OpenRouter released a comprehensive evaluation of 19 embedding models via its API, providing scenario-specific recommendations for RAG, multilingual, code, and multimusearch. Key findings suggest openai/text-embedding-3-small for English RAG, voyage-code-4 for code search, and qwen3-embedding-8b for multilingual tasks. The guide details pricing, context limits, and dimensionality, emphasizing that vectors from different model families are incompatible and require index rebuilding. It offers practical advice on cost optimization, testing methodologies, and deployment best practices to reduce trial-and-error for developers.