Model comparison ยท 2026-09-22

Turkish embedding models compared: choosing for RAG and search

How to choose an embedding model for Turkish RAG and search systems, comparing dimension, context length, multilinguality, and cost, and how to test with your own data.

Diagram of a RAG pipeline showing a Turkish document set converted to vectors by an embedding model, then passing through search and reranking steps.

Not the same question as comparing chat models

The criteria covered in the Turkish LLM comparison guide look at generated text quality. Choosing an embedding model measures something different: how close two Turkish sentences with the same meaning sit in vector space, and how far an unrelated sentence sits from them. A chat model with high generation quality is not necessarily a good embedding model; the two have different training objectives.

For this reason, a team building a RAG pipeline should treat the chat model and the embedding model as two independent decisions; there is no requirement that both come from the same provider.

Four things to check

Vector dimension directly affects storage and search cost; a larger dimension usually means a more detailed representation, but vector database cost rises with it too. Context length determines how much text fits into a single embedding call; your chunking strategy is built around that limit.

Multilinguality matters for mixed-content document sets (Turkish and English together); a model trained only for Turkish can unexpectedly underperform on technical documents that include English terminology. Cost should be calculated as the per-million-token price multiplied by total document volume during initial indexing; embedding cost is usually the sum of a one-time load plus ongoing query volume.

  • Vector dimension: affects storage and search cost.
  • Context length: sets the upper bound of your chunking strategy.
  • Multilinguality: matters for mixed Turkish-English content.
  • Cost: account for indexing volume and query volume separately.

Don't decide without measuring on your own data

A general-purpose benchmark score does not guarantee real performance on your own document set. Preparing a small sample query-answer set (for example, 30-50 real user questions with the correct document match) and comparing candidates on it with a simple metric like recall@k is a more reliable selection method than trusting general scores. Adding a reranking step, which reorders the initial embedding search results, usually improves hit rate.

Frequently asked questions

If a provider's chat model performs well, should I also use its embedding model?

Not necessarily. Embedding quality requires a separate evaluation; choosing the chat model and the embedding model from different providers is not a technical problem as long as both are reachable through a single API.

Does a rerank step replace embedding search?

No, it complements it. Embedding search quickly narrows a large candidate set; reranking reorders those candidates with a more detailed model to surface the most relevant results. The two are used together.

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