Granite Embedding Multilingual R2
Multilingual text embeddings with 32,768-token context.
IBM's second-generation multilingual Granite embedding model family, comprising multilingual text-embedding variants based on ModernBERT and designed for retrieval and related embedding tasks.
Pricing
IBM publishes a single embedding-model rate of USD 0.10 per million tokens; the pricing page does not break out separate input and output token prices for this model.
IBM's official watsonx.ai pricing page states that all embedding models are available for USD 0.10 per million tokens. IBM's Granite Embedding English Reranker r2 model card identifies the r2 model as a text-embedding/reranking model in the Granite Embeddings collection. No separate public r2-specific price line was found, so the published embedding-model rate applies.
Model Intelligence
Recent stories
VAGOsolutions released multilingual retrievers under Apache 2.0, ranging from 1.5M to 115M parameters. The team reports 8× encoding throughput versus Qwen3-Embedding-0.6B for the largest model.
IBM released 97M and 311M multilingual Granite Embedding R2 models under Apache 2.0, replacing XLM-RoBERTa with ModernBERT and extending context length from 512 to 32,768 tokens. The 311M model posts a +11.8 gain on MMTEB retrieval and ships with ONNX, OpenVINO, vLLM, and GGUF support.