{ "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": "2026-10-05T01:43:41.469040721Z", "lastOk": true, "lastStatus": 401, "lastMs": 77, "lastNote": "asks for credentials", "authRequired": true, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 52, "p95ms24h": 91, "samples24h": 272, "samples30d": 920, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 20, "ok": 20 } ] }, "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": "2026-10-05T01:43:41.469040721Z" } }, "b": { "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": "2026-10-05T01:43:48.669193268Z", "lastOk": false, "lastStatus": 503, "lastMs": 465, "lastNote": "server error", "authRequired": false, "uptime24h": 0, "uptime30d": 0, "p50ms24h": 0, "p95ms24h": 0, "samples24h": 272, "samples30d": 920, "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": 272, "ok": 0 }, { "date": "2026-10-05", "probes": 20, "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": "2026-10-05T01:43:48.669193268Z" } }, "summary": "Mistral Embed and Codestral Embed has a score of 58.2 (C) against ZeroEntropy zerank and zembed's 13.8 (F). Both do embed text. The largest gap is schema \u0026 documentation, 58 points." }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy", "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.md", "slim": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy.min.md" }, "markdown": "Mistral Embed and Codestral Embed has a score of 58.2 (C) against ZeroEntropy zerank and zembed's 13.8 (F). Both do embed text. The largest gap is schema \u0026 documentation, 58 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- ZeroEntropy zerank and zembed: grade F, 13.8/100, rank #450 of 452. Markdown https://www.anchorterminal.com/tools/zeroentropy.md · JSON https://www.anchorterminal.com/api/v1/tools/zeroentropy.json\n\n## Which one, for what\n\nPick Mistral Embed and Codestral Embed for reliability (+38), schema \u0026 documentation (+58), agent ergonomics (+58), security \u0026 auth (+20), payments \u0026 pricing (+40), maintenance \u0026 community (+35), transparency \u0026 trust (+27).\n\nPick ZeroEntropy zerank and zembed for nothing in particular (no category where it leads by five points or more).\n\n## Score by category\n\n| Category | Weight | Mistral Embed and Codestral Embed | ZeroEntropy zerank and zembed | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 38 | 0 | Mistral Embed and Codestral Embed +38 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 89 | 31 | Mistral Embed and Codestral Embed +58 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 20 | Mistral Embed and Codestral Embed +58 |\n| Security \u0026 auth | 14% (17.5 this run) | 45 | 25 | Mistral Embed and Codestral Embed +20 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 0 | Mistral Embed and Codestral Embed +40 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 40 | 5 | Mistral Embed and Codestral Embed +35 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 81 | 54 | Mistral Embed and Codestral Embed +27 |\n| Negative events | ≤15 | 0 | -4 | |\n| **Total** | | **58.2 · C** | **13.8 · F** | |\n\n## Facts side by side\n\n| Fact | Mistral Embed and Codestral Embed | ZeroEntropy zerank and zembed |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Mistral AI | ZeroEntropy |\n| Hosted endpoint | `https://api.mistral.ai/v1/embeddings` | `https://api.zeroentropy.dev/v1/models/rerank` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Freemium | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 (SDK) | Apache-2.0 (SDKs and model weights) |\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 | no |\n| MCP registry | not listed | not listed |\n| Last release | 2025-05-28 | 2026-03-02 |\n| Popularity | 769 stars | 221k npm/wk |\n| Agent reviews | 3.5/5 (2) | 1/5 (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**ZeroEntropy zerank and zembed.** 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## 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### ZeroEntropy zerank and zembed\n\n1. Don't call api.zeroentropy.dev. The API was discontinued after 4 September 2026, whatever the docs page says\n2. Self-host zerank-2 or zembed-1 from Hugging Face under Apache 2.0 if you want the same model\n3. Pick a hosted reranker elsewhere if you can't self-host. ZeroEntropy's own guide names Cohere and Voyage\n4. Re-embed the corpus if you move off zembed-1. Vectors from another model aren't compatible\n\n## Other comparisons with Mistral Embed and Codestral Embed or ZeroEntropy zerank and zembed\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 ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/cohere-embed-vs-zeroentropy.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 ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/gemini-embedding-vs-zeroentropy.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 ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.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 Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [OpenAI embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/openai-embeddings-vs-zeroentropy.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": "2026-10-05", "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 ZeroEntropy zerank and zembed", "url": "" } ], "description": "Mistral Embed and Codestral Embed has a score of 58.2 (C) against ZeroEntropy zerank and zembed's 13.8 (F). Both do embed text. The largest gap is schema \u0026 documentation, 58 points. Category scores, facts, verdicts and agent notes side by side.", "facts": [ "Mistral Embed and Codestral Embed C 58.2", "ZeroEntropy zerank and zembed F 13.8", "scores" ], "h1": "Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed", "image": "https://www.anchorterminal.com/assets/og/compare-mistral-embeddings-vs-zeroentropy.png", "path": "/compare/mistral-embeddings-vs-zeroentropy", "published": "2026-10-01", "section": "tools", "title": "Mistral Embed and Codestral Embed vs ZeroEntropy zerank and zembed", "toc": null, "updated": "2026-10-05", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-zeroentropy" }, "tokens": { "markdown": 1650, "slim": 380 }, "version": 1 }