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sentence-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, similarity scoring, and text ranking. Priced at $0.005 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
$0.005
/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":"sentence-transformers/all-minilm-l6-v2","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

unknown
Context
1K
Maximum output
0K
Input
$0.0050
Output
Free
Cache read
Not provided
Cache write
Not provided
sentence-transformers

models

All models
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Sentence Transformers: paraphrase-MiniLM-L6-v2

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...

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