OpenAI · 1 Arena leaderboard
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Links and model details
Crowd-ranked benchmarks from arena.ai · snapshot 2026-04-15T06:47:52.332462+00:00.
| Leaderboard | Rank | Elo | Votes |
|---|---|---|---|
| code | 38 | 1335.1 | 7,765 |
Generate project setup, configuration files, repetitive code structures
Example
Create React components with TypeScript, API endpoints with tests, database schemas
Reduce setup time by 50-70%, maintain consistent code patterns across team
Understand unfamiliar code, generate docstrings, create technical documentation
Example
Explain complex algorithms, document API endpoints, generate README files
Onboard developers 2-3x faster, maintain up-to-date documentation automatically
Identify potential bugs, security issues, performance problems
Example
Catch SQL injection vulnerabilities, find race conditions, suggest optimizations
gpt-5.2-codex is in the explainx.ai LLM directory. OpenAI · 1 Arena leaderboard. It is labeled closed or API-first, with publisher field OpenAI and license Proprietary. Structured FAQs below clarify source, weights, and benchmark data. Canonical URL: /llms/gpt-5.2-codex.
Listing on explainx.ai. Information may change; verify with the publisher.
Catch 60-70% of common issues before human review, improve code quality
Modernize legacy code, migrate between languages/frameworks
Example
Convert JavaScript to TypeScript, refactor class components to hooks, migrate Python 2 to 3
Accelerate technical debt reduction, de-risk migration projects
Prerequisites
Time Estimate
1-2 hours for API integration, 15 minutes for IDE extension
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Code-specialized transformers trained on public code repositories (GitHub, Stack Overflow), optimized for programming languages and syntax.
✓ Use when
Use for boilerplate generation, code explanation, documentation, refactoring suggestions, and learning new technologies. Best for accelerating development on well-understood problems.
✗ Avoid when
Avoid for: security-critical features (auth, crypto, payments), complex business logic requiring deep domain knowledge, performance-critical code, or when understanding WHY code works is more important than speed of generation.
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