Best Embedding Models for RAG in 2025

We evaluated each ai processing solution against these criteria: Retrieval accuracy (MTEB) — a critical factor for production deployments. Multilingual support — a critical factor for production deployments. Cost per million tokens — a critical factor for production deployments. Fine-tuning options — a critical factor for production deployments. Ease of integration — a critical factor for production deployments. Each criterion was weighted based on its importance to teams building RAG applications at scale. Our evaluation methodology is transparent and reproducible. Each solution was tested with identical datasets across multiple use cases including document search, question answering, and multi-modal retrieval. We measured query latency at various percentiles (p50, p95, p99), recall at different k values, and indexing throughput for datasets ranging from 10K to 10M vectors. The results reflect real-world performance rather than synthetic benchmarks that may not translate to production conditions.

Best general-purpose embedding model with excellent out-of-box performance. Pros: Strong general performance, Matryoshka dimensions, Simple API. Cons: Closed source, No fine-tuning, Higher cost for large volumes. OpenAI text-embedding-3-large is a strong choice for teams that prioritize strong general performance and can work around closed source.

Best for multilingual RAG applications with fine-tuning needs. Pros: Excellent multilingual, Input type optimization, Fine-tuning available. Cons: Requires input type selection, Newer model, Smaller community. Cohere embed-v3.0 is a strong choice for teams that prioritize excellent multilingual and can work around requires input type selection.

Best for domain-specific RAG where specialized embeddings matter. Pros: Domain-specific models, Code embeddings, Legal embeddings. Cons: Smaller company, Fewer integrations, Limited documentation. Voyage AI voyage-large-2 is a strong choice for teams that prioritize domain-specific models and can work around smaller company.

Best open-source option for teams wanting full control over embeddings. Pros: Open source, Self-hosted, Strong benchmarks. Cons: English-focused, Requires GPU for inference, No managed API. BGE-large-en-v1.5 is a strong choice for teams that prioritize open source and can work around english-focused.

At a glance: OpenAI text-embedding-3-large (ranked #1) excels at strong general performance. Cohere embed-v3.0 (ranked #2) excels at excellent multilingual. Voyage AI voyage-large-2 (ranked #3) excels at domain-specific models. BGE-large-en-v1.5 (ranked #4) excels at open source. The best choice depends on your specific requirements, team expertise, and infrastructure constraints.

IngestIQ integrates with all the ai processing solutions listed above. Use IngestIQ as your data ingestion and processing layer, then route vectors to whichever ai processing solution fits your needs. This decoupled architecture means you can switch between options without rebuilding your pipeline.