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Gemini vs GitHub CoPilot comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jun 26, 2025

Review summaries and opinions

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

ROI

Sentiment score
6.2
Users experience diverse financial impacts from Gemini; some save costs and boost productivity, while ROI evaluation varies widely.
Sentiment score
7.4
GitHub CoPilot boosts productivity, efficiency, and resource management, though it may be costly for some individual users.
For some of the models it's actually free. It doesn't cost anything, but once you get to production scenarios in which you have to use the API, you have to pay.
Efficiencies with GitHub CoPilot have improved by 30%, which means a quicker go-to market and a simplified way of documenting technical designs.
 

Customer Service

Sentiment score
6.1
Gemini's customer service is mixed; many praise self-service resources, but direct support is perceived as challenging to reach.
Sentiment score
6.5
GitHub CoPilot support receives mixed reviews; users value knowledgeable help but note delays, relying on community and Microsoft resources.
They rely on a self-service approach, providing a lot of information online through blogs and documents.
Microsoft has done better, though they're not great at it, but they seem to be more responsive.
it's really difficult to reach them
With a large user base, it covers a wide range of questions, from simple to complex, ensuring that answers are available.
 

Scalability Issues

Sentiment score
7.4
Google Gemini's scalability praised for infrastructure efficiency, but criticized for context size, costs, and token-related integration challenges.
Sentiment score
7.1
GitHub CoPilot scales well but faces reliability issues under high traffic, with performance varying by deployment scale and plan.
Google Gemini handles multiple PDF files and big files efficiently.
It can conduct research quickly, taking only five to seven minutes to produce a ten-page research document with a reasonable executive summary.
If you want to grow the amount of information that you want to insert into the model before you provide an answer, you have to use different techniques.
It cannot be fully depended on to build every component and run a large enterprise application without significant human intervention.
With an enterprise plan, there are no limitations, so scalability is not an issue.
 

Stability Issues

Sentiment score
8.4
Google Gemini is praised for stability and reliable performance, with rare issues, earning high ratings and positive feedback.
Sentiment score
7.5
GitHub CoPilot is stable with minor connectivity issues, prompting some users to explore alternatives for certain tasks.
Everything I've tried so far works without instability, bugs, or hallucinations.
Recently, Google Gemini has been very stable, without performance issues.
In most cases, it does not generate irrelevant code.
At certain times, you may not get the required response and realize it's either down or not responding for other reasons.
 

Room For Improvement

Gemini improvements focus on context expansion, intuition, accuracy, customization, speed, format support, and addressing content freshness concerns.
GitHub CoPilot needs improved stability, integration, suggestion accuracy, contextual awareness, language support, and pricing to enhance functionality and usability.
Google Gemini needs more accurate answers and the ability to export data to Excel or Google Sheets.
When working on a 20-page document, Google Gemini sometimes loses context about earlier parts.
Currently, it operates mostly autonomously, and while it provides structured activities, making the research configuration more accessible and flexible would be beneficial.
There is excellent support across various code editors like JetBrains, VS Code, and NeoGen.
To understand our application better and learn from it would likely require access to the entire codebase, which a lot of companies may not allow.
 

Setup Cost

Gemini's pricing is average, impacted by currency changes, but offers discounted features, reasonably priced for enterprise use.
GitHub CoPilot costs $10 monthly for personal use, $19 for enterprise, with possible discounts for current GitHub licensees.
Google Gemini is free.
The per license cost is on par with others, but with the number of licenses, it becomes expensive.
The feature of Gemini 2.5 research is highly discounted.
They recently made Copilot free to use up to a certain limit, which is a positive change.
 

Valuable Features

Google Gemini enhances productivity with text writing, image generation, and data processing but lacks creativity in storytelling or marketing.
GitHub CoPilot enhances productivity with code generation, test creation, multi-language support, and seamless IDE integration for faster development.
The most valuable feature of Google Gemini is its ability to function as an intelligent assistant, providing accurate answers to natural language queries and performing translations.
The AI capabilities of Google Gemini are a multi-modal LLM which allows me to pass documents, images, and texts in the same prompt.
It provides an experimental search module capable of scanning hundreds of websites to deliver summarized data.
It is certainly time-saving; we have seen upwards of around 30% plus of time savings using GitHub CoPilot.
Copilot is integrated into my environment, providing the context and the bigger picture of how the code is used throughout the project.
 

