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. Both cover 200+ languages, are tuned on 52 languages, handle 32K-token context (64x R1), and add code retrieval across 9 programming languages.

In this post: Enterprise-Ready by Design · A Strong Sub-100M Multilingual Model · What Changed from R1 · Training the Full-Size 311M Model · Building the compact 97M Multilingual Model · Benchmark Results · Matryoshka Embeddings · Deployment Options · For Framework Integrators · Which Model Should You Use? · Try The Models

Multilingual embedding models face a persistent tension: broad language coverage usually comes at the cost of model size, and small models usually sacrifice languages. If you work across languages — retrieval-augmented generation over multilingual corpora, cross-lingual search, code retrieval in international teams — you've likely had to choose between a model that's fast enough and one that's good enough.

The Granite Embedding Multilingual R2 release narrows that gap considerably. We're releasing two new multilingual embedding models:

• granite-embedding-311m-multilingual-r2 — A 311M-parameter full-size model with 768-dimensional embeddings, Matryoshka dimension support, and top-tier multilingual retrieval quality.
• granite-embedding-97m-multilingual-r2 — A 97M-parameter compact model with 384-dimensional embeddings that delivers strong retrieval quality for its size.

52 enhanced-support languages (click to expand)

The underlying encoder was pretrained on text from 200+ languages, producing general-purpose embeddings for any of them. The following 52 languages receive explicit retrieval-pair and cross-lingual training for higher-quality retrieval:

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