Embedding Model Selection

Name: OpenModels
Creator: OpenModels
License: https://github.com/openmodelsrun/openmodels

IntermediatedataMinimum 32K context

Selects an embedding model for a retrieval workload by weighing retrieval quality, dimensionality and index cost, max sequence length, multilingual and domain coverage, and hosted versus self-hosted tradeoffs. Includes building a small domain-specific benchmark instead of trusting leaderboards, and planning the re-embedding migration when the model changes.

embeddings vector-search retrieval benchmarking model-selection rag

Use cases

Comparing embedding models for a specific domain corpus
Trading off vector dimensionality against index size and cost
Building a small labeled benchmark to evaluate candidates
Planning a re-embedding migration without downtime

Example prompt

Help me choose an embedding model for semantic search over 2 million multilingual support
tickets, served from a managed vector database.

Shortlist candidates and compare them on retrieval quality, dimensionality and storage cost,
max input length, and multilingual coverage. Describe how to build a small labeled benchmark from
our own tickets to decide, and outline the re-embedding migration plan if we switch later.

Recommended models

claude-opus-5 gemini-3-6-flash gpt-5-6-sol

Compatible tools

claude-codecursorkiroany

Modalities

Input: text, code

→

Output: text, code

Author

OpenModels Community

@openmodelsrun