benchmark-models

Cross-model AI benchmarking solves the problem of objectively comparing performance on the same prompt across Claude, GPT, and Gemini. It helps you choose the best model for your workflows by measuring latency, token usage, and cost without manual reruns.

You compare AI model output quality by optionally enabling an external quality judge during the benchmark workflow. This assesses the output quality alongside the latency and cost metrics for each selected provider.

The best way to evaluate which AI model suits a specific task is to run a cross-model benchmark. By selecting a prompt source and choosing authed providers, you can compare performance metrics and optionally save the results for future comparisons.

You need to authenticate with your chosen providers to run a cross-model benchmark. The guided workflow includes interactive prompts to verify authentication and select the specific authed providers you want to compare before running the benchmark.

You can save your AI model comparison results for later analysis. The benchmark workflow optionally saves the per-provider results, including latency, tokens, and cost, to a JSON file for future reference or model update comparisons over time.