thenlper

Access 2 thenlper models on OpenRouter including GTE-Base and GTE-Large. Compare pricing, context windows, and capabilities.

thenlper tokens processed on OpenRouter

Thenlper: GTE-BaseGTE-Base
245M tokens
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.
by thenlperNov 18, 2025512 context$0.005/M input tokens$0/M output tokens

thenlper

Access 2 thenlper models on OpenRouter including GTE-Base and GTE-Large. Compare pricing, context windows, and capabilities.

thenlper tokens processed on OpenRouter

Thenlper: GTE-BaseGTE-Base
245M tokens
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.
by thenlperNov 18, 2025512 context$0.005/M input tokens$0/M output tokens

Thenlper: GTE-LargeGTE-Large

6.98M tokens

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 clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.

by thenlperNov 18, 2025512 context$0.01/M input tokens$0/M output tokens