Why is indexing a one-time cost but queries recurring?

You embed your corpus once to build the vector index, so that is a single upfront cost. Every search embeds the query text, so query cost recurs with usage. Both are shown separately.

What is the quality-adjusted score?

It divides each provider's cost by a relative retrieval-quality weight so cheaper-but-weaker models are not unfairly favoured. Lower is better. The weights are editable estimates based on public benchmark standings.

How are word counts converted to tokens?

The tool uses roughly 1.3 tokens per English word. Embedding APIs bill per token, so a 500-word document is about 650 tokens.

Are the prices exact?

They are editable presets from published list prices and clearly labelled as estimates. Providers update pricing, so confirm current rates before committing to a corpus-scale embed job.

Is my data sent anywhere?

No. The comparison runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

Cohere vs OpenAI Embeddings Cost Comparison

Cohere vs OpenAI embeddings cost comparison

Embedding a large corpus is a real expense, and the cheapest provider depends on your scale and quality bar. Enter your document count, average length, and daily query volume, and this tool prices Cohere Embed v3, OpenAI’s text-embedding-3 large and small, and Voyage AI — with a one-time indexing cost, recurring monthly query cost, and a quality-adjusted score.

How it works

tokens_per_doc = avg_words × 1.3
index_cost     = (doc_count × tokens_per_doc / 1,000,000) × price
query_cost/mo  = (queries_per_day × 30 × query_tokens / 1,000,000) × price

Indexing is a single upfront charge to vectorise your whole corpus; query cost recurs because each search embeds the incoming query. The quality-adjusted score divides total first-month cost by a relative retrieval-quality weight, so a model that is 20% cheaper but noticeably weaker does not automatically win.

Tips

Small models go far. OpenAI’s text-embedding-3-small is dramatically cheaper and is enough for many RAG workloads — test recall before paying for large.
Index cost dominates at scale. For millions of documents the one-time embed dwarfs query cost, so model price matters most there.
Re-embedding is expensive. Switching providers means re-indexing the whole corpus — choose deliberately, not just on this month’s price.