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benchmark-models

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

1|Updated May 27, 2026
Search Tags:#models#quality#latency#benchmark#cost#gstack
One-click install
npx skills add https://github.com/mi179/claude-skills --skill benchmark-models-mi179
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-models
Source: https://github.com/mi179/claude-skills/tree/main/user/gstack/benchmark-models
Command: npx skills add https://github.com/mi179/claude-skills --skill benchmark-models-mi179

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Benchmark cross-model performance by running the same prompt across Claude, GPT, and Gemini to compare latency, tokens, cost, and quality.

Core Features & Use Cases

  • Cross-model prompt evaluation across Claude, GPT, and Gemini to generate a data-backed comparison.
  • Outputs latency, token usage, cost, and optional quality scores to identify the best-performing model for given prompts.
  • Use Case: Evaluate which model best suits your gstack workflows for tasks like plan reviews, code generation, and data queries.

Quick Start

Run the benchmark by selecting a prompt and models, then review the results.

Frequently Asked Questions about benchmark-models

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

FAQPage Schema
How do I compare Claude, GPT, and Gemini performance for the same prompt?▼

Cross-model benchmarking evaluates Claude, GPT, and Gemini by running the identical prompt across all configured providers to compare latency, token usage, cost, and quality metrics in a single comparison table.

What is the best way to measure LLM latency and cost in gstack workflows?▼

To measure LLM latency and cost in gstack workflows, run a cross-model benchmark that outputs per-model metrics for token usage and response times, generating a data-backed comparison to identify the optimal model.

Do I need a specific environment to benchmark cross-model performance?▼

Benchmarking cross-model performance requires access to the gstack benchmark binary and properly configured model providers for Claude, GPT, and Gemini before you can execute prompt evaluation tasks.

Can I evaluate model quality for code generation and data queries?▼

Evaluating model quality for code generation and data queries is supported by running targeted prompts through the benchmark, which outputs optional quality scores alongside cost and latency data for each model.

How do I identify which model best suits my prompts for plan reviews?▼

To identify which model best suits plan review prompts, execute a cross-model benchmark that generates a comparison table detailing per-model metrics, allowing you to select the best performer for your gstack tasks.

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