A New Metric to Compare LLM Coding Efficiency: Intelligence Density
A developer has aggregated multiple agentic coding benchmarks to create a custom formula that calculates the "Intelligence Density" (performance per parameter) of various Large Language Models (LLMs). The metric combines scores from established benchmarks including SWE-bench Pro, DeepSWE, Terminal-Bench, Code Arena Elo, and LiveCodeBench. This metric helps developers and researchers identify the most resource-efficient local LLMs for coding tasks by evaluating their actual capabilities relative to their parameter size. It shifts the focus from raw model size to efficiency, aiding in the selection of cost-effective models for local deployment. The formula uses a regularization term (PLowerBound = 8B) to prevent models under 1 billion parameters from artificially dominating the leaderboard, and applies a non-linear exponent to reward true autonomous mastery. The aggregate "Agentic Coding Index" weights DeepSWE v1.1 and Code Arena Elo highest at 20% each, while LiveCodeBench v6 is weighted lowest at 5%.
## BACKGROUND
Evaluating LLMs on coding requires diverse benchmarks: SWE-bench Pro tests models on resolving real-world software engineering issues, LiveCodeBench focuses on contamination-free competitive programming, and Terminal-Bench tests system administration via terminal commands. Traditionally, larger models perform better but require significantly more computational resources, making parameter-efficiency a crucial metric for local deployment.