Multilingual E5 Large

huggingface
Embeddings

Microsoft E5 multilingual embedding model with 560M parameters, initialized from XLM-RoBERTa-large and trained with weakly supervised contrastive learning. Covers around 100 languages and returns 1024-dim vectors. It expects query: and passage: prefixes on inputs and is a popular open model for multilingual semantic search.

Embed with Multilingual E5 Large
Vectorize text and preview the first 8 dimensions as a bar chart.
Sign in to try this model with €5 free credits.
Sign in
Outputs a high-dimensional vector you can plug into RAG or search.
Vector preview appears here.
TL;DR·Last updated June 24, 2026

Multilingual E5 Large is embeddings AI model from huggingface, priced at €0.000 per 1M input tokens with a 512 tokens context window.

About this model

multilingual-e5-large from Microsoft Research is built on XLM-RoBERTa-large and trained with a contrastive objective over large weakly supervised text-pair data followed by supervised fine-tuning. It covers roughly 100 languages and outputs 1024-dim embeddings with a 512-token limit. The E5 convention requires prefixing inputs with query: for search queries and passage: for documents, which the model relies on to separate the two roles. It is a strong, widely adopted open baseline for multilingual retrieval and clustering. Served through the Hugging Face feature-extraction pipeline; mean-pool the token outputs to get the sentence embedding.
Try Multilingual E5 Large
Sign in to generate — 50 free credits on sign-up

Pricing

Price per Generation
Per generation€1.00

API Integration

Use our OpenAI-compatible API to integrate Multilingual E5 Large into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const vectors = await rw.run("multilingual-e5-large", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions

// Or use the embed() method for full control
const res = await rw.embed("multilingual-e5-large", ["Hello", "World"]);
for (const item of res.data) {
  console.log(item.embedding.length);
}
Specifications
Price
€1.00
Context window
512 tokens
Developer
huggingface
Category
Embeddings
Supported Formats
text
Tags
embedding
retrieval
rag
huggingface
e5
intfloat
microsoft
multilingual
open-weights

Frequently asked questions

Related Models

View all Embeddings

BGE Large EN v1.5

huggingface

BAAI (Beijing Academy of AI) open-weight English embedding model with 335M parameters. Returns 1024-dim vectors and was a top MTEB English retrieval model on release. The v1.5 update improved similarity distribution so it works well without a query instruction prefix for symmetric tasks. A widely used open alternative to hosted embeddings.

€1.00

BGE-M3 (Multilingual)

huggingface

BAAI multilingual embedding model covering 100+ languages with an 8192-token context. M3 stands for its multi-functionality (dense, sparse and ColBERT-style multi-vector retrieval), multilinguality and multi-granularity over long documents. Returns 1024-dim dense vectors and is a strong open choice for cross-lingual and long-text retrieval.

€1.00

ESM-2 650M (Protein Embeddings)

huggingface

Meta AI 650M-parameter protein language model trained on UniRef50 sequences. Feed it an amino-acid sequence and the per-residue hidden states act as learned protein embeddings, used for structure prediction, variant-effect and function tasks. This 33-layer checkpoint is the common balance of quality and cost in the ESM-2 family.

€2.00

Nomic Embed Text v1.5

huggingface

Nomic AI open embedding model with a fully reproducible training pipeline (open weights, data and code). Supports an 8192-token context and Matryoshka representation learning, so you can truncate the 768-dim output down to 64 dims with graceful quality loss. Uses task prefixes like search_query and search_document.

€1.00

Start using Multilingual E5 Large today

Get started with free credits. No credit card required. Access Multilingual E5 Large and 100+ other models through a single API.