benchmark-models

Benchmark AI models by running the same prompt through multiple providers to compare latency, token usage, and cost. This generates a table with per-model metrics, allowing data-driven decisions for which AI model best suits a specific task.

Yes, you can evaluate output quality during model comparison by using an optional LLM judge. The judge evaluates the prompt responses from different models, adding quality scores to the latency and cost metrics to inform your final model choice.

Cross-model benchmarking runs identical prompts through different models like Claude, GPT, and Gemini to compare performance. You need it to decide which model is best for a given skill based on data rather than vibes.

Run a side-by-side prompt comparison by executing your prompt through selected model providers. The benchmark surfaces latency, token usage, and price per model, outputting the results as a table or JSON for analysis.

Yes, you need access to model providers and the gstack-model-benchmark tool to benchmark latency and token usage. These prerequisites enable the cross-model comparison and generate the per-model metrics output.

The best way to choose an AI model based on data is to run cross-model benchmarking. This process compares latency, cost, and optional judge quality results across providers, outputting a metrics table to guide your selection.