Cohere Embed v4 vs OpenAI text-embedding-3

Why this comparison usually starts

In my experience this pair gets compared for one of two reasons, and neither is a benchmark score. Either the corpus is full of documents that are really images, and someone has realised the extraction pipeline is going to be the expensive part. Or a compliance requirement has arrived stating that customer text may not transit a shared multi-tenant service, and someone is looking for a provider that offers private deployment.

Cohere Embed v4 answers both of those. OpenAI text-embedding-3 answers neither. If neither applies to you, this comparison is not the one you should be running, and the general selection framework will serve you better.

Side by side

The multimodal case, which is the real headline

Consider a corpus of ten thousand scanned supplier invoices, or a library of client slide decks, or engineering drawings with annotations. With a text-only embedding model, the work looks like this: OCR each page, detect layout so tables and columns do not turn into word salad, reconstruct reading order, extract the text, then embed. That pipeline is real engineering, it has its own failure modes, and it degrades quietly on the documents that are hardest to read.

With a multimodal embedding model, the page is embedded as a page. The chart is part of the vector. The table's spatial structure is part of the vector. There is no reading order to reconstruct because nothing was flattened into a string.

That is not a benchmark difference, it is the deletion of a subsystem, and it is why the price comparison is usually the wrong frame. The relevant comparison is Embed v4's price against the cost of building and maintaining an extraction pipeline, and against the quality loss that pipeline introduces. On document-heavy corpora the multimodal model routinely wins that comparison outright.

The private deployment case

The second reason this comparison runs is compliance. A hosted embedding API means your text leaves your infrastructure, and for some organisations that is simply not permissible, regardless of what the data processing agreement says.

Cohere offers private deployment, including inside a customer's own cloud environment, which keeps the text within a boundary the customer controls while still giving them a commercially supported model. That occupies a genuinely useful middle ground between a public API and self-hosting an open model, where you own everything including the pager.

Worth being precise about the alternative, though. If you are willing to self-host anyway, an open model such as BGE-M3 gives you the same sovereignty with no licence cost, at the price of owning the serving stack. The choice between private-deployment commercial and self-hosted open comes down to whether you want a vendor to call when it breaks. That trade-off sits at the centre of most GDPR-compliant and self-hosted LLM engagements I run.

Matryoshka dimensions, and why they are worth understanding

Both models let you use fewer dimensions than the model natively produces, and the reason to care is money rather than quality. Index size, memory footprint, and per-query search cost all scale with dimensions, so a vector database bill is substantially a function of how many numbers you chose to keep.

Matryoshka representation learning is what makes truncation safe: the vector is trained so that its first N dimensions are a coherent embedding on their own, not an arbitrary slice. In practice that means you can cut dimensions, measure the recall change on your own benchmark, and keep the reduction if the loss is acceptable. It is one of the few genuinely free-ish optimisations in a retrieval stack, and it is routinely left on the table.

When OpenAI is simply the right answer

If your corpus is plain English text, your data can go through a public API, and your volume is moderate, use OpenAI text-embedding-3-small. It is cheaper, it is the default in every framework, and the quality difference on that corpus will not decide anything about your product.

Spend the effort you save on chunking and on adding a reranker instead. Both of those will move your retrieval quality more than the choice between these two models will, on the corpora where this comparison is a close call at all.

Frequently asked questions

What is the main difference?

Cohere Embed v4 is multimodal and can embed images and mixed PDF pages directly; OpenAI text-embedding-3 is text-only and needs an extraction pipeline in front of it.

What are Matryoshka dimensions?

A training method that makes the leading dimensions of a vector usable on their own, so you can truncate to cut index cost without re-embedding and with a measurable, usually small, quality loss.

Can Embed v4 run in my own VPC?

Yes. Cohere offers private deployment including in a customer's own cloud environment. OpenAI has no equivalent for embeddings, which is often what decides the comparison.

Which is better for multilingual?

Cohere, generally, with more even coverage across a hundred-plus languages. Measure recall per language rather than averaged, or you will hide the exact weakness you are testing for.

Is the higher price worth it?

Yes if your corpus is documents-as-images, genuinely multilingual, or subject to private-deployment requirements. No if it is English plain text going through a public API either way.

Bottom line

Choose Cohere Embed v4 when your documents are really images, when your corpus is multilingual, or when compliance requires deployment inside your own environment. Choose OpenAI text-embedding-3 when none of those hold, which covers most English-language text applications, and put the saved money and effort into chunking and reranking where it will do more good.