Codex vs Copilot vs Claude Sonnet 4: AI-Developer in Real-World Combat
- Krzysztof Kosman
- Sep 10
- 6 min read
Why AI-Powered Coding Is No Longer Science Fiction
Remember when auto-complete in your IDE felt like magic? Today, the new normal is a sidebar buddy or a chat window with a "coder brain" hotwired straight into every keystroke. AI code assistants like Codex, GitHub Copilot, and the new Claude Sonnet 4 from Anthropic don’t just finish your for-loops—they suggest architectures, invent schema migrations, and draft unit tests on the fly. But do they actually help in the daily grind, or are we all caught up in the hype cycle?
If you’re a developer or CTO, you’ve probably heard big claims and seen benchmark comparisons. This time, let’s skip the synthetic benchmarks. Instead, we’ll walk through authentic, messy project scenarios, looking for practical advantages, hidden pain points, and the ultimate question: which AI would you actually want as your battle-hardened coding partner?
The Surge of AI Pair Programming in Everyday Workflows
Why are so many teams adding AI code assistants to their stack now? Because real projects are messy. EdTech companies, SaaS teams, and digital agencies don’t just code new features—they wrestle with onboarding legacy code, fixing unpredictable bugs, running migrations, and reviewing someone else’s spaghetti. These aren’t toy problems—they’re a brutal test of whether an AI coder truly ‘gets it’.
Across the fast-changing landscape, two things are clear:
AI is now "familiar"—Tens of millions of coders are living with Copilot, ChatGPT, or their rivals as embedded teammates.
Teams want accuracy, context-awareness, and reliability more than witty completions or raw speed.
But real outcomes depend on integrations, codebase context, and how quickly an AI learns your project’s quirks.
In this article, I’ll refer to developer chats and hands-on experiments—rather than just the vendor gloss. For further EdTech context, I’ll reference the ongoing debate on what AI actually changes in product teams (see our past review for OpenAI Codex + VS Code, and the analysis piece from The New Stack).
How We Tested: The Not-So-Pretty Realities
Forget lab benchmarks. We dropped Codex, Copilot, and Claude Sonnet 4 into daily work for a medium-sized, multi-featured EdTech platform (modals, model changes, database migrations, old React code, and an endless backlog). The questions were simple:
Which AI assistant catches the context and integrates with your workflow best?
How does each react when you get something wrong, or the interface doesn’t “just work”?
Do AI tools help reduce cognitive friction, or do they introduce new types of guesswork?
Our tester, Damian, shared frank notes from 1:1 experiments:
“Claude suddenly started running tests, checking for mistakes and even offered to check the browser UI for issues. Codex did the migration but didn’t suggest running it. Copilot’s stable, but not proactive—less ‘wow’, more business as usual.”
Claude Sonnet 4: The Devil’s in the Details
What’s impressive:
Codebase-level awareness: Claude actively asks whether to review database schema or trigger migrations, going beyond just the current file or function.
Proactive behavior: As soon as a migration is suggested, Claude asks “Should I run this migration now or do you want a code review first?” followed by discrete button prompts in its interface.
Testing and feedback loop: Before you even explicitly request it, Sonnet 4 asks to run coverage checks and launch browser previews. This doesn’t just save time; it builds developer confidence.
Project realism: When implementing a modal that didn’t work the first time, Claude not only fixed the initial error but offered layered solutions—diagnosing trigger errors, correcting event bindings, then running final UI and unit test verifications. Three iterations deep—with clear incremental summary at each step.
Biggest hidden value? Its lived-in feel: after some real use, Claude seems to "pick up" on your project’s quirks, making its suggestions increasingly context-relevant.
For a hands-on, independent review of Claude vs. other assistants, check out ZDNet’s in-depth test: Anthropic Claude vs. GitHub Copilot: Which is best for coding?
Where Claude Sonnet 4 Still Falters:
Still hallucinates—occasionally invents unprompted syntax or functions, though less than earlier LLMs.
Some advanced context handoff demands a bit of upfront project interaction; magic takes a while to kick in.
Codex: Very Fast, Sometimes Shallow
Codex shines in raw speed, generating fully-formed code blocks for migrations, modals, or data models within seconds. It plugs directly into VS Code (and other IDEs) and feels natively “plug-and-play”.
Strengths:
Lightning-quick completion: Very attentive to immediate code surroundings, producing canonical solutions with solid syntax discipline.
