e5-base-v2

Mid-sized E5 embedding checkpoint for English retrieval and semantic search with a more practical serving footprint than the large variant.

Microsoft / intfloat · E5

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (intfloat/e5-base-v2)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Builders who want a balanced English embedding model for search and RAG on moderate local or hosted infrastructure.

Who should use it

Builders who want a balanced English embedding model for search and RAG on moderate local or hosted infrastructure.
Builders who want local or self-hosted testing options.
RAG builders comparing retrieval quality, latency, and multilingual coverage.

Common workflows

Balanced embedding workflows
embedding workflows
rag workflows
semantic search workflows
retrieval workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

MIT. Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

Open weights model option for E5 workflows.
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.

Limitations

Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
Context window and limits: 512 tokens.
Verify the exact model card, provider docs, license, and serving support before production use.