⚡️ FlashAttention-4: up to 1.3× faster than cuDNN on NVIDIA Blackwell →

Introducing Together AI's new look →

🔎 ATLAS: runtime-learning accelerators delivering up to 4x faster LLM inference →

⚡ Together GPU Clusters: self-service NVIDIA GPUs, now generally available →

📦 Batch Inference API: Process billions of tokens at 50% lower cost for most models →

🪛 Fine-Tuning Platform Upgrades: Larger Models, Longer Contexts →

Models / BAAI
Embeddings

BGE-Large-EN v1.5

BAAI v1.5 large maps text to dense vectors for retrieval, classification, clustering, semantic search, and LLM databases.

Read our Docs
read docs

About model

BGE-Large-EN v1.5 generates high-quality English text embeddings using a BERT-based encoder architecture, ranking 1st on the MTEB benchmark at release. It excels at semantic retrieval and text similarity tasks. Suitable for developers and researchers requiring top-tier embedding performance for search and RAG pipelines.

    Related models
    Embeddings
    GTE ModernBERT base
    • Input/1M
      $0.08
    • Parameters
      149.0M
    No items found.
    Embeddings
    Embeddings
    BGE-Base-EN v1.5
    • Input/1M
      $0.01
    • Parameters
      109M
    No items found.
    Embeddings
    Embeddings
    UAE-Large v1
    • Parameters
      326M
    No items found.
    Embeddings
    Model specifications
    Model data
    • Model provider
      BAAI
    • Type
      Embeddings
    • Main use cases
      Embeddings
    • Deployment
      Monthly Reserved
    • Parameters
      335M
    • Context length
      512
    • Input price

      $0.02 / 1M tokens

    • Output price

      $0.02 / 1M tokens

    • Input modalities
      Text
    • Output modalities
      Structured Data
    • Released
      September 11, 2023
    • Last updated
      February 5, 2026
    • External link
      Provider docs
    • Category
      Embeddings
    Quickstart docs
    Deploy model