benchmark-models

To compare AI model performance, you run the same prompt or gstack skill across multiple providers like Claude, GPT, and Gemini side-by-side. This benchmarks latency, token usage, and cost to eliminate guesswork in model selection.

An LLM judge is an optional scoring mechanism that evaluates output quality on a 0–10 scale during model benchmarking. It helps determine the best overall model when response quality matters as much as speed and cost.

You do not need to authenticate all providers beforehand. The benchmark performs a dry run first to check provider readiness, then automatically executes the benchmark only across the authed providers.

The best AI model recommendation is based on collected performance metrics including latency, token cost, and usage. It applies to model selection where skill behavior matters more than raw generation speed.

You should run recurring provider shootouts periodically because models and providers drift over time. Re-checking ensures you maintain the best balance of latency, token cost, and output quality for your workflows.