gstack-benchmark-models

Run the same prompt across Claude, GPT, and Gemini to compare latency, token usage, and cost side-by-side. This benchmarking approach measures performance and spend, with an optional LLM judge scoring output quality.

Prompt evaluation for model selection executes the identical prompt through different AI models to compare measurable outcomes. It produces a tabular summary of latency, tokens, and cost to remove guesswork from choosing the best model.

Yes, an auth-aware dry-run readiness check verifies which LLM providers are authenticated and ready before executing a paid benchmark. This safe preflight workflow ensures providers are available before comparing latency and token usage.

Save benchmark results as JSON to create repeatable baselines for model performance. Comparing future runs against saved latency, token, and cost data detects performance drift over time.

The best way to compare AI model quality and spend is running a repeatable prompt-based shootout across providers. It measures cost against an optional LLM judge score for output quality, producing a tabular summary for direct comparison.