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sentence-transformers

Sentence Transformers: paraphrase-MiniLM-L6-v2

Source description (English)

The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval...

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
$0.005
per 1M tokens
API

Quick start

Set OPENROUTER_API_KEY locally. Python requires requests; JavaScript runs in Node.js. Keep the key on the server.

API documentation

curl --fail-with-body https://openrouter.ai/api/v1/embeddings \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"sentence-transformers/paraphrase-minilm-l6-v2","input":"Your text here"}'
Capabilities

Input → Output

Input
text
Output
embeddings
API

Supported API parameters

Available providers

1 Provider

Checked: September 16, 2026

Live providers on OpenRouter

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

OpenRouter

DeepInfra

Not provided
Context
1K
Maximum output
0K
Input
$0.005
Output
Free
Cache read
Not provided
Cache write
Not provided
sentence-transformers

4 models

All models
sentence-transformers logosentence-transformers

Sentence Transformers: all-MiniLM-L12-v2

The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and...

Context
1K
Input
text
Output
embeddings
Input: $0.005per 1M tokens
View model details →
sentence-transformers logosentence-transformers

Sentence Transformers: multi-qa-mpnet-base-dot-v1

The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.

Context
1K
Input
text
Output
embeddings
Input: $0.005per 1M tokens
View model details →
sentence-transformers logosentence-transformers

Sentence Transformers: all-mpnet-base-v2

The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and...

Context
1K
Input
text
Output
embeddings
Input: $0.005per 1M tokens
View model details →
sentence-transformers logosentence-transformers

Sentence Transformers: all-MiniLM-L6-v2

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering,...

Context
1K
Input
text
Output
embeddings
Input: $0.005per 1M tokens
View model details →