Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

The Granite Embedding Multilingual R2 release introduces two new Apache 2.0 licensed multilingual embedding models, offering significant advancements in retrieval quality across 200+ languages and code. The compact 97M-parameter model, built on ModernBERT architecture, achieves outstanding retrieval scores, outperforming other sub-100M parameter models on the MTEB Multilingual Retrieval benchmark, while the full-size 311M-parameter model ranks second among open models under 500M parameters. These models, which feature 32K-token context handling and cross-lingual code retrieval, are designed to be enterprise-ready, having been trained with IBM-curated datasets and stringent governance processes to ensure commercial use safety. They are compatible with sentence-transformers and other frameworks like LangChain, LlamaIndex, Haystack, and Milvus, requiring no task-specific instructions for integration. The models also support Matryoshka Representation Learning, allowing flexible embedding dimensions to optimize storage and computation costs. These innovations represent a leap forward in multilingual model efficiency, offering robust performance in cross-lingual, code, and long-document retrieval tasks.