $0.05 for zembed-1. The weights are free to self-host under Apache 2.0.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": 221378, "pypiWeekly": null, "asOf": "2026-09-30" }, "docsUrl": "https://docs.zeroentropy.dev/models", "capabilities": [ "rerank", "embed.text", "embed.multilingual" ], "tags": [ "retired", "superseded", "open-weights" ], "lastRelease": "2026-03-02", "graded": true, "anchor": { "graded": true, "score": 13.8, "grade": "F", "agentReady": false, "rank": 450, "ranked": true, "rankOf": 452, "categoryRank": 7, "methodology": "0.3", "run": "2026-10-01", "scores": { "ergonomics": 20, "maintenance": 5, "payments": 0, "reliability": 0, "schema": 31, "security": 25, "transparency": 54 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": -4, "negativeNotes": [ "The API was discontinued after 2026-09-04 per ZeroEntropy's own acquisition notice of 2026-07-24, but on 2026-10-01 the models page (https://docs.zeroentropy.dev/models) and pricing page (https://www.zeroentropy.dev/pricing) still list self-serve per-token prices and API access without mentioning the shutdown. Endpoint removed while still advertised (-4). The shutdown notice is at https://www.zeroentropy.dev/articles/zeroentropy-is-joining-notion/" ], "verdict": "All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.", "strengths": [ "All four models now open weights under Apache 2.0 on Hugging Face", "A migration guide with self-hosting recipes for Baseten and Modal and named hosted alternatives", "42 days' notice before the API was retired", "Migration support promised over Slack, Discord and email" ], "weaknesses": [ "The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026", "The docs and pricing page still advertise per-token API prices without mentioning the shutdown", "Nothing published on what happens to customer documents after the shutdown", "No status page, changelog or OpenAPI file" ], "agentNotes": [ "Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says", "Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model", "Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage", "Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 1, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "F", "methodology": "0.3", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 13.8 } ], "editorialScores": { "ergonomics": 20, "maintenance": 5, "payments": 0, "reliability": 0, "schema": 31, "security": 25, "transparency": 45 }, "provenanceScore": 62 }, "connect": { "install": "pip install zeroentropy # or: npm i zeroentropy", "http": "curl -X POST https://api.zeroentropy.dev/v1/models/rerank \\\n -H \"Authorization: Bearer $ZEROENTROPY_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"model\":\"zerank-2\",\"query\":\"reranker price per million tokens\",\"documents\":[\"zerank-2 costs $0.025 per million tokens.\",\"The office is in California.\"],\"top_n\":1}'" }, "letme": { "capability": "https://letme.dev/rerank", "tool": "https://letme.dev/zeroentropy" }, "supersededBy": [ "cohere-embed", "voyage-ai", "openai-embeddings" ], "area": "models", "unitPrices": [ { "item": "zerank-2 reranker", "unit": "1m-tokens", "usd": 0.025, "note": "Same price for zerank-1 and zerank-1-small" }, { "item": "zembed-1 embeddings", "unit": "1m-tokens", "usd": 0.05 } ], "provenance": { "legalEntity": "ZeroEntropy, Inc.", "domain": "zeroentropy.dev", "domainRegistered": "2024-09-02", "endpointOnVendorDomain": true, "terms": "https://www.zeroentropy.dev/terms", "privacy": "https://www.zeroentropy.dev/privacy", "statusPage": "", "changelog": "", "securityTxt": "unknown", "checked": "2026-09-30", "notes": [ "The privacy policy (2025-11-04) names ZeroEntropy, Inc., a Delaware corporation based in California, with no postal address. The terms (last revised 2025-10-07) name no entity and no governing law.", "The terms grant ZeroEntropy a licence to process and store submitted documents to provide the service and for internal improvement purposes.", "The proxy refused our fetch of security.txt, the docs llms.txt and the GitHub SDK page with a rate limit, so those are unchecked. The SDK's public repository was cloned instead.", "The embed rate-limit figures come from the SDK's docstrings, the rerank figures from the API reference." ], "score": 62 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/zeroentropy.json", "live": { "slug": "zeroentropy", "probe": { "target": "https://api.zeroentropy.dev/v1/models/rerank", "method": "get", "lastAt": "2026-10-04T22:35:34.4105834Z", "lastOk": false, "lastStatus": 503, "lastMs": 429, "lastNote": "server error", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 272, "samples30d": 884, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 0 }, { "date": "2026-10-02", "probes": 248, "ok": 0 }, { "date": "2026-10-03", "probes": 271, "ok": 0 }, { "date": "2026-10-04", "probes": 256, "ok": 0 } ] }, "versions": [ { "registry": "npm", "name": "zeroentropy", "version": "0.1.0-alpha.10", "seenAt": "2026-10-04T16:44:49.463872676Z" }, { "registry": "pypi", "name": "zeroentropy", "version": "0.1.0a11", "released": "2026-03-03", "seenAt": "2026-10-04T16:44:49.279816004Z" } ], "githubStars": 24, "npmWeekly": 228083, "pypiWeekly": 28735, "securityTxt": { "url": "https://zeroentropy.dev/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-04T15:15:50.889400174Z" }, "domain": { "domain": "zeroentropy.dev", "registered": "2024-09-02", "source": "https://pubapi.registry.google/rdap/domain/zeroentropy.dev", "checkedAt": "2026-10-04T13:04:15.835541457Z" }, "pages": [ { "url": "https://www.zeroentropy.dev/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-04T15:53:02.834493349Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "e17c53db6505" }, { "url": "https://www.zeroentropy.dev/terms", "kind": "terms", "status": 304, "checkedAt": "2026-10-04T15:53:04.985362149Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "f3a8554b48fc" } ], "updatedAt": "2026-10-04T22:35:34.4105834Z" } } ] }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/categories/embeddings", "json": "https://www.anchorterminal.com/categories/embeddings.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/categories/embeddings.md", "slim": "https://www.anchorterminal.com/categories/embeddings.min.md" }, "markdown": "Models that turn text, images and code into vectors for search, and rerankers that reorder search results by relevance. Compared on retrieval quality, dimensions and context length, multilingual support and price per million tokens.\n\n- Tools ranked: 7 · agent-ready (BB or better): 3 · accept x402: 0 · hosted endpoints: 7 · desk reviews by the panel: 14\n- JSON: https://www.anchorterminal.com/api/v1/tools.json (list) · https://www.anchorterminal.com/api/v1/rankings.json (ranked) · https://www.anchorterminal.com/api/v1/x402.json (payable) · https://www.anchorterminal.com/api/v1/capabilities.json (by capability)\n- Grades run AA, A, BB, B, C, D, E, F · methodology: https://www.anchorterminal.com/benchmark/\n\n- Capabilities in this category: embed.text, embed.multimodal, embed.code, embed.multilingual, rerank\n- https://letme.dev/embed.text picks the top-graded tool in this list and says how to call it direct; calling through letme comes later (https://www.anchorterminal.com/letme/index.md)\n\n## Ranking\n\n| # | Tool | Vendor | Kind | Category | Grade | Score | Confidence | x402 | Auth | Where | Reviews | Page |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 59 | OpenAI embeddings | OpenAI | HTTP API | Embeddings | BB | 73.4 | high | no | API key | hosted | 4.5/5 (2) | https://www.anchorterminal.com/tools/openai-embeddings.md |\n| 69 | Cohere Embed and Rerank | Cohere | HTTP API | Embeddings | BB | 72.5 | medium | no | API key | hosted | 3.5/5 (2) | https://www.anchorterminal.com/tools/cohere-embed.md |\n| 90 | Gemini Embedding | Google | HTTP API | Embeddings | BB | 71 | medium | no | API key | hosted | 3/5 (2) | https://www.anchorterminal.com/tools/gemini-embedding.md |\n| 230 | Jina Embeddings and Reranker | Jina AI (Elastic) | HTTP API | Embeddings | C | 61.3 | medium | no | API key | hosted | 3/5 (2) | https://www.anchorterminal.com/tools/jina-embeddings.md |\n| 273 | Voyage AI embeddings and rerankers | Voyage AI (MongoDB) | HTTP API | Embeddings | C | 59 | medium | no | API key | hosted | 4/5 (2) | https://www.anchorterminal.com/tools/voyage-ai.md |\n| 283 | Mistral Embed and Codestral Embed | Mistral AI | HTTP API | Embeddings | C | 58.2 | medium | no | API key | hosted | 3.5/5 (2) | https://www.anchorterminal.com/tools/mistral-embeddings.md |\n| 450 | ZeroEntropy zerank and zembed | ZeroEntropy | HTTP API | Embeddings | F | 13.8 | medium | no | API key | hosted | 1/5 (2) | https://www.anchorterminal.com/tools/zeroentropy.md |\n\nScores are from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/), with Performance and Task success pending. p95 latency and context cost come from our probes, which haven't run yet.\n\n## Summaries\n\n### 59. OpenAI embeddings, BB (73.4)\n\nOpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications. 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- Page: https://www.anchorterminal.com/tools/openai-embeddings · Markdown: https://www.anchorterminal.com/tools/openai-embeddings.md · JSON: https://www.anchorterminal.com/api/v1/tools/openai-embeddings.json\n- Capabilities: embed.text, embed.multilingual · endpoint: `https://api.openai.com/v1/embeddings`\n\n### 69. Cohere Embed and Rerank, BB (72.5)\n\nEmbed 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. 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- Page: https://www.anchorterminal.com/tools/cohere-embed · Markdown: https://www.anchorterminal.com/tools/cohere-embed.md · JSON: https://www.anchorterminal.com/api/v1/tools/cohere-embed.json\n- Capabilities: embed.text, embed.multimodal, embed.multilingual, rerank · endpoint: `https://api.cohere.com/v2/embed`\n\n### 90. Gemini Embedding, BB (71)\n\ngemini-embedding-2, Google's multimodal embedding model, takes text, images, video, audio and PDFs into one 3072-dimension space (truncatable to 128) at 8,192 input tokens in 100+ languages. Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.\n\n- Page: https://www.anchorterminal.com/tools/gemini-embedding · Markdown: https://www.anchorterminal.com/tools/gemini-embedding.md · JSON: https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json\n- Capabilities: embed.text, embed.multimodal, embed.code, embed.multilingual · endpoint: `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent`\n\n### 230. Jina Embeddings and Reranker, C (61.3)\n\njina-embeddings-v5 in text and omni (text, image, audio, video, PDF) variants at up to 32,768 tokens, plus the jina-reranker-v3.5 at 131,072 tokens a call. jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.\n\n- Page: https://www.anchorterminal.com/tools/jina-embeddings · Markdown: https://www.anchorterminal.com/tools/jina-embeddings.md · JSON: https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json\n- Capabilities: embed.text, embed.multimodal, embed.code, embed.multilingual, rerank · endpoint: `https://api.jina.ai/v1/embeddings`\n\n### 273. Voyage AI embeddings and rerankers, C (59)\n\nEmbedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI. 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- Page: https://www.anchorterminal.com/tools/voyage-ai · Markdown: https://www.anchorterminal.com/tools/voyage-ai.md · JSON: https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n- Capabilities: embed.text, embed.multimodal, embed.code, embed.multilingual, rerank · endpoint: `https://api.voyageai.com/v1/embeddings`\n\n### 283. Mistral Embed and Codestral Embed, C (58.2)\n\nMistral's API for generating text and code embeddings. EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.\n\n- Page: https://www.anchorterminal.com/tools/mistral-embeddings · Markdown: https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON: https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.json\n- Capabilities: embed.text, embed.code · endpoint: `https://api.mistral.ai/v1/embeddings`\n\n### 450. ZeroEntropy zerank and zembed, F (13.8)\n\nDiscontinued retrieval API acquired by Notion. Its embedding and reranking models remain available as open weights for self-hosting. All four models now open weights under Apache 2.0 on Hugging Face. The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026.\n\n- Page: https://www.anchorterminal.com/tools/zeroentropy · Markdown: https://www.anchorterminal.com/tools/zeroentropy.md · JSON: https://www.anchorterminal.com/api/v1/tools/zeroentropy.json\n- Capabilities: rerank, embed.text, embed.multilingual · endpoint: `https://api.zeroentropy.dev/v1/models/rerank`\n\n## How we test this category\n\nThe same corpus and queries embedded with each model, then the top results reranked. We check retrieval quality against labelled answers, latency and the cost per million tokens. This test hasn't run yet, so Task success is pending and the grades here come from the categories assessed from public evidence.\n\n## Indexed, not reviewed (3)\n\nSorted into this category from public catalogues, with facts and our own checks but no score, grade or rank (https://www.anchorterminal.com/indexed/index.md).\n\n| Listing | Kind | What it does | Why it's here |\n| --- | --- | --- | --- |\n| [AI Wave](https://www.anchorterminal.com/tools/elopstudio-ai-wave.md) | MCP server | AI model releases, price changes and deprecations in one feed: chat, embedding, speech, video. | vendor's own |\n| [apple-rag.com MCP server](https://www.anchorterminal.com/tools/apple-rag-mcp-server.md) | MCP server | Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients | vendor's own |\n| [forge](https://www.anchorterminal.com/tools/voxell-forge.md) | MCP server | MCP server for Forge, Voxell's embedding API. Voxell's Ingot-8B-R3 ranks \\#1 for English on MTEB. | vendor's own |\n\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": "Terminal", "url": "https://www.anchorterminal.com/tools/" }, { "name": "Embeddings \u0026 rerankers", "url": "" } ], "description": "7 embeddings \u0026 rerankers ranked by the Anchor benchmark. Leader OpenAI embeddings (BB). Models that turn text, images and code into vectors for search, and rerankers that reorder search results by relevance. Compared on retrieval quality, dimensions and context length, multilingual support and price per million tokens.", "facts": [ "OpenAI embeddings BB", "Cohere Embed and Rerank BB", "Gemini Embedding BB" ], "h1": "Embedding and reranking APIs for AI agents", "image": "https://www.anchorterminal.com/assets/og/categories-embeddings.png", "path": "/categories/embeddings", "published": "", "section": "tools", "title": "Embedding and reranking APIs for AI agents, ranked | Anchor Terminal", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/categories/embeddings" }, "tokens": { "markdown": 2350, "slim": 430 }, "version": 1 }