Skip to main content
Watch: I Built an AI App Without Leaving Databricks

Cohere - API - verified

Embed English v3.0 embeddings

Embed English v3.0 is a current embedding model from Cohere, called by API as embed-english-v3.0. It returns 1,024-dimension vectors by default and accepts up to 512 input tokens, for text and image input. No per-1M-token list price is stated for it. cohere.com/pricing shows no self-serve per-token rate for Embed v3 models today.

CurrentModel id: embed-english-v3.0

Default dims
1,024
Max input
512 tok
Price / 1M
Not stated
Inputs
Text, Image

Dimensions

  • 1,024default

Default output: 1,024 dimensions.

Pricing

Per 1M input tokens
Not stated
Batch, per 1M
Not stated

cohere.com/pricing shows no self-serve per-token rate for Embed v3 models today.

Source: cohere.com/pricing

Usage

Copied verbatim from the page linked below. Placeholders such as project ids and API keys are the source's own.

Code
embed_job_response = co.embed_jobs.create(
    dataset_id=input_dataset.id,
    input_type="search_document",
    model="embed-english-v3.0",
    embedding_types=["float"],
    truncate="END",
)

# block until the job is complete

embed_job = co.wait(embed_job_response)

Source: docs.cohere.com/docs/embed-jobs-api

Worth knowing

English-only model that also works with the Embed Jobs API for asynchronous batch embedding, which supports only the v3.0 models.

Release date not stated in the sources. Multilingual: no.

FAQ

How many dimensions does Embed English v3.0 output?

Default output: 1,024 dimensions.

What is the max input length of Embed English v3.0?

512 tokens per input, per Cohere's documentation. Longer text has to be chunked before you embed it.

How much does Embed English v3.0 cost?

No per-1M-token list price is stated for it. cohere.com/pricing shows no self-serve per-token rate for Embed v3 models today. Source: https://cohere.com/pricing.

What can Embed English v3.0 embed?

Text and image input, per the provider. It is not documented as multilingual.

Sources

Last verified from the pages below.

More Cohere embedding models

Default dimensions, max input tokens and list price per 1M tokens.

Cohere API modelsPrices per 1M tokens, context windows and specs for every Cohere model in the API registry.

All embedding models

Newsletter

New models, every week

One email per week. New model releases, embedding and retrieval updates and what they mean for what you build.

Keep exploring

Model API pricing and specs

First-party prices per 1M tokens for chat and reasoning models.

Local models

Open-weight LLMs you can download and run, with sizes and licenses.

What is RAG?

Where embeddings fit in a retrieval pipeline, end to end.

Vector databases compared

Where the vectors go once you have picked a model.