Categories and Ranking

Gemini
Ranking in AI Code Assistants
2nd
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
12
Ranking in other categories
AI Writing Tools (1st), Large Language Models (LLMs) (1st), AI Proofreading Tools (1st)
GitHub CoPilot
Ranking in AI Code Assistants
1st
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
28
Ranking in other categories
Rapid Application Development Software (10th)
 

Mindshare comparison

As of June 2025, in the AI Code Assistants category, the mindshare of Gemini is 5.7%, up from 0.5% compared to the previous year. The mindshare of GitHub CoPilot is 7.0%. It is calculated based on PeerSpot user engagement data.
AI Code Assistants
 

Featured Reviews

Luís Silva - PeerSpot reviewer
Tool proves competitive with cost-effectiveness and speed while benefiting decision-making and integration capabilities, but shows room for enhancing creativity and handling large data contextually
There is another benefit in that Google Gemini is highly competitive from a cost perspective. The cost per token is very competitive. It's not the best - the Chinese models such as DeepSeek are better, but I can't use them. From a cost perspective, between European and United States options, Google Gemini is very competitive. It's not only its cost, it's also very performing. The time it takes to process the prompts and context information is very fast. It's one of the fastest models in the market. That's another advantage. There are other models which are also very good in quality but not as fast, such as Anthropic Claude. Google Gemini currently has a very good balance. The AI capabilities of Google Gemini are a multi-modal LLM which allows me to pass documents, images, and texts in the same prompt. Being multi-modal makes my life easier. I don't have to translate information before I can work with it. Google provides free tools to test scenarios, which speeds up development and reduces costs. It's a very fast and accurate model with quality responses.
TarunRevalla - PeerSpot reviewer
Saves time with context-aware code suggestions and seamless integration
In terms of improvements for Copilot, I haven't considered much since it offers many useful interactions. It integrates well with GitHub repositories, tracks changes on PRs, and provides valuable suggestions where applicable. There is excellent support across various code editors like JetBrains, VS Code, and NeoGen. I also run many automations within GitHub. When an NPR is raised, it automatically provides suggestions, which is part of the enterprise edition without limitations. While I don't see immediate room for improvement, one suggestion is for Copilot to provide specific suggestions for certain lines of code instead of rewriting entire sections. If the tool could focus on specific lines where changes are suggested, it would save time and reduce server load.
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Top Industries

By visitors reading reviews
University
12%
Computer Software Company
11%
Comms Service Provider
8%
Government
7%
Financial Services Firm
16%
Computer Software Company
10%
Healthcare Company
9%
Manufacturing Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What is your experience regarding pricing and costs for Google Gemini?
If ten is expensive and one is free, I would rate it a three or four.
What needs improvement with Google Gemini?
There's a new release every two months and it's very fast. I would say that they should keep increasing the context size of information that we can provide. Going from 1 million to 10 million would...
What is your primary use case for Google Gemini?
I am aware of Google Gemini translation tools, but I haven't used them. They don't impact my work as all my work is done in English, so I don't need any kind of translation. I've been using the API...
What is your experience regarding pricing and costs for GitHub CoPilot?
I honestly don't have a clue about how much we usually pay for GitHub CoPilot because the increase in license costs is managed by another team, and I am not the administrator, so I generally don't ...
What needs improvement with GitHub CoPilot?
The areas of GitHub CoPilot that need improvement include the ability to debug, which is applicable to AI-driven tools that allow you to code, and to understand our application better and learn fro...
 

Comparisons

 

Also Known As

Google Bard, Google Gemini
No data available
 

Overview

Find out what your peers are saying about Gemini vs. GitHub CoPilot and other solutions. Updated: June 2025.
859,129 professionals have used our research since 2012.