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Cohere Command R vs Google Gemini AI comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Cohere Command R
Ranking in Large Language Models (LLMs)
13th
Average Rating
8.0
Reviews Sentiment
4.6
Number of Reviews
4
Ranking in other categories
No ranking in other categories
Google Gemini AI
Ranking in Large Language Models (LLMs)
1st
Average Rating
8.0
Reviews Sentiment
5.0
Number of Reviews
18
Ranking in other categories
AI Writing Tools (1st), AI Code Assistants (5th), AI Proofreading Tools (1st)
 

Mindshare comparison

As of July 2026, in the Large Language Models (LLMs) category, the mindshare of Cohere Command R is 1.1%. The mindshare of Google Gemini AI is 15.3%, up from 14.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Large Language Models (LLMs) Mindshare Distribution
ProductMindshare (%)
Google Gemini AI15.3%
Cohere Command R1.1%
Other83.6%
Large Language Models (LLMs)
 

Featured Reviews

Husain Barwala - PeerSpot reviewer
AI Engineer at Walkover Web Solutions
Improved document-based answers and chatbot accuracy while still needing fresher knowledge and longer outputs
There are some cons of this model. The output cap is 4,000 max tokens only, which was a lag part of this model. The knowledge base cutoff is June 2024, which is over a year and a half old now. It should be updated with the latest cutoff data. If this model supported a web tool with RAG and web search inbuilt, that would be very great and the model would be very perfect. For complex coding and multi-step logic, this model is of no use because it does not give accurate answers. This model should work only to make RAG better and better. There should be a model known by the name of RAG only, Retrieval-Augmented Generation, that will be used as RAG only for different platforms where users do not have to create a RAG pipeline and pass a tool. This model can help improve RAG and web search. If this model does not find data in the document and if users allow web search, then at runtime this model will perform web search and return the output. This way there is less chance the user will get a better output and this way the model can be improved. The large context window is a limitation. Suppose I want large output from this model, but the max output tokens are 4,000 only, so I cannot retrieve large answers from this model. This is one of the drawbacks, which is why I cut one point. This model lacks web search, so web search is not available. If web search were there, then this model could give answers from the web if the data is not present in that document, which is why I cut one point from this as well. The third point is the knowledge cutoff that this model is trained on, which is June 2024. It has been 1.5 years and it is now May 2026. The knowledge cutoff is very poor for this model, which is why I cut three points for this model. This is why I rate it 7 out of 10.
UB
AI Researcher and Full Stack Developer at ADP
AI workflows have transformed prototyping and coding productivity across my daily projects
There is a steeper learning curve for advanced agentic features that could be improved, and hallucinations should be reduced. The answers provided are long, which is impressive but not efficient for users needing rapid, crisp responses. Providing concise answers would improve the user experience. Google Gemini AI's UI code is too vague and the designs are not very appealing. Google Gemini AI can improve its UI code and address hallucination issues. The long answers provided can be tiresome to read, and the pricing is too high for individuals like me. These considerations led me to give a rating one point less than ten. Native GitHub or Vercel export could be integrated, and the context could be increased to over two million tokens. A simplified agentic setup for the UI could also help non-technical experts handle it more effectively.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"The best feature Cohere Command R offers is the latency, which is faster than other solutions I have tried and has improved the latency and our time to delivery."
"After implementing Cohere Command R, the whole process became streamlined, reducing time and increasing end user engagement."
"After this model release, when we integrated this model on our platform, around 20% of users came to use chatbot, and previously they were facing complaints that the chatbot replied too slowly or hallucinated a lot, but after using this model the complaints are very minimal and their support tickets are reduced by 5% to 10%."
"Personally, compared to other models, Cohere Command R is pretty easy to set up and good for what I need as of now."
"I'm impressed with its orchestration capabilities, which structure the workflow for research automatically and meaningfully, making it a helpful tool."
"There is a significant return on investment, with a reported four times productivity increase on research and coding tasks that I do daily."
"Recently, Google Gemini has been very stable, without performance issues, even handling network problems smoothly with a retry."
"The main benefits that Gemini brings to the table include definitely speeding things up significantly, and it is also introducing many new use cases that we were not able to work on earlier."
"I have compared responses from Gemini and ChatGPT and received similar results but presented differently, and every tool has its uniqueness; it is good, and I am enjoying using both tools, but most often I use ChatGPT because I haven't used Gemini recently."
"Google Gemini AI is better in terms of searching the web, considering that Google Gemini AI is a property of Google, and the search results when looking for answers from the web are superior compared to those given by Alexa."
"It is like having an expert at my fingertips for those out-of-scope queries."
"The most beneficial aspect of Google Gemini for me is that it's able to do searches much better."
 

Cons

"For complex coding and multi-step logic, this model is of no use because it does not give accurate answers."
"I do not know about the pricing; for me, it is kind of too much."
"The main area of improvement can be performance on complex reasoning and coding tasks."
"Google Gemini AI is not used much because it does not appear to be as responsive or as effective as Alexa when responding to questions, queries, instructions, or commands."
"Google Gemini often gets factual answers wrong, which is problematic."
"Google Gemini could improve its functionalities compared to other tools like ChatGPT, especially in the customization options, Canvas mode, and web search tools, which aren't as advanced."
"Sometimes there is some difficulty while understanding the issue and the technical jargon, but otherwise it is all good."
"Google Gemini needs more accurate answers and the ability to export data to Excel or Google Sheets."
"When I have reached out to Google Support, it's been very limiting. I would rate Google at about a five."
"Google can improve in model justification and interpretability of answers. I still perceive Google Gemini, in some instances, as a kind of black box."
"Currently, it operates mostly autonomously, and while it provides structured activities, making the research configuration more accessible and flexible would be beneficial."
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Top Industries

By visitors reading reviews
Construction Company
46%
Comms Service Provider
7%
Financial Services Firm
6%
Healthcare Company
5%
University
10%
Comms Service Provider
9%
Computer Software Company
8%
Financial Services Firm
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business7
Midsize Enterprise6
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Cohere Command R?
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 ...
What needs improvement with Cohere Command R?
The main area of improvement can be performance on complex reasoning and coding tasks. Cohere Command R is strong for RAG and grounded generation, but I would not choose it for those tasks. There w...
What is your primary use case for Cohere Command R?
I have used Cohere Command R mainly for Retrieval-Augmented Generation (RAG) workflows where the model needs to answer questions from enterprise documents rather than relying on its pre-trained kno...
What is your experience regarding pricing and costs for Google Gemini?
The pricing of Google Gemini AI is not well understood, so no feedback can be provided on the cost. It was thought to have come together with the device subscription.
What needs improvement with Google Gemini?
Sometimes there is some difficulty while understanding the issue and the technical jargon, but otherwise it is all good. It is not a 10 for me because some features need to be improved on the techn...
What is your primary use case for Google Gemini?
I use Google Gemini AI to get ticket details, older tickets, and suggestions on what needs to be done and the email format. Google Gemini AI is a tool which is integrated with the Enterprise cloud....
 

Comparisons

No data available
 

Also Known As

No data available
Gemini, Google Bard
 

Overview

Find out what your peers are saying about Cohere Command R vs. Google Gemini AI and other solutions. Updated: June 2026.
902,894 professionals have used our research since 2012.