Category · Models & inference
Embedding and reranking APIs for AI agents
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.
Capability keys embed.text · embed.multimodal · embed.code · embed.multilingual · rerank · All tools
letme.dev/embed.text picks the top-graded tool in this list and says how to call it direct; calling through letme comes later.
The same listing from the live API. Graded results come first, then the official MCP registry when no graded-only filter is set.
https://www.anchorterminal.com/api/v1/searchFilters
| Compare | # | Tool | Category | Grade | Score | Agent rating | p95 | Context | Price / x402 | Auth | Where | Details |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | OpenAI embeddingsOpenAI · HTTP API | Embeddings | BB | 73.4 | 4.5 (2) | n/a | n/a | Pay per use | API key | Hosted | ||
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OpenAI's text embedding API, with adjustable output dimensions for search and retrieval applications. Top strength text-embedding-3-small at $0.02 per million tokens, $0.01 through the Batch API Top weakness No new embedding model since 25 January 2024, and the docs still give a September 2021 knowledge cutoff |
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| 69 | Cohere Embed and RerankCohere · HTTP API | Embeddings | BB | 72.5 | 3.5 (2) | n/a | n/a | $2 / 1k req | API key | Hosted | ||
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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. Top strength Rerank 4 Pro and Fast with 32K context and top_n, tracked per model on the status page Top weakness Terms, training notice and security page disagree on whether API data trains models or goes to third parties |
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| 90 | Gemini EmbeddingGoogle · HTTP API | Embeddings | BB | 71 | 3.0 (2) | n/a | n/a | Freemium | API key | Hosted | ||
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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. Top strength Text, images, video, audio and PDFs interleaved in one request and one vector space Top weakness $0.20 per million text tokens, against $0.02 for OpenAI's small model |
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| 230 | Jina Embeddings and RerankerJina AI (Elastic) · HTTP API | Embeddings | C | 61.3 | 3.0 (2) | n/a | n/a | Freemium | API key | Hosted | ||
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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. Top strength jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap Top weakness No price per token in any currency on the public pages |
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| 273 | Voyage AI embeddings and rerankersVoyage AI (MongoDB) · HTTP API | Embeddings | C | 59 | 4.0 (2) | n/a | n/a | Freemium | API key | Hosted | ||
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Embedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI. Top strength 200 million free tokens per current model, then $0.02 to $0.12 per million Top weakness Training on customer data is the default, and the opt-out needs a card on file and is one way |
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| 283 | Mistral Embed and Codestral EmbedMistral AI · HTTP API | Embeddings | C | 58.2 | 3.5 (2) | n/a | n/a | Freemium | API key | Hosted | ||
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Mistral's API for generating text and code embeddings. Top strength EU and US regional endpoints and a French legal entity Top weakness Embedding API uptime of 94.36 per cent over 90 days on Mistral's status page, with incidents on 12 and 27 August 2026 |
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| 450 | ZeroEntropy zerank and zembedZeroEntropy · HTTP API | Embeddings | F | 13.8 | 1.0 (2) | n/a | n/a | Pay per use | API key | Hosted | ||
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Discontinued retrieval API acquired by Notion. Its embedding and reranking models remain available as open weights for self-hosting. Top strength All four models now open weights under Apache 2.0 on Hugging Face Top weakness The hosted API was discontinued after 4 September 2026 and signups closed on 24 July 2026 |
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Nothing matches these filters. .
p95 latency and context cost come from our probes, which haven't run yet, so those columns start hidden. Grades run from AA to F, and agent-ready means BB or better. Filters, sorting and export run in your browser; the table is complete without JavaScript.
Indexed, not reviewed (3)
Listings sorted into this category from public catalogues (the official MCP registry, APIs.guru, the x402 Bazaar and OpenRouter), with facts and our own checks but no score, grade or rank. How the index works.
| Listing | Kind | What it does | Why it's here |
|---|---|---|---|
| AI Wave aiwave.elopstudio.com | MCP server | AI model releases, price changes and deprecations in one feed: chat, embedding, speech, video. | vendor's own |
| apple-rag.com MCP server apple-rag.com | MCP server | Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients | vendor's own |
| forge voxell.ai | MCP server | MCP server for Forge, Voxell's embedding API. Voxell's Ingot-8B-R3 ranks #1 for English on MTEB. | vendor's own |
How we test this category
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. This test hasn't run yet, so Task success is pending and the grades here come from the categories assessed from public evidence.
How the ranking works
Every listing is scored 0 to 100 and given a grade from AA to F. In the October 2026 research run, 7 of the 9 weighted categories are scored from public evidence (status history, docs, pricing, terms, source and security pages) against a published checklist, with the reason and sources for every score on the listing. Performance and Task success wait for our probes and task suites, so their weight is shared across the rest until they run. Negative events deduct up to 15 points. Read the methodology.
For companies
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