Top AI Models: Best LLMs for Coding

Updated: June 13, 2025

This leaderboard aggregates performance data on various coding tasks from several major coding benchmarks: Livebench, Aider, ProLLM Acceptance, WebDev Arena, and CanAiCode. Models are ranked using Z-score normalization, which standardizes scores across different benchmarks with varying scales. The final ranking represents a balanced view of each model's overall coding capabilities, with higher Z-scores indicating better performance relative to other models.

* Scores are aggregated from various benchmarks using Z-score normalization. Missing values are excluded from the average calculation.

Understanding the Leaderboard

Z-Score Avg: This shows how well a model performs across all benchmarks compared to other models. A positive score means the model performs better than average, while a negative score means it performs below average. Think of it as a standardized "overall performance score."

Std. Dev. (Standard Deviation): This measures how consistent a model's performance is across different benchmarks. A low value means the model performs similarly across all benchmarks, it's consistently good (or consistently average) at everything. A high value means the model's performance varies, it might be exceptional at some tasks but struggle with others.

Benchmark Emojis: 🥇, 🥈, 🥉 indicate that the model ranked in the top 3 for that specific benchmark. ⭐️ indicates the model is in the top 15% for that specific benchmark (excluding the top 3 medalists).

About the Benchmarks: