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benchmark-models

Execute identical prompts across Claude, GPT, and Gemini to compare latency, token consumption, and cost.

Updated May 9, 2026
Search Tags:#benchmark#performance-testing#cross-model#ai-models#model-selection#cost-tracking#llm-comparison
One-click install
npx skills add https://github.com/kk20300113-png/my-claude-skills --skill benchmark-models-kk20300113-png
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-models
Source: https://github.com/kk20300113-png/my-claude-skills/tree/main/benchmark-models
Command: npx skills add https://github.com/kk20300113-png/my-claude-skills --skill benchmark-models-kk20300113-png

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Choosing the right AI model for your work is usually based on hype or anecdotes instead of real performance data. This Skill removes the guesswork by running the same prompt across multiple LLM providers and measuring objective, comparable metrics.

Core Features & Use Cases

  • Cross-model side-by-side comparison: Run identical prompts through Claude, GPT, and Gemini to compare latency, token usage, and cost in a single table.
  • Optional quality scoring: Use an LLM judge to rate each model's output on a 0-10 scale to measure output quality, not just speed and cost.
  • Baseline tracking: Save benchmark results as JSON to compare future runs and catch model performance regressions over time.
  • Use Case: If you are deciding which model to use for a new coding skill, run this benchmark to see which provider delivers the best balance of speed, cost, and output quality for your specific prompt.

Quick Start

Use the benchmark-models skill to compare the performance of Claude, GPT, and Gemini on your specific task prompt to identify the best model for your needs.

Frequently Asked Questions about benchmark-models

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

FAQPage Schema
How do I compare LLM performance across Claude, GPT, and Gemini?▼

To compare LLM performance across Claude, GPT, and Gemini, you can execute identical prompts through each provider to collect objective metrics like latency, token consumption, and cost in a single comparable table.

Can I measure output quality and not just latency when benchmarking AI models?▼

You can measure output quality when benchmarking AI models by enabling an optional LLM judge feature, which rates each provider's response on a 0-10 scale to evaluate quality alongside latency and cost metrics.

What is the best way to track AI model performance regressions over time?▼

The best way to track AI model performance regressions is saving benchmark results as JSON baselines, allowing you to compare future runs against stored latency, token usage, and cost data to detect degradations.

Does cross-model performance testing support evaluating existing skills or only custom prompts?▼

Cross-model performance testing supports evaluating both arbitrary user prompts and existing skills, executing them identically across multiple providers to generate objective performance and cost data for comparison.

How do I calculate and compare token costs for different AI providers?▼

You calculate and compare token costs for different AI providers by running identical benchmark prompts across Claude, GPT, and Gemini, automatically tracking token consumption and calculating total cost for each model.

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