benchmark-models

You can benchmark cross-model performance by running the same prompt through Claude, GPT, and Gemini to generate a structured report comparing latency, token usage, and cost.

Cross-model benchmarking is the process of running identical prompts through multiple providers like Claude, GPT, and Gemini to compare performance. You need it for data-driven model selection when deciding which model handles a specific task best.

Yes, cross-model comparison supports optional quality evaluation via an LLM judge. This allows you to assess output quality alongside latency, token usage, and cost metrics for a comprehensive comparison.

No, you cannot use this benchmarking approach without a running gstack environment. It specifically requires the gstack-model-benchmark binary to be available to execute cross-model comparisons.

The best way to run a model shootout is to execute a representative prompt across Claude, GPT via Codex CLI, and Gemini simultaneously. This surfaces measurable differences in latency, tokens, and cost to inform your model choice.

You can determine the best LLM by running your specific skill prompt through multiple providers and reviewing the structured report of latency, cost, and optional quality. This provides measurable data instead of subjective opinions for model selection.