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

DevFeed: [Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context -- Best Sub-100M Retrieval Quality](<https://devfeed.tech/articles/granite-embedding-multilingual-r2-open-apache-2-0-multilingual-embeddings-with-32k-context-best-sub-100m-retrieval-quality-7260.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ibm-granite/granite-embedding-multilingual-r2>)

Author: Radu Florian; Parul Awasthy; Aashka Trivedi; Madison Lee

Published: 2026-05-14T18:55:01Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [frameworks](<https://devfeed.tech/tags/frameworks.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [llamaindex](<https://devfeed.tech/tags/llamaindex.md>), [model](<https://devfeed.tech/tags/model.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>)

## AI overview

Granite Embedding Multilingual R2 introduces two Apache 2.0 multilingual embedding models: a compact 97M-parameter model and a 311M full-size model. They support more than 200 languages, 32K-token contexts, code retrieval across nine programming languages, and integration with popular retrieval frameworks.

## Source excerpt

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context -- Best Sub-100M Retrieval Quality TL;DR: Two new Apache 2.0 multilingual embedding models built on ModernBERT -- a 97M-parameter compact model that beats every open sub-100M multilingual embedder on MTEB Multilingual Retrieval (60.3), and a 311M full-size model that scores 65.2 on MTEB Multilingual Retrieval (#2 among open models under 500M parameters) with Matryoshka support.