Qwen3 Embedding series introduces a new set of models designed for text embedding, retrieval, and reranking tasks, leveraging the advanced multilingual capabilities of the Qwen3 foundation model. These open-sourced models demonstrate state-of-the-art performance in multiple benchmarks and provide flexibility in size and functionality for various applications. The series aims to enhance text understanding and retrieval efficiency, with ongoing optimizations planned for future development.

Google has launched the Gemini Embedding model (gemini-embedding-001), now available to developers via the Gemini API and Vertex AI, showcasing superior performance on the Massive Text Embedding Benchmark. This versatile model supports over 100 languages and features flexible output dimensions, allowing developers to optimize for performance and cost. Users are encouraged to migrate from older models before their deprecation dates, with enhanced features like Batch API support coming soon.

EmbeddingGemma is a 300M parameter embedding model developed by Google DeepMind, designed for generating vector representations of text for various tasks such as search, classification, and semantic similarity. It supports over 100 languages and is optimized for deployment in resource-constrained environments, making advanced AI accessible to a wider audience. Users must agree to Google's usage license to access the model via Hugging Face.