LLMs & Chatbots

Claude Opus 4.6 vs GPT-5.4: Which Model Writes Better Code?

Ethan Rourke ·
Claude Opus 4.6 vs GPT-5.4: Which Model Writes Better Code?

Claude Opus 4.6 vs GPT-5.4: Which Model Writes Better Code?

Anthropic’s Claude Opus 4.6 and OpenAI’s GPT-5.4 are the two strongest coding models available in March 2026. Both companies claim top-tier performance on code generation benchmarks. Marketing claims do not help you pick the right model for your team. So we built a custom benchmark with 120 real-world coding tasks to find out which model delivers better production code for Claude Opus 4.6 coding and GPT-5.4 in practice.

Our benchmark covers Python, TypeScript, and Rust. Tasks range from simple utility functions to multi-file refactoring, API integration, and debugging existing code. Here is what we found.

Benchmark Design and Scoring

  • 120 tasks total: 40 Python, 40 TypeScript, 40 Rust. Each language includes 10 easy, 15 medium, and 15 hard tasks.
  • Scoring criteria: Correctness (does it pass tests?), first-attempt pass rate, code quality (linting, type safety), and completeness (does it handle edge cases?).
  • Environment: Both models tested through their official APIs with standard system prompts. Temperature set to 0 for reproducibility.
  • Context: Each task includes the relevant code context (imports, types, related functions) that a developer would normally have open.

Python Results: Claude Opus 4.6 Takes the Lead

Claude Opus 4.6 scored 84.2% first-attempt pass rate on Python tasks. GPT-5.4 scored 79.5%. The gap widened on hard tasks, where Claude hit 73.3% compared to GPT-5.4 at 66.7%.

Claude’s advantage in Python came from better handling of complex data transformations and library-specific code. When asked to write a pandas pipeline that merges three DataFrames with specific join conditions and aggregations, Claude produced correct code more often. GPT-5.4 tended to miss edge cases in multi-step data operations.

For simple Python tasks (file I/O, string processing, basic algorithms), both models were effectively identical at 95%+ pass rates.

TypeScript Results: GPT-5.4 Wins on Type Safety

GPT-5.4 scored 81.0% on TypeScript tasks. Claude scored 76.5%. GPT-5.4’s edge was in type inference and generic type handling. When tasks required complex TypeScript generics, mapped types, or conditional types, GPT-5.4 produced correctly typed code more consistently.

Claude compensated with better architectural decisions. On tasks that required structuring a multi-file TypeScript module, Claude’s code organization was cleaner and followed established patterns more closely. But the type errors in Claude’s output would require manual fixes in a strict TypeScript project.

“GPT-5.4 treats TypeScript types as a first-class concern. Claude treats them as annotations to add after the logic is right. For projects with strict type checking, that difference matters.” — From our benchmark notes.

Rust Results: Both Models Struggle, Claude Edges Ahead

Claude scored 62.5% on Rust tasks. GPT-5.4 scored 57.5%. Neither model handles Rust’s borrow checker reliably on complex tasks. Both produced code with lifetime annotation errors and incorrect ownership patterns on roughly one-third of medium and hard tasks.

Claude was better at implementing trait bounds correctly. GPT-5.4 was better at generating unsafe blocks with proper documentation (when unsafe was actually needed). For Rust, both models are useful as starting points but require more human review than Python or TypeScript output.

Debugging and Refactoring: Where the Real Differences Show

We also tested 30 debugging tasks where each model received broken code and needed to identify and fix the issue.

Claude found and fixed 83% of bugs correctly. GPT-5.4 fixed 78%. Claude’s debugging advantage was most visible in multi-file bugs where the error in one file was caused by a change in another file. Claude tracked cross-file dependencies more reliably.

For refactoring tasks (improve performance, reduce duplication, modernize syntax), the models performed equally well. Both produced clean, idiomatic refactored code about 80% of the time.

Code Quality Beyond Correctness

Passing tests is the minimum bar. We also evaluated readability, naming conventions, error handling, and documentation.

Claude produced better error handling. Its generated code included try-catch blocks, input validation, and descriptive error messages more consistently. GPT-5.4 sometimes returned the “happy path” only, skipping error cases unless explicitly asked.

GPT-5.4 wrote better inline comments and docstrings. Its documentation was more concise and useful. Claude’s comments tended to be verbose and sometimes restated what the code obviously does.

Both models followed naming conventions well. Neither produced code with poor variable names or inconsistent casing in our tests.

Which Coding Model Should You Choose?

The answer depends on your primary language and workflow.

  1. Python-heavy teams: Start with Claude Opus 4.6. Its edge on complex Python tasks and debugging is meaningful.
  2. TypeScript/React teams: Start with GPT-5.4. Better type safety saves review time in strict TypeScript projects.
  3. Rust teams: Neither model is reliable enough to use without careful human review. Claude has a slight edge but not enough to recommend strongly.
  4. Multi-language teams: Consider running both models and comparing output on critical tasks. The best model varies by task type.

Both models are strong enough for production use with human review. The gap between them is smaller than the gap between either model and the generation before it. Pick the one that fits your stack, and spend your time on the review process rather than the model choice.