benchmark-models

Cross-model benchmarking compares prompt responses across Claude, GPT (Codex CLI), and Gemini by measuring latency, token usage, and cost to help you choose the best model. The workflow includes prompt selection, provider readiness checks, and reviewing benchmark results.

You can evaluate output quality during cross-model benchmarking by enabling an LLM judge. This optional feature assesses the quality of prompt responses alongside the standard latency, token usage, and cost metrics to provide deeper performance insights.

Cross-model benchmarking tracks latency, token usage, and cost metrics across different AI providers. By running a dry-run and selecting specific providers, you surface these quantitative measurements to compare model performance for specific tasks.

Yes, you need a functioning gstack-benchmark-models binary and authenticated providers to run cross-model benchmarks. The workflow requires valid authentication for Claude, GPT (Codex CLI), and Gemini before initiating provider selection and benchmarking steps.

Use cross-model benchmarking when evaluating model choices for gstack skills, running experiments, or comparing AI vendors. It helps teams determine the best fit for a given task by revealing differences in latency, token usage, and overall cost across providers.

The best way to compare AI model costs and latency is through a structured cross-model benchmark workflow. Initiate a benchmark, select your prompt and target models, optionally enable a quality judge, and review the generated results and recommendations.