I tested OpenAI GPT-4o, Anthropic Claude, and Google Gemini on the same Apex code review task with identical prompts. Each AI found different “critical issues” — GPT-4o focused on security, Claude on performance, and Gemini on validation — revealing that AI model selection significantly impacts your code review results.
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What?
A practical comparison of how three leading AI models (OpenAI GPT-4o, Anthropic Claude, and Google Gemini) analyze the same Salesforce Apex code with wildly different results.
Who?
Salesforce developers, architects, administrators, and technical leaders who want to understand how different AI models approach code analysis.
Why?
To help you decide which AI model best suits your Salesforce development workflow and quality standards.
→ Find the right AI for your code reviews and save development time while improving quality.
What can you do with it?
Implement automated code quality reviews in your Salesforce org
Compare different AI models for technical analysis effectiveness
Set up a continuous code quality system for your development team
Create a historical record of code improvements over time
I recently had the opportunity to experiment with applying different AI models to Salesforce Apex code analysis. This eye-opening experiment revealed how three leading models approached the same code with remarkably different perspectives — much like having three different senior developers review your code.
Using GPTfy, an AppExchange app that lets you connect any AI model to your Salesforce org, I created a system to analyze Apex code automatically. Here’s the setup:
The goal was simple: see how different AI models would evaluate the exact same code using the exact same prompt.
What I discovered was fascinating — each AI had its own unique “personality” when it came to code review.
GPT-4o immediately identified a critical security issue: the absence of a sharing declaration in the class. This focus on security considerations before anything else shows GPT-4o’s tendency to prioritize access control and security vulnerabilities.
It provides clear explanations of why this was problematic and offers straightforward solutions:
GPT-4o also found other issues but categorized them as “important” rather than “critical,” such as:
What stood out was GPT-4o’s straightforward but slightly less detailed explanations compared to the other models.
Claude took a different approach, identifying SOQL queries within loops as the critical issue. This focus on performance and scalability over security shows Claude’s tendency to prioritize operational efficiency.
Claude’s explanations were notably more detailed and educational, almost like having a mentor review your code:
Claude’s code examples seemed more practical and thoughtful, and its explanations were more coach-like and user-friendly.
Gemini was noticeably faster than the other models, but it exhibited some interesting quirks in its responses.
Its explanations sometimes stray off-topic, making unusual analogies or including irrelevant commentary. While Gemini seemed to prioritize input validation as its primary focus, its explanations were sometimes unclear or unfocused, less coherent. They sometimes contained confusing statements that weren’t directly related to the analyzed code.
While speed might be Gemini’s advantage, the clarity and focus of its code review feedback lagged behind the other models in this specific use case.
What makes this experiment particularly interesting is how easily you can switch between different AI models using a platform like GPTfy. Here’s how it works:
This flexibility lets you choose the right model for your specific needs or even run multiple models for a more comprehensive review.
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Get GPTfyRead More hereThe implementation uses a simple but effective architecture:
When a developer updates an Apex class:
This experiment revealed several important insights:
| Aspect | Before AI Code Reviews | After AI Code Reviews |
|---|---|---|
|
Code Quality Checks |
Manual, inconsistent |
Automated, consistent |
|
Review Time |
Hours per class |
Seconds per class |
|
Coverage |
Limited by reviewer bandwidth |
Every class reviewed |
|
Historical Tracking |
Difficult to track improvements |
Complete history of code evolution |
|
Standards Enforcement |
Varies by reviewer |
Consistent application |
AI models can dramatically improve your Salesforce development process by providing automated code reviews, but different models have different strengths. OpenAI GPT-4o focuses on security issues, Anthropic Claude excels at performance optimization with detailed explanations, and Google Gemini offers speed but less consistent quality.
By integrating these AI capabilities directly into your Salesforce org with tools like GPTfy, you can implement a continuous code quality system that evolves with your development team and maintains a historical record of improvements.
Consider running multiple models against critical code for a more comprehensive review, or select the model that best aligns with your organization’s priorities.
Ready to eliminate Salesforce grunt work while maintaining enterprise security?
Saurabh is an Enterprise Architect and seasoned entrepreneur spearheading a Salesforce security and AI startup, with inventive contributions recognized by a patent.
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