BGE Large EN v1.5
BGE Large EN v1.5 is the 335M-parameter English flagship from BAAI's FlagEmbedding family, producing 1024-dim embeddings with a 512-token context window. Released in late 2023 under MIT license, it became the de facto MTEB benchmark anchor and remains one of the most-downloaded sentence-similarity models on the Hub.
The MTEB reference point that everyone benchmarks against. Still excellent for short-chunk English RAG but the 512-token ceiling shows its age — if you're starting fresh in 2026, nomic-v1.5 or gte-modernbert-base give you 8K context with comparable quality. Keep BGE if you're already paired with bge-reranker-v2-m3.
Overview
BGE Large EN v1.5 is the 335M-parameter English flagship from BAAI's FlagEmbedding family, producing 1024-dim embeddings with a 512-token context window. Released in late 2023 under MIT license, it became the de facto MTEB benchmark anchor and remains one of the most-downloaded sentence-similarity models on the Hub.
Strengths
- 1024-dim dense embeddings with strong MTEB English score (~64.2)
- MIT license — no commercial restrictions, training data sources documented
- Massive ecosystem support: native in sentence-transformers, llama.cpp, ONNX, OpenVINO
- v1.5 dropped the similarity-distribution issue of v1 — usable without prefix tuning
Weaknesses
- Only 512-token context — chunking required for any real document
- English-only; use bge-m3 or multilingual-e5 for non-English corpora
- Architecture (BERT-large) is older — newer 8K-context models match quality at lower latency
- No Matryoshka support — full 1024 dims always required at storage
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.2 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of BGE Large EN v1.5.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run BGE Large EN v1.5?
Can I use BGE Large EN v1.5 commercially?
What's the context length of BGE Large EN v1.5?
Source: huggingface.co/BAAI/bge-large-en-v1.5
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify BGE Large EN v1.5 runs on your specific hardware before committing money.