GTE-Large
335M BERT-based text embedding model, a long-time retrieval baseline.
About
GTE-Large is Alibaba DAMO Academy's 335-million-parameter general text embedding model, part of the GTE family trained with multi-stage contrastive learning on large-scale relevance pairs. With 1024-dimensional embeddings from a BERT-based transformer, it became a standard open baseline for semantic search and retrieval-augmented generation.
It outperformed OpenAI's text-embedding-ada-002 on MTEB-style evaluations despite being far smaller, and the thenlper checkpoints remain widely used in local RAG stacks. The weights are released under Apache 2.0.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 130; Trending score 74/100 from researcher assessment.