Stella is a compact but capable text embedding specialist — it punches above its weight class for a 1.5B parameter model, producing dense vector representations useful for semantic search, retrieval, and similarity tasks. It handles long contexts up to 131k tokens, making it practical for embedding lengthy documents without chunking. The trade-off is that, like all embedding models, it produces vectors rather than generated text, so it's a retrieval tool, not a conversational one.