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intfloat

Intfloat: Multilingual-E5-Large

The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and large-scale data search. Priced at $0.01 per M tokens. 512 token context window.

View source on OpenRouter Hugging Face
Overview

Model specifications

Context
512 tokens
Maximum output
460 tokens
Architecture
text->embeddings
Tokenizer
Other
Knowledge cutoff
Not provided
Moderated
No
OPENROUTER

Complete pricing

Synchronized OpenRouter rates. Token prices are shown per one million tokens.

Input
$0.01
/M tokens
API

Quick start

Call this exact model ID through OpenRouter’s OpenAI-compatible API.

curl https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"intfloat/multilingual-e5-large","input":"Your text here"}'
Capabilities

Input → Output

Input
text
Output
embeddings
API

Supported API parameters

frequency_penaltymax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstoptemperaturetop_ktop_p
Available providers

1 Provider

Live providers on OpenRouter

Provider availability, latency, throughput and routing can change continuously. Open the source page for current operational data.

OpenRouter

DeepInfra

fp32
Context
1K
Maximum output
0K
Input
$0.010
Output
Free
Cache read
Not provided
Cache write
Not provided
intfloat

models

All models
intfloat logointfloat

Intfloat: E5-Large-v2

The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.

Context
1K
Input
text
Output
embeddings
Input: $0.010Output: Freeper 1M tokens
View model details →
intfloat logointfloat

Intfloat: E5-Base-v2

The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring,...

Context
1K
Input
text
Output
embeddings
Input: $0.0050Output: Freeper 1M tokens
View model details →