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.

Understanding the trade-offs between models

Embedding models differ on three axes that affect the right choice for your use case:

Dimensionality and storage. Larger models typically produce higher-dimensional vectors. OpenAI text-embedding-3-large outputs 3,072 dimensions (reducible with the dimensions parameter); text-embedding-3-small outputs 1,536. Cohere Embed v3 can output 1,024 dimensions. Higher dimensions take more storage in your vector database and slow nearest-neighbour search, so smaller models can be faster at retrieval even when their quality is similar.

Multilingual support. If your corpus is multilingual, Cohere Embed v3 was built with multilingual retrieval in mind. OpenAI’s models also handle many languages but were primarily trained on English data.

Retrieval vs. similarity. Some models are tuned for asymmetric retrieval (short query vs. longer document) rather than symmetric similarity (comparing documents of similar length). For RAG workloads — where queries are short and documents are long — asymmetric retrieval quality matters most.

Worked example

For a corpus of 50,000 documents averaging 800 words, with 500 queries per day:

• Tokens per document: 800 × 1.3 = 1,040
• Total index tokens: 50,000 × 1,040 = 52 million tokens
• Monthly query tokens: 500 × 30 × 100 (assuming short queries) = 1.5 million tokens

The tool shows how that breaks into an upfront indexing cost and a recurring monthly query cost for each provider, so you can see whether a quality premium pays for itself at your scale.

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.
• Test recall on your own data. Public benchmarks are a guide, not a verdict. Different corpora have different embedding difficulty; always test with a sample of your actual documents before committing to a provider at scale.