benchmark-models

To benchmark LLM performance, you run the same prompt through Claude, GPT via Codex CLI, and Gemini to collect and compare latency, token usage, and cost data side-by-side.

Yes, you can compare model latency and token cost by executing an identical prompt across multiple providers and aggregating the structured execution metrics for analysis.

The best way to select an AI model is to run cross-model benchmarks measuring latency, tokens, and cost, optionally adding an LLM judge to evaluate output quality and validate choices.

Yes, you need provider authentication for each service. The benchmark coordinates authentication across Claude, GPT via Codex CLI, and Gemini to execute prompts and gather results.

Cross-model benchmarking primarily evaluates performance metrics like latency and cost, but it optionally uses an LLM judge to surface models with the best output quality.