Cohere Embed
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Cohere
- Organisation type
- Industry
- Country
- Canada
- Published
- 2 November 2023
- Authors
- Nils Reimers, Elliott Choi, Amr Kayid, Alekhya Nandula, Manoj Govindassamy, Abdullah Elkady
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Language
- Task
- Semantic embedding, Retrieval-augmented generation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
"First, they have been trained on questions and answers from a large web crawl. When we presented our multilingual-v2.0 model last year, we had a collection of over 1.4 billion question-and-answer pairs from 100+ languages on basically every topic on the internet." "Hence, the second stage involved measuring content quality. We used over 3 million search queries from search engines and retrieved the top-10 most similar documents for each query."
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- API access
- Training code
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Likely above 10²³ FLOP
- Yes
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
"We are releasing new English and multilingual Embed versions with either 1024 or 384 dimensions. All models can be accessed via our APIs. As of October 2023, these models achieve state-of-the-art performance among 90+ models on the Massive Text Embedding Benchmark (MTEB) and state-of-the-art performance for zero-shot dense retrieval on BEIR."
Sources
Where this record came from and when it was last checked.
- Reference
- Cohere Command & Embed on Amazon Bedrock
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Cohere Embed was published by Cohere, in Canada, in November 2023. industry is the category the publisher falls under.
It works in Language, and is recorded as doing semantic embedding, Retrieval-augmented generation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Cohere Embed — common questions
What is Cohere Embed used for?
Cohere Embed works in Language, and is recorded as handling semantic embedding, Retrieval-augmented generation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run Cohere Embed?
None. Cohere Embed is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is Cohere Embed open source?
No. Cohere Embed has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Cohere Embed have?
No parameter count has been published for Cohere Embed, which is why no memory or speed figure appears on this page.
Who created Cohere Embed?
Cohere Embed was published by Cohere, based in Canada, categorised as industry.
When was Cohere Embed released?
Cohere Embed was published in November 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.