BridgeBench

BridgeBench is a comprehensive AI coding model benchmarking platform built by BridgeMind that evaluates and ranks leading AI models across multiple coding-related categories. It provides up-to-date leaderboards covering UI generation, security, refactoring, hallucination resistance, debugging, speed, and cost efficiency. The platform also includes a dedicated hardware benchmark for local inference on NVIDIA DGX Spark, and a community voting system for best vibe-coding models.

• UI Benchmark — Ranks models on their ability to generate user interface code, scored on quality and accuracy.
• Security Benchmark — Evaluates models on identifying and handling security vulnerabilities in code.
• Refactoring Benchmark — Measures how well models restructure and improve existing code while preserving intent.
• Hallucination Benchmark — Tracks fabrication rates and overall reliability of model outputs in coding contexts.
• Debugging Benchmark — Scores models on diagnosing and fixing bugs across a range of code samples.
• Speed Benchmark — Measures tokens per second and time-to-first-token (TTFT) for each model.
• Cost Efficiency Benchmark — Derives strict-success economics from debugging and security runs to rank models by cost-per-win.
• DGX Spark Bench — Dedicated leaderboard for local model inference performance on NVIDIA DGX Spark hardware.
• Community Voting — Allows signed-in users to rank their top frontier AI models for vibe coding.
• Model Detail Pages — Each model has its own page with per-benchmark scores and run details.