{ "data": { "a": { "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": "2026-10-04T23:48:12.180809634Z", "lastOk": true, "lastStatus": 401, "lastMs": 62, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 52, "p95ms24h": 91, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "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": "2026-10-04T23:48:12.180809634Z" } }, "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": "2026-10-04T23:48:18.326854436Z", "lastOk": true, "lastStatus": 405, "lastMs": 675, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 196, "p95ms24h": 685, "samples24h": 272, "samples30d": 898, "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": 270, "ok": 270 } ] }, "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": "2026-10-04T23:48:18.326854436Z" } }, "summary": "Voyage AI embeddings and rerankers has a score of 59 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 38 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai", "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md", "slim": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.min.md" }, "markdown": "Voyage AI embeddings and rerankers has a score of 59 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 38 points.\n\n- Mistral Embed and Codestral Embed: grade C, 58.2/100, rank #283 of 452. Markdown https://www.anchorterminal.com/tools/mistral-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-embeddings.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 Mistral Embed and Codestral Embed for schema \u0026 documentation (+28), transparency \u0026 trust (+30).\n\nPick Voyage AI embeddings and rerankers for reliability (+7), agent ergonomics (+20), maintenance \u0026 community (+38).\n\n## Score by category\n\n| Category | Weight | Mistral Embed and Codestral Embed | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 45 | Voyage AI embeddings and rerankers +7 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 61 | Mistral Embed and Codestral Embed +28 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 98 | Voyage AI embeddings and rerankers +20 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 45 | even |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 40 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 40 | 78 | Voyage AI embeddings and rerankers +38 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 81 | 51 | Mistral Embed and Codestral Embed +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.2 · C** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Mistral Embed and Codestral Embed | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Mistral AI | Voyage AI (MongoDB) |\n| Hosted endpoint | `https://api.mistral.ai/v1/embeddings` | `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 | Apache-2.0 (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 | 2025-05-28 | 2026-09-30 |\n| Popularity | 769 stars | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | 3.5/5 (2) | 4/5 (2) |\n\n## Verdicts\n\n**Mistral Embed and Codestral Embed.** EU and US regional endpoints and a French legal entity. Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026.\n\n**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### Mistral Embed and Codestral Embed\n\n1. Use codestral-embed whenever you want smaller or binary vectors. mistral-embed has no output options\n2. Pass output_dimension 512 and output_dtype int8 on codestral-embed to cut vector storage before touching anything else\n3. Keep chunks under 8k tokens. There's no long-context embedding model on this API\n4. Check status.mistral.ai before a big index job and retry with backoff, since the Embedding API had two degradations in August 2026\n5. Pin dated model ids (mistral-embed-2312, codestral-embed-2505) so an alias move can't change your vectors\n\n### 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 Mistral Embed and Codestral Embed or Voyage AI embeddings and rerankers\n\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 Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)\n- [Gemini Embedding vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/gemini-embedding-vs-mistral-embeddings.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 Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.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 OpenAI embeddings](https://www.anchorterminal.com/compare/mistral-embeddings-vs-openai-embeddings.md)\n- [Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.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": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers", "url": "" } ], "description": "Voyage AI embeddings and rerankers has a score of 59 (C) against Mistral Embed and Codestral Embed's 58.2 (C). Both do embed text. The largest gap is maintenance \u0026 community, 38 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Mistral Embed and Codestral Embed C 58.2", "Voyage AI embeddings and rerankers C 59", "scores" ], "h1": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers", "image": "https://www.anchorterminal.com/assets/og/compare-mistral-embeddings-vs-voyage-ai.png", "path": "/compare/mistral-embeddings-vs-voyage-ai", "published": "2026-10-01", "section": "tools", "title": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and…", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai" }, "tokens": { "markdown": 1650, "slim": 380 }, "version": 1 }