Cohere Command

Closed weights Cohere 52B parameters March 2023

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
1 March 2023

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Language
Task
Language modeling/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.

Parameters
52B

52B for larger version https://aws.amazon.com/bedrock/cohere-command-embed/ Cohere Command has had a few different sizes over time and is continuously updated, but there's been a 52B version since at least March 2023: https://twitter.com/percyliang/status/1638236921754443776

Training data
tokens

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware
Google TPU v4

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.

Foundation model
Yes
Likely above 10²³ FLOP
Yes
Record confidence
Speculative

Sources

Where this record came from and when it was last checked.

Reference
World-class AI, at your command
Last updated
28 November 2025

What the numbers mean

Background

Cohere Command was published by Cohere, in Canada, in March 2023. The organisation is categorised as industry.

It works in Language, and is recorded as doing language modeling/generation.

This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.

Answers

Cohere Command — common questions

01

What GPU do I need to run Cohere Command?

None. Cohere Command 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.

02

Is Cohere Command open source?

No. Cohere Command has not had its weights published, so it exists only as a service controlled by its owner.

03

How many parameters does Cohere Command have?

Cohere Command has 52B parameters. 52B for larger version https://aws.amazon.com/bedrock/cohere-command-embed/ Cohere Command has had a few different sizes over time and is continuously updated, but there's been a 52B version since at least March 2023: https://twitter.com/percyliang/status/1638236921754443776. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.

04

Who created Cohere Command?

Cohere Command was published by Cohere, based in Canada, categorised as industry.

05

When was Cohere Command released?

Cohere Command was published in March 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.

06

What is Cohere Command used for?

Cohere Command works in Language, and is recorded as handling language modeling/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.

Source

Original publication

Record last updated 28 November 2025

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.