Artificial intelligence (AI) is transforming the coding landscape, offering tools that assist, automate, and improve developer tasks. In this review, I tested five popular AI coding models: Claude 3.5 Sonnet, Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4o, and o3-mini (medium/high reasoning). Each model offers unique strengths and weaknesses, proving that there’s no one-size-fits-all solution in the AI landscape for coding.
In this breakdown, I’ll share the key takeaways from my experience, from coding precision to usability quirks. Let’s dive into how these models stack up for specific tasks like refactoring, creating a game, and navigating real-world complexities. 🚀
🔑 Why AI in Coding Matters
AI in coding isn’t just about automating repetitive tasks—it’s about enhancing productivity, creativity, and accuracy. Whether you’re refactoring old code, building new features, or experimenting with game ideas, AI models can save time and energy. However, not all models are created equal, and choosing the right one can make or break your results.
1️⃣ Claude 3.5 Sonnet: Precision Over Speed
🤔 What It Does Well:
Claude 3.5 Sonnet is lauded for its laser focus and context retention. It executes tasks precisely, analyzing not just the specific file but the surrounding dependencies to ensure cohesive results. This feature is invaluable for complex codebases where subtle errors can ripple across files.
Example Task:
When prompted to make a small change in one file, Claude 3.5 reads related files, ensuring its edits align perfectly.
⚡ Challenges:
- It’s slow, but fewer debugging needs offset the waiting time.
- Plays it too safe, avoiding changes that could improve related files.
Memorable Quote:
“I’d rather have it work slower and less debugging on my end than work faster and then I spend a whole crap ton of time debugging that code.”
💡 Quick Tip:
Use this model for tasks requiring high precision where you don’t want unrelated parts to be touched.
2️⃣ Claude 3.7 Sonnet: Ambition with a Price
🤔 What It Does Well:
Claude 3.7 Sonnet is like Claude 3.5’s overachieving sibling. It’s more ambitious—refactoring additional parts of the codebase it deems related, even without being explicitly told.
Example Task:
It examines an entire codebase, identifies improvements, and initiates changes across unrelated areas.
⚡ Challenges:
- Overreach: Often updates too many files, creating unnecessary code bloat.
- Inconsistencies: May delete vital functions without proper replacements.
- Extended “thinking mode” is prone to hallucinations, inefficiency, and extremely high costs.
Memorable Observation:
“Why did it delete that function? I need that function for this file over here!”
💡 Quick Tip:
Avoid for critical sections unless you’re ready to spend extra time reviewing its overly ambitious contributions.
3️⃣ Gemini 2.5 Pro: The All-Rounder 🔥
🤔 What It Does Well:
Gemini 2.5 Pro combines the best of Claude 3.5’s accuracy and Claude 3.7’s breadth. With an immense context window, it balances precision and ambition better than any other tool.
Example Task:
- Handled a complex refactor efficiently. Even when tasked with updating related files, it smartly avoided unnecessary changes.
- Delivered a near-perfect codebase-wide refactor for Rust, using efficient methods like
.filter_map.
⚡ Challenges:
- Broader than Claude 3.5, sometimes touching files you didn’t want modified.
- Requires extra care with specific or high-stakes sections.
Why It Wins:
Gemini produced flawless results for a one-shot P5.js game. Despite requiring two error prompts for fine-tuning, it far outperformed the competition in accuracy and relevance to the prompt. 🎯
💡 Quick Tip:
Pick Gemini 2.5 for large-scale projects or complex, multi-file tasks needing context-aware reasoning and optimal results.
4️⃣ o3-mini (Medium/High Reasoning): Simple but Frustrating
🤔 What It Does Well:
o3-mini provides control and precision, sticking strictly to the file or area you specify—perfect for when you want to avoid touching unrelated parts of the codebase.
Example Task:
- Minimal changes and no hallucinations. If prompted to make a change, it does only that—and nothing more.
⚡ Challenges:
- Manual Iterations: Doesn’t complete tasks in one go. You’ll need to prompt repeatedly.
- Limited Context Awareness: Barely analyzes the codebase, so results often lack cohesion.
Humorous Observations:
- “I’ll now apply these changes… I forgot the previous changes.”
- “Wait, why aren’t the updates appearing yet?”
💡 Quick Tip:
Use o3-mini for tiny, specific file updates—but be prepared to hand-hold it through the process.
5️⃣ GPT-4o: More Conversations, Less Results
🤔 What It Does Well:
While GPT-4o excels as a chat companion, providing friendly, motivational responses for brainstorming scenarios, it falls short when applied to coding.
Example Task:
- Attempted to create a launch-style game. The result? A functional game missing major features like aiming and proper obstacle physics.
⚡ Challenges:
- Hallucinations: Overwrites code with no discernible change.
- Poor precision and additional debugging compared to Claude 3.5.
- Speed ≠ Accuracy: Quicker responses, but also more errors.
Memorable Comment:
“Bro, you’re cooking now—that’s low-key fire, dude.” 🔥 🤖 (Typical GPT-4o encouragement, but not useful!)
💡 Quick Tip:
Stick to GPT-4o for high-level ideation and brainstorming—not actual coding.
🔥 Real World Test Cases: Coding Challenges Recap
Game Development with P5.js
- Gemini 2.5 Pro: Best performer with a polished game after minimal iterations.
- o3-mini: Surprisingly creative but failed to follow the prompt completely (missed obstacle dynamics).
- Claude 3.7 + GPT-4o: Struggled, creating unusable or incomplete results.
Rust Code Refactoring
- Gemini 2.5 Pro: Clear winner, producing logical, memory-efficient changes.
- Claude 3.7: Lost to Gemini due to lower efficiency and redundant modifications.
- o3-mini and GPT-4o: Mediocre and lacked robustness.
🧰 The Resource Toolbox: Enhance Your Coding Game
Here are some handy tools to amplify your coding workflow:
- Dev Notes Newsletter: Free, valuable insights for developers.
- Micro Center Products: Offers cutting-edge laptops, monitors, and hardware at discounted prices.
- Notion Template for Students: Organize your coding projects and study schedules effectively.
- GitHub: Follow along for code examples straight from the video!
- Claude AI by Anthropic: Test Claude models and explore their coding capabilities.
- OpenAI GPT Models: For ideation, brainstorming, and debugging.
- P5.js Web Editor: Test creative coding ideas in real time.
💬 Closing Thoughts: Elevate Your Workflow
Each AI model has a specific forte and purpose. Deciding the right model depends on your project’s needs:
- For Precision Tasks: Claude 3.5 Sonnet is your best friend.
- For Wide-Scale Refactoring: Gemini 2.5 Pro gets the job done.
- For Experimental Code: o3-mini shines when simplicity is key.
- For Conversations & Chatting: GPT-4o excels here—leave the heavy lifting to others.
The key takeaway? 🎯 No single model dominates every use case, and the right choice can save hours of debugging and frustration. Embrace these tools as collaborators—not replacements!
