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Cohere Command R vs Google Gemini AI comparison

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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 October 2026, in the Large Language Models (LLMs) category, the mindshare of Cohere Command R is 2.0%. The mindshare of Google Gemini AI is 14.3%, down from 16.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Large Language Models (LLMs) Mindshare Distribution
ProductMindshare (%)
Google Gemini AI14.3%
Cohere Command R2.0%
Other83.7%
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.
Boya Uday Kumar - PeerSpot reviewer
Ai Research Enthusiast And 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

"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."
"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."
"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."
"Google Gemini uses all the data that Google has produced."
"The most beneficial aspect of Google Gemini for me is that it's able to do searches much better."
"Google Gemini has the best combination of scalability, costs, performance, and accuracy."
"There is a significant return on investment, with a reported four times productivity increase on research and coding tasks that I do daily."
"Google Gemini AI positively impacts our organization as it eases the work, which is a positive aspect that has improved our day-to-day activity."
"The integration of Gemini with other Google services is quite good; we develop the application using open source platforms such as LangGraph or LangChain, where the integration for Gemini is quite good."
"I would rate my overall experience with Google Gemini as a nine out of ten."
 

Cons

"The main area of improvement can be performance on complex reasoning and coding tasks."
"I do not know about the pricing; for me, it is kind of too much."
"For complex coding and multi-step logic, this model is of no use because it does not give accurate answers."
"I do not have a special recommendation for improvement."
"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 needs more accurate answers and the ability to export data to Excel or Google Sheets."
"Sometimes there is some difficulty while understanding the issue and the technical jargon, but otherwise it is all good."
"Google Gemini's biggest strength is also its drawback; it's excellent for generating reports and working with data in real-time, but it isn't the most creative LLM for tasks such as creating a digital storytelling campaign or crafting marketing messages."
"The only drawback for Gemini services or Google Vertex AI platform is that their platform understanding and getting started with the platform is a difficult process."
"Gemini 3 Pro is too expensive for individuals like me, costing about thirty dollars per user per month, and its responses tend to be long, requiring users to read considerably more than other models that provide crisper answers."
"I conducted some research using Google Gemini, and sometimes the results are not correct. For example, when I asked for information about marketing and inquired about the sources used, the sources were not relevant or had no relation to the subject I was researching."
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Top Industries

By visitors reading reviews
Construction Company
41%
Comms Service Provider
12%
Financial Services Firm
7%
Outsourcing Company
5%
Comms Service Provider
11%
University
8%
Financial Services Firm
8%
Computer Software Company
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....
 

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: September 2026.
915,341 professionals have used our research since 2012.