ZeroReader BGE-M3 Multilingual Embeddings

x402

ZeroReader BGE-M3 Multilingual Embeddings is a paid API for AI agents from api.zeroreader.com, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-02).

Generates dense vector embeddings for text using the BGE-M3 multilingual model, supporting 100+ languages

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

BGE-M3 (Multilingual) — Best multilingual embedding model. 100+ languages.

Output

Returns a JSON object with a list of embedding objects, each containing an index, object type 'embedding', and a dense floating-point vector array (e.g. [0.1, 0.2, 0.3, ...]) representing the semantic content of the input text. Supports single string or batch array input.

Example use cases

Example prompt

“Embed this sentence using the BGE-M3 multilingual model so I can store it in my vector database for semantic search: '人工知能は現代社会を大きく変えている。'”

Technical details

Endpoint

POSThttps://api.zeroreader.com/v1/ai/embed-bge-m3?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need high-quality embeddings that work across 100+ languages, especially for multilingual or cross-lingual semantic search, RAG pipelines, or clustering. Prefer over English-only models (like BGE Large EN v1.5) when your text corpus includes non-English content such as Japanese, Chinese, Arabic, or mixed-language documents.

Side effects

Invalid input type — text must be a string or array of strings
Empty text input returns error or zero-length embedding
Oversized input exceeding model token limit returns truncation or error
Network timeout for very large batches
Payment failure (x402) blocks request if USDC balance is insufficient

Input

Schema · Request body

Field	Type	Description
text	—

Output

Schema · Response

{
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.1,
        0.2,
        0.3
      ]
    }
  ],
  "object": "list"
}

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