Model family
E5 Models
E5 models are widely used for multilingual embeddings, semantic search, retrieval, low-overhead indexing, and RAG pipelines.
Best for
Embedding
Use this family hub to compare E5 variants for embedding workflows, then open the detail page for deeper deployment notes.
RAG
Use this family hub to compare E5 variants for rag workflows, then open the detail page for deeper deployment notes.
Multilingual
Use this family hub to compare E5 variants for multilingual workflows, then open the detail page for deeper deployment notes.
Search
Use this family hub to compare E5 variants for search workflows, then open the detail page for deeper deployment notes.
Variants
E5 models grouped by workflow
Embedding and reranking
Multilingual E5 Large
Microsoft / intfloat · E5
Best for: Teams building multilingual retrieval, semantic search, and RAG pipelines.
Local: Runs locally for many embedding and semantic search prototypes on CPU or modest GPU hardware.
e5-mistral-7b-instruct
Microsoft / intfloat · E5
Best for: Teams comparing larger instruction-tuned embedding models for retrieval quality when smaller E5 checkpoints are not enough.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
multilingual-e5-large-v2
Microsoft / intfloat · E5
Best for: Teams building multilingual search or RAG where one English-only embedding model is not enough.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-large-v2
Microsoft / intfloat · E5
Best for: Teams that want a strong English retrieval baseline for search and RAG before moving to multilingual or larger embedding experiments.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-base-v2
Microsoft / intfloat · E5
Best for: Builders who want a balanced English embedding model for search and RAG on moderate local or hosted infrastructure.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-small-v2
Microsoft / intfloat · E5
Best for: Local or cost-sensitive embedding pipelines that need a practical English retrieval model without the overhead of larger E5 releases.
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-large
Microsoft / intfloat · E5
Best for: Legacy large embedding baseline
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-base
Microsoft / intfloat · E5
Best for: Legacy base embedding baseline
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
e5-small
Microsoft / intfloat · E5
Best for: Legacy lightweight embedding baseline
Local: Use the exact checkpoint and quantization that matches your hardware and latency target.
Compare
All E5 models in the directory
| Model | Type | Best for | Local runner notes | License | Detail |
|---|---|---|---|---|---|
| Multilingual E5 Large | Embedding | Teams building multilingual retrieval, semantic search, and RAG pipelines. | Runs locally for many embedding and semantic search prototypes on CPU or modest GPU hardware. | MIT | Open |
| e5-mistral-7b-instruct | Embedding | Teams comparing larger instruction-tuned embedding models for retrieval quality when smaller E5 checkpoints are not enough. | Use the exact checkpoint and quantization that matches your hardware and latency target. | MIT | Open |
| multilingual-e5-large-v2 | Embedding | Teams building multilingual search or RAG where one English-only embedding model is not enough. | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |
| e5-large-v2 | Embedding | Teams that want a strong English retrieval baseline for search and RAG before moving to multilingual or larger embedding experiments. | Use the exact checkpoint and quantization that matches your hardware and latency target. | MIT | Open |
| e5-base-v2 | Embedding | Builders who want a balanced English embedding model for search and RAG on moderate local or hosted infrastructure. | Use the exact checkpoint and quantization that matches your hardware and latency target. | MIT | Open |
| e5-small-v2 | Embedding | Local or cost-sensitive embedding pipelines that need a practical English retrieval model without the overhead of larger E5 releases. | Use the exact checkpoint and quantization that matches your hardware and latency target. | MIT | Open |
| e5-large | Embedding | Legacy large embedding baseline | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |
| e5-base | Embedding | Legacy base embedding baseline | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |
| e5-small | Embedding | Legacy lightweight embedding baseline | Use the exact checkpoint and quantization that matches your hardware and latency target. | Check exact model card | Open |