Repeatable output: If you want boilerplate, consistent code, Codex is reliable (and captures team conventions well after a few sessions).
Limitations:
Shallow problem-solving: Codex focuses on file-level context, rarely proposing broader architectural changes or proactively asking to test or trigger things.
Feedback fatigue: In the case of a "modal not working" issue, Damian reported, "Even when I told Codex three times what the trigger should be, it kept rewriting trivial code fragments without grasping the root cause."
Codex can feel like a mechanical, if fast, pair-coder—a shot of energy for CRUD tasks, but you’ll need to explicitly drive feedback and testing cycles.
For past impressions of Codex in EdTech dev cycles, see our earlier review: Codex + VS Code: The AI Coding Experience You Have to Feel to Believe
GitHub Copilot: Reliable, Familiar, But Less Proactive
Copilot remains the most widely adopted AI code “sidekick”—particularly for teams with a clear GitHub workflow or a legacy codebase.
What Copilot gets right:
Stable feature set and robust VS Code/JetBrains integration: It’s almost invisible in your daily routine—just use Tab/Enter and move.
Solid code reviews: Copilot catches missing imports, argument mismatches, and even hints at security gaps during reviews (if you prompt it).
What you may miss:
Copilot is largely reactive, not anticipatory. It won’t propose running a migration, nor prompt you to run tests unless asked.
Its "intelligence"—at least for now—is strongly code-completion focused, not architecture-level or test-oriented.
Yet, for many teams, "stable and boring" is a feature, not a bug. Copilot serves as a reliable helper for refactors and basic automation (“fine for code review,” as our developer put it).
Human-Developer vs. AI-Developer — It’s About the Feedback Loop
The differences aren’t just in code completion or bug fixing. The most tangible benefit for daily development is the feedback loop between what the AI suggests and how it adapts to your project’s actual state.
Claude Sonnet 4 builds a ‘shared context’ over time: By constantly checking project state, database status, and even browser UI, it behaves like an attentive, proactive junior developer. Its questions (“Can I see the database schema? Should I run tests?”) are golden for data-heavy EdTech work.
Codex is reactive and persistent: It will do what you ask, repeatedly, sometimes to a fault. This can be perfect for batch migration work or templating but may cause repetition if you’re not articulate in prompts.
Copilot is the status quo partner: You seldom notice it, for better or worse. It completes what’s in your head but rarely pushes you to check for test failures, unfinished migrations, or business logic landmines.
Notably, UX differences accumulate over time
For example: Codex asks you to manually run php artisan migrate after generating a migration file. Claude Sonnet 4, on the other hand, directly prompts: “Should I launch the migration and tests?” with ok/cancel buttons—closing the feedback loop for you. These seemingly small details are what elevate developer trust and flow.
So Who Wins—And When?
For teams wanting proactive testing, smarter code review, and learning behavior: Claude Sonnet 4 is currently the best-in-class, especially as your codebase grows more complex. The more it sees, the more it adapts. For hands-on, unbiased coverage, see this GeekWire test run.
For templating, speed, and “set-and-forget” code gen: Codex is the tool of choice—if you don’t mind leading the way at every step.
For reliability and the need to minimize distraction: Copilot holds steady. It’s easy to onboard and scales cleanly with any mainstream IDE and GitHub workflow.
One caveat: None of these tools eliminates code review, tester feedback, or human debugging. In fact, the best results come from blending the AI’s strengths with a clear, opinionated codebase and strong developer hygiene.
Further Reading:
Takeaways for Teams and CTOs
Test in your real codebase before picking a winner. Small workflow details matter more than headline features.
Prioritize the feedback loop—does the AI catch your bugs, spot code smells, and help you finish the job and not just the file?
For EdTech or SaaS—where migrations, tests, and legacy code are bread and butter—tools that “ask more” will save the most time in the long run.
What surprised us the most? The "detailing"—how an AI that nudges you (to run tests, review migrations, sync database changes) increases both trust and delivery speed. It’s no longer who is fastest, but who is most helpful after generating code.
Curious about the future?
Next time, we’ll look at how real teaching teams are blending these AI coders into live hackathons, and what’s changing as EdTech tools become AI-native. Meanwhile—what’s your real-world AI coding story?
For more insights on AI code review and actionable EdTech developer strategies, check out: AI for Code Review: Copilot, ChatGPT, and Workflow Realities
