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Command R is a generative language model optimized for long-context tasks and large scale production workloads.
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Overview
Command R is a highly performant generative large language model, optimized for a variety of use cases including reasoning, summarization, and question answering. Command R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. This variant of the model runs on A100 GPUs.
Highlights
- Command R is built for enterprises that plan to leverage their internal data and documents for a tailored and accurate language model. It utilizes retrieval-augmented generation (RAG) to provide accurate and verified information, ensuring credible interactions. Command R model outputs come with clear citations. It is proficient in the most commonly used business languages (10 languages), ensuring seamless understanding and response in various tongues.
- **128k Context Window:** Processes a wide context window of up to 128k tokens, optimizing for RAG use cases and performance. **Tool Use:** Build user-defined tools that enable Command R to automate highly sophisticated tasks using internal infrastructure as well as external tools like CRMs and search engines.
- Command r 082024 finetuning allows you to customize and run a command r 082024 (A100/H100) model for your specific needs. It provides an efficient solution for all your fine-tuning requirements. This powerful combination ensures a seamless and optimized experience for fine-tuning.
Details
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Pricing
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Try this product free for 7 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g4dn.12xlarge Inference (Batch) Recommended | Model inference on the ml.g4dn.12xlarge instance type, batch mode | $32.89 |
ml.p4de.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $32.89 |
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No refunds. Please contact support+aws@cohere.com for further assistance.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
We've updated our SageMaker integration with a major version release for Cohere's Command models, including notebook updates. The "/invocation" endpoint now defaults to API V2, ensuring a seamless transition to the latest version. Please see the notebook on how to use this model with the API update: a. All existing (and future) Embed models: https://github.com/cohere-ai/cohere-aws/blob/main/notebooks/sagemaker/Embed%20Models.ipynb b. All existing (and future) Rerank models: https://github.com/cohere-ai/cohere-aws/blob/main/notebooks/sagemaker/Rerank%20Models.ipynb c. All existing (and future) Command models: https://github.com/cohere-ai/cohere-aws/blob/main/notebooks/sagemaker/Command%20Models.ipynb
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters that can be used to control the generated text. See examples and fields descriptions below.
- Input MIME type
- application/json
https://github.com/cohere-ai/cohere-aws/blob/main/examples/command_r_samples/sample_input.json
https://github.com/cohere-ai/cohere-aws/blob/main/examples/command_r_samples/sample_input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
message | Text input for the model to respond to.
| Type: FreeText
| Yes |
chat_history | chat_history – (array of messages) A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's message. Required fields: role – (enum string) Takes “USER” or “CHATBOT”. message – (string) Text contents of the message.
| Default value: [] Type: FreeText
| No |
documents | A list of texts that the model can cite to generate a more accurate reply. Each document is a string-string dictionary. The resulting generation will include citations that reference some of these documents. It is recommended to keep the total word count of the strings in the dictionary to under 300 words. An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model.
| Default value: [] Type: FreeText
| No |
search_queries_only | When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated.
| Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE
| No |
preamble | Overrides the default preamble for search query generation. Has no effect on tool use generations.
| Default value: [] Type: FreeText
| No |
stream | When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
| Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE
| No |
max_tokens | The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
| Default value: [] Type: Integer Minimum: 0
| No |
temperature | Use a lower value to decrease randomness in the response. Randomness can be further maximized by increasing the value of the `p` parameter.
| Default value: 0.3 Type: Continuous Minimum: 0 Maximum: 2
| No |
Top P (p) | Use a lower value to ignore less probable options. Set to 0 or 1.0 to disable. If both p and k are enabled, p acts after k.
| Default value: 0.75 Type: Continuous Minimum: 0.01 Maximum: 0.99
| No |
Top K (k) | Specify the number of token choices the model uses to generate the next token. If both p and k are enabled, p acts after k.
| Default value: 0 Type: Continuous Minimum: 0 Maximum: 500
| No |
Custom attributes
The following table describes custom attributes for real-time inference endpoints.
Field name | Description | Constraints | Required |
|---|---|---|---|
safety_mode | Controls the contents of the safety section of the system prompt. “CONTEXTUAL” Responds as instructed while rejecting harmful or illegal suggestions. “STRICT” Designed to avoid sensitive topics. Strict content guardrails provide an extra safe experience by prohibiting inappropriate responses or recommendations. "NONE" Omits the safety section of the system prompt.
| Default value: CONTEXTUAL Type: Categorical Allowed values: CONTEXTUAL, STRICT, NONE
| No |
Resources
Vendor resources
Support
Vendor support
Contact us at support+aws@cohere.com
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Customer reviews
Collins-Omondi
Chat sentiment analysis has supported hobby projects but pricing and setup still need improvement
Reviewed on Mar 30, 2026
Review from a verified AWS customer
What is our primary use case?
