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benchmark-models

Benchmark Claude, GPT, and Gemini models on latency, cost, and output quality.

2|1|Updated Mar 5, 2026
Search Tags:#quality#latency#benchmark#model#cost#multimodel#gstack
One-click install
npx skills add https://github.com/vnmoorthy/Skylog --skill benchmark-models-vnmoorthy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-models
Source: https://github.com/vnmoorthy/Skylog/tree/main/.claude/skills/gstack/benchmark-models
Command: npx skills add https://github.com/vnmoorthy/Skylog --skill benchmark-models-vnmoorthy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Cross-model benchmarking across Claude, GPT, and Gemini to compare latency, token usage, cost, and perceived quality for gstack prompts.

Core Features & Use Cases

  • Cross-model prompt benchmarking across multiple providers to surface speed, cost, and quality differences.
  • Flexible prompt sources and model selection with optional quality judging to guide tool choices.
  • Use case: compare how different models perform on a representative gstack task to inform provider decisions.

Quick Start

Run a full cross-model benchmark across Claude, GPT, and Gemini using the gstack-model-benchmark binary and review latency, cost, and quality results.

Frequently Asked Questions about benchmark-models

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

FAQPage Schema
How do I benchmark large language model performance across Claude, GPT, and Gemini?▼

Cross-model benchmarking measures latency, token usage, cost, and perceived quality across Claude, GPT, and Gemini for gstack prompts. You select prompt sources, choose providers, and optionally enable quality judging to compare outputs.

What is the best way to compare LLM latency and cost for prompt evaluation?▼

Comparing LLM latency and cost involves running prompts through multiple providers to surface speed and expense differences. This benchmark generates a human-readable table and optional JSON artifact showing token usage, cost, and latency.

Do I need configured credentials for Claude, GPT, and Gemini to run a multi-model benchmark?▼

Configured credentials for Claude, GPT, and Gemini are required to run a multi-model benchmark. You also need the local gstack-model-benchmark binary installed locally to execute the prompt evaluation and generate results.

Can I measure output quality differences when comparing models?▼

Measuring output quality differences is supported through optional quality judging. You can enable this feature during cross-model benchmarking to assess and compare perceived quality alongside latency, token usage, and cost metrics.

What format are cross-model benchmark results output in?▼

Cross-model benchmark results are output as a human-readable table and an optional JSON artifact. This format presents latency, token usage, cost, and quality measurements for the evaluated gstack prompts across selected providers.

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