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Thenlper: GTE-Base

Source description (English)

The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.

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":"thenlper/gte-base","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
thenlper

1 models

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Thenlper: GTE-Large

The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and...

Context
1K
Input
text
Output
embeddings
Input: $0.01per 1M tokens
View model details →