Gemini 3 Pro vs GPT-5 Coding Test: We Ignored the Scores and Just Let Them Code

Gemini 3 Pro vs GPT-5 Coding Test Results

Quick Summary: Key Takeaways

  • The Verdict: GPT-5 wins on pure logic and "LeetCode" style problems, but Gemini 3 Pro dominates in real-world, large-context system architecture.
  • The Context Gap: Gemini's 1M+ token context window allowed it to refactor entire legacy repositories where GPT-5 (400k limit) lost the thread.
  • The Budget Option: For simple debugging, DeepSeek V3 offers comparable performance to the giants at nearly 95% lower cost.
  • The "Benchmark Lie": A perfect 100% score on the AIME benchmark didn't prevent GPT-5 from hallucinating imports in a React codebase.

The leaderboards say the war is over. If you look at the charts, GPT-5 has achieved a staggering 100% on math benchmarks like AIME 2025, ostensibly making it the smartest entity on the planet.

But when you are staring at a 5,000-line "spaghetti code" legacy file at 2 AM, you don't care about math Olympiad medals. You care about whether the AI can understand your specific, messy, undocumented reality without crashing.

This deep dive is part of our extensive guide on Interpreting LLM Benchmark Scores: Why "Humanity’s Last Exam" is Lying to You.

The Setup: Real Work vs. "Humanity's Last Exam"

Most comparisons test models on isolated functions, generating a Fibonacci sequence or solving a Sudoku puzzle. We didn't do that. We ran a Gemini 3 Pro vs GPT-5 coding test focusing on the three tasks that actually consume developer time:

Here is what happened when we ignored the scores and just let them code.

Test 1: The "Context" King (Refactoring)

The Task: We fed both models a zip file containing 150 interconnected Python files (approx. 800k tokens) and asked them to "Identify circular dependencies and propose a modular refactor."

GPT-5 High: It choked. With a context limit of around 400k tokens, it couldn't ingest the full repo. We had to chunk the data. It correctly identified dependencies in local chunks but failed to see the global architecture, suggesting a refactor that would have broken the database layer.

Gemini 3 Pro: It ingested the entire 1M+ token context effortlessly. It didn't just find the circular dependencies; it wrote a migration script to decouple them.

Winner: Gemini 3 Pro. In 2026, context is intelligence. If the model can't "see" the whole project, its high IQ is useless.

Test 2: The Logic Engine (Greenfield Build)

The Task: "Write a single-file Python script that simulates a gravitational n-body problem with Runge-Kutta integration."

Gemini 3 Pro: It wrote code that worked, but it was verbose. It hallucinated a library scipy.integrate.ode_rk4 that doesn't exist, requiring a follow-up prompt to fix.

GPT-5 High: Perfection on the first try. The code was highly optimized, using vectorization (NumPy) instead of loops. It felt like watching a master mathematician work.

Winner: GPT-5. For pure logic generation where the prompt is self-contained, OpenAI's "reasoning" focus still holds the crown.

Test 3: The Debugging Assistant (DeepSeek vs. The Giants)

We threw a curveball. We tested DeepSeek V3 against the giants for a simple debugging task.

The Task: Find the syntax error in a 50-line JavaScript file.

Winner: DeepSeek. For simple tasks, paying top-tier prices is burning money. This raises the question: is the performance worth the cost? often, the answer is no.

Conclusion: Choose Your Weapon

The "best" model doesn't exist. There is only the best tool for the specific job.

Don't let the Gemini 3 Pro vs GPT-5 coding test scores dictate your stack. Trust the results, not the leaderboard.

Edit Video by Editing Text. Try Fathom AI for Free.

Fathom AI Meeting Notetaker

We may earn a commission if you buy through this link. (This does not increase the price for you)

Frequently Asked Questions (FAQ)

Q1: Which AI writes better Python code: Gemini or GPT-5?

For modern, vectorized, and algorithmically complex Python, GPT-5 is superior. For maintaining large, multi-file Python projects where context matters, Gemini 3 Pro is better.

Q2: Can Gemini 3 Pro handle legacy code refactoring?

Yes, it is currently the market leader for this. Its massive context window (over 1 million tokens) allows it to "read" entire legacy repositories at once, ensuring it doesn't break hidden dependencies during refactoring.

Q3: Does GPT-5 hallucinate less in coding tasks?

In self-contained logic tasks, yes. However, when forced to work with codebases larger than its context window, hallucination rates increase significantly as it tries to "guess" missing code.

Q4: Which model is better for debugging: DeepSeek or Gemini?

For complex, cross-file logic bugs, Gemini 3 Pro is better due to context. For syntax errors and single-file logic, DeepSeek is equally capable and significantly cheaper.

Q5: Real-world coding performance vs benchmark score?

Benchmarks like HumanEval or SWE-bench test isolated snippets. Real-world performance requires understanding project structure, which is why models with lower scores but higher context (like Gemini) often win in production.

Sources & References

Back to Top