I implemented it myself in my bot, defining what is acceptable based on how people chat and what they might mean when they say certain things. I never actually used that feature for Cohere Command R .
The dataset I am using is just the chat, the user chat, and it is not that big. It is just a few months, and I always clear the chats after a few months. So it is just normal content, nothing extraordinary; I do not think it can be quantified as big data.
What is most valuable?
Cohere Command R works for what I need. I know there are many other models and many other free models. I have tried CodeGemma, but it is not for what I was trying to do; it is more about coding. I wanted something interactive, focusing on the language side of it, not the coding side.
Personally, compared to other models, Cohere Command R is pretty easy to set up and good for what I need as of now.
Deploying this solution is pretty similar to working with any other model for me. I cannot really say much about complexities, but I am a bit technical, so the process is quite the same.
What needs improvement?
Honestly, I have never needed technical support, but I think if you could improve on that, it would be acceptable. I do not know about the pricing; for me, it is kind of too much. Of course, I am using the free models, but if I could get the newer models, I think they are interesting.
I know we are talking about Cohere Command R for now, but I think there are some other models that I have seen some interest in, like Embed 4. If the pricing could be adjusted, that would be better because the pricing is kind of high.
Of course, it matters; for organizations, it is acceptable, but for personal use like mine, it is just a hobby project. Spending that much money on something that you do not earn from is not ideal. So for people testing or using it for hobby projects, I think you could reduce the pricing a bit. But for now, I am using Cohere Command R for free.
For how long have I used the solution?
I have been using it occasionally for around two to three years.
How are customer service and support?
I have never interacted with the support team and do not know anyone who has, so I rate it five. I am not saying it is bad; I just have never tried it before, and I do not want to give it a lower score. So I will say five because I hope it is good.
How was the initial setup?
It takes a few hours, a lot of hours, to deploy Cohere Command R. Not days, but just a lot of hours debugging and dealing with issues when mostly it was on the AWS side, like exposing the API and the static routes. It was just the AWS side of it that took a lot of time, but the model itself was not that complicated.
What's my experience with pricing, setup cost, and licensing?
I did not purchase it from Cohere; I think it was free by the time I was working with it. I am not sure. It was a while ago when I started using it, but I do not know if the pricing has changed. I did not pay for it back then.
What other advice do I have?
I am still using Cohere and maybe Cloudinary .
I work with Cohere Command R occasionally, but not so much.
I am familiar with Cohere Command R, and I just use it as a model. It is pretty similar to the others that I use, so I cannot really say anything specific about it.
I do not use it every time, just occasionally.
I never tried real-time analysis; I never needed to.
I would rate this review a 7.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
reviewer2785584
Fast retrieval has improved our genAI latency and supports timely project delivery
Reviewed on Dec 09, 2025
Review provided by PeerSpot
What is our primary use case?
My main use case for Cohere Command R is for a GenAI application. For the RAG project, we are using Cohere Command R for the retrieval process.
What is most valuable?
The best feature Cohere Command R offers is the latency. What stands out to me about the latency is that it is faster than other solutions I have tried. Regarding the positive impact, it has improved the latency and our time to delivery.
What needs improvement?
I do not know how Cohere Command R can be improved. I do not have anything at all I would like to see improved, even if it is something small.
For how long have I used the solution?
I have been using Cohere Command R for three years.
What do I think about the stability of the solution?
Cohere Command R is stable.
What do I think about the scalability of the solution?
Cohere Command R's scalability is good enough.
How are customer service and support?
The customer support for Cohere Command R is good.
Which solution did I use previously and why did I switch?
I did not previously use a different solution before Cohere Command R.
What was our ROI?
I have not seen a return on investment and cannot share any relevant metrics.
What's my experience with pricing, setup cost, and licensing?
My experience with pricing, setup cost, and licensing is that it is good.
Which other solutions did I evaluate?
Before choosing Cohere Command R, I did not evaluate other options. I cannot share which other options I evaluated before choosing Cohere Command R because there were no other options.
What other advice do I have?
I do not have anything else to add about how I use Cohere Command R in my projects. I do not know how Cohere Command R has impacted my organization positively.
My advice to others looking into using Cohere Command R is to try it. I would recommend trying the product. I am giving this review a rating of 9.