benchmark-models

Run the same prompt across Claude, GPT, and Gemini to compare latency, cost, and output quality.

Updated May 11, 2026

Search Tags:#models #quality #comparison #latency #benchmark #cost #gstack

Ready to run

npx skills add https://github.com/wodeh/gstack-kimi --skill benchmark-models-wodeh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: benchmark-models
Source: https://github.com/wodeh/gstack-kimi/tree/main/benchmark-models
Command: npx skills add https://github.com/wodeh/gstack-kimi --skill benchmark-models-wodeh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cross-model benchmarking isolates and quantifies how different AI models perform on the same task, enabling data-driven model selection across prompts and workflows.

Core Features & Use Cases

Cross-model prompt execution to compare latency, tokens, and cost across Claude, GPT, and Gemini.
Optional quality assessment via built-in judge integration and structured reporting.
Use Case: When choosing the best model for a given skill or workload, run a side-by-side assessment and select the model that offers the best balance of speed, cost, and quality.

Quick Start

Run a cross-model benchmark on a representative prompt to compare latency, cost, and quality.

Frequently Asked Questions about benchmark-models

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compare AI model performance for a specific prompt?▼

To compare AI model performance, you can run cross-model benchmarks that execute the same prompt across Claude, GPT, and Gemini to measure latency, token usage, and cost side-by-side. This isolates and quantifies how each model handles your specific task.

Can I evaluate output quality automatically when benchmarking language models?▼

Yes, you can evaluate output quality automatically during a language model benchmark by enabling optional judge integration. This assesses the model responses and provides structured reporting alongside the latency and cost metrics.

What is the best way to choose the right AI model for my workflow?▼

The best way to choose an AI model is to run a side-by-side cross-model benchmark on a representative prompt. This generates per-model metrics for speed, cost, and quality, helping you select the model with the optimal balance for your workload.

Does cross-model benchmarking work with Claude, GPT, and Gemini simultaneously?▼

Yes, cross-model benchmarking works with Claude, GPT, and Gemini simultaneously by running model availability checks and executing the same prompt across all three. It then aggregates the results into a final summary with per-model metrics.

How do I get a cost comparison between different AI models?▼

You get a cost comparison between AI models by running a cross-model benchmark with cost reporting enabled. It calculates the token usage and associated expenses for Claude, GPT, and Gemini on the same prompt, providing a detailed per-model breakdown.