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Comparison of Intfloat: E5-Large-v2, Intfloat: E5-Base-v2, gte-base, Qwen3-Embedding-8B
4 modelsintfloatIntfloat: E5-Large-v2Remove ×intfloatIntfloat: E5-Base-v2Remove ×thenlpergte-baseRemove ×QwenQwen3-Embedding-8BRemove ×
What’s differentThe e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic…The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as…The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE…The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3…
DateNov 22, 2023ListedNov 22, 2023ListedNov 22, 2023ListedJun 10, 2025Listed
API prices USD · provider configuration shown below
Listed rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →1 endpoint →1 endpoint →
Benchmarks Each result retains its evaluation configuration
FreshStack · MTEB 1.38.43 ↗MTEB 1.38.43; test/default; dataset 7a20df1aNot reportedNot reportedNot reported0.43017 ↗Revision 4e423935; test/defaultContributor reported
Specifications
Context window51251251241K
Max output46046046032K
Parameters———8B
Open weightsSupportedSupportedSupportedSupported