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AI Code Review Showdown: Anthropic's Claude vs IBM's Granite

BeginnerGuided Project

Compare Anthropic's Claude 3.7 Sonnet and IBM's Granite 3.2 8B Instruct models for Python code review tasks in both reasoning and non-reasoning modes. This lab evaluates how these hybrid reasoning models perform when analyzing syntax errors, algorithms, authentication systems, and architecture. Discover which model delivers better accuracy, speed, and cost-efficiency for different coding scenarios. Learn when reasoning mode provides advantages over non-reasoning mode and identify optimal use cases for each approach based on comprehensive performance metrics.

4.7 (12 Reviews)

Language

  • English

Topic

  • Skills Network

Enrollment Count

  • 96

Skills You Will Learn

  • LLM, Generative AI, Prompt Engineering, Python, Artificial Intelligence, Granite

Offered By

  • IND

Estimated Effort

  • 30 minutes

Platform

  • SkillsNetwork

Last Update

  • January 7, 2026
About this Guided Project
Imagine you're writing code, whether it's your first Python script or a complex application. You need AI assistance—for debugging, optimization, or architectural advice—but with so many models available, how do you choose the right one?

AI coding assistants like Claude 3.7 Sonnet and IBM Granite 3.2 8B Instruct promise to help, but their abilities vary. Does reasoning mode actually improve suggestions? Is one model better for beginners versus complex projects? In this hands-on lab, you'll test these models across four practical coding tasks—from basic syntax checks to system design—to see which AI truly elevates your development process. With the Generative AI Classroom, compare models side-by-side—no setup, no fees, no guesswork. Just log in, run experiments, and discover which AI best supports your coding journey.

Project Overview

This lab benchmarks Claude 3.7 Sonnet vs. IBM Granite 3.2 8B Instruct across coding tasks of increasing complexity:
1️⃣ Basic Code Review (syntax errors, style fixes)
2️⃣ Algorithm Analysis (time/space complexity optimizations)
3️⃣ System Design (authentication flows, architecture patterns)
4️⃣ End-to-End Feedback (readability, maintainability, scalability)

You’ll evaluate them on three key metrics:
✅ Speed – Response times for quick iterations
✅ Cost – Value per query at scale
✅ Accuracy – Error detection and suggestion quality

What You’ll Learn

By completing this lab, you will:
  • Compare reasoning vs. standard modes for coding tasks
  • Identify which model excels at beginner support vs. expert-level design
  • Learn prompting techniques to get better coding help
  • Gain hands-on experience with AI-assisted development

Who Should Do This Lab?

Perfect for anyone who codes—no matter your level:
  • Beginners seeking AI tutoring for fundamentals
  • Intermediate devs optimizing algorithms
  • Senior engineers evaluating AI for architecture reviews
  • Educators comparing AI teaching tools
No advanced skills needed—start with simple code and progress at your pace.

What You Need

✅ A browser (Chrome/Firefox/Safari etc.)
✅ Basic coding awareness (any language)
✅ Curiosity—test prompts and draw conclusions!

Zero installations—everything runs in your browser. By the end, you’ll know whether Claude’s detailed analysis or Granite’s quick feedback better matches your needs—and how to leverage both effectively.

Instructors

Karan Goswami

Data Scientist at IBM

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Jigisha Barbhaya

Data Scientist

I am a Data scientist at IBM and Lead instructor at Skills network. I love to learn and educate. I have completed my MSc(Computer Application) specialisation in Data science from Symbiosis University.

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Contributors

Zikai Dou

Data Scientist at IBM

Ph.D. Candidate in Computer Science at McMaster University, specializing in Federated Learning (FL), Graph Neural Networks (GNNs), and Computer Vision (CV). I develop privacy-preserving, distributed AI systems that tackle real-world challenges in healthcare, finance, and enterprise applications. Passionate about bridging academic research with industry impact to advance scalable and trustworthy AI.

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