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Google Gemini AI vs Meta Llama 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

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)
Meta Llama
Ranking in Large Language Models (LLMs)
14th
Average Rating
7.0
Reviews Sentiment
2.2
Number of Reviews
1
Ranking in other categories
AI-Powered Chatbots (17th)
 

Mindshare comparison

As of August 2026, in the Large Language Models (LLMs) category, the mindshare of Google Gemini AI is 15.2%, up from 14.9% compared to the previous year. The mindshare of Meta Llama is 1.3%, up from 0.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Large Language Models (LLMs) Mindshare Distribution
ProductMindshare (%)
Google Gemini AI15.2%
Meta Llama1.3%
Other83.5%
Large Language Models (LLMs)
 

Featured Reviews

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.
Muralidharan Neelameghan - PeerSpot reviewer
Group CIO at a tech services company with 51-200 employees
Offline rag workflows have improved compliance and cost control but still need domain-focused datasets
I would like to see additional features in the future where Meta Llama allows us to have the datasets by domain, rather than just covering everything. There is nothing else I would add or improve at this moment; everything is still in progress. The world is moving in different directions, and I need to make a use case out of it properly for AI, especially in the supply chain and financial services. I am trying to see how we can bring AI on top of CRM or AI on top of ERP. That is the way things are moving. You cannot have AI as the main product; AI is just a subsidiary product or add-on. It is not mainstream and can never be mainstream. I would like to bring most implementers in business to understand this because in business, we go technology, but we need to go from the business line towards AI. The question is how do you deploy AI on top of your existing infrastructure? We need accessibility and affordability of AI for small and medium businesses; that is where the game changer is happening, and that is where I am investing my time and money.

Quotes from Members

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

Pros

"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."
"The most valuable feature of Google Gemini for us is its text writing capabilities, which we are using for writing texts and social media posts for our company's social media page."
"It is like having an expert at my fingertips for those out-of-scope queries."
"The best features for my use cases with Google Gemini are its dynamic LLM model that can perform dynamic searches, which is wonderful for factual searches to understand numbers and to generate real-time answers."
"Google Gemini has the best combination of scalability, costs, performance, and accuracy."
"I'm impressed with its orchestration capabilities, which structure the workflow for research automatically and meaningfully, making it a helpful tool."
"Google Gemini uses all the data that Google has produced."
"Recently, Google Gemini has been very stable, without performance issues, even handling network problems smoothly with a retry."
"I assess the benefits of Meta Llama's automation processes as substantial."
 

Cons

"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."
"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."
"Google can improve in model justification and interpretability of answers. I still perceive Google Gemini, in some instances, as a kind of black box."
"There are scalability issues, and they are all related to what you can do within that context window of a million tokens."
"The binning process could be more intuitive, especially when grouping data into categories like age groups."
"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."
"The code generation capabilities in Gemini could be improved."
"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."
"I might even drop Meta Llama because it is very heavy on memory."
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Top Industries

By visitors reading reviews
University
10%
Comms Service Provider
9%
Computer Software Company
8%
Financial Services Firm
7%
No data available
 

Company Size

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

Questions from the Community

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....
What needs improvement with Meta Llama?
I would like to see additional features in the future where Meta Llama allows us to have the datasets by domain, rather than just covering everything. There is nothing else I would add or improve a...
What is your primary use case for Meta Llama?
I deal mostly with tools such as Meta Llama for embeddings, and I was primarily using it for search, but now I use OpenSearch instead of Azure search, bringing some small language models and offlin...
What advice do you have for others considering Meta Llama?
I am working with a combination of products now, mostly as I work with air-gapped systems and have moved to all open sources. Prediction is up to us, which is why I said it is on top of your ERP or...
 

Also Known As

Gemini, Google Bard
No data available
 

Overview

Find out what your peers are saying about Google, OpenAI, Cohere and others in Large Language Models (LLMs). Updated: July 2026.
908,800 professionals have used our research since 2012.