Модели

The Qwen3 Embedding model series is the newest proprietary addition to the Qwen family, purpose-built for text embedding and ranking applications. Leveraging the strong multilingual abilities, long-context comprehension, and reasoning prowess of its base model, Qwen3 Embedding delivers impressive progress across various embedding and ranking tasks. These include text retrieval, code search, text classification, clustering, and bitext mining.

Gemini-Embedding-001 is Google’s top-ranked multilingual embedding model, supporting over 100 languages and flexible output dimensions (3072, 1536, or 768). It is optimized for semantic search, clustering, and recommendations, and leverages Matryoshka Representation Learning for efficient, high-quality embeddings.

Text-Embedding-3-Small is OpenAI’s efficient, compact embedding model, designed to convert text into numerical representations for semantic tasks such as search, clustering, and recommendations. It offers improved performance and cost-effectiveness compared to previous models, with low latency and storage requirements.

Text-Embedding-3-Large is OpenAI’s most capable embedding model, supporting both English and non-English text tasks. It produces high-dimensional embeddings (up to 3072 dimensions) for advanced semantic similarity, search, and clustering, and allows flexible trade-offs between performance and resource usage.