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benchmark-models

Run identical prompts across AI model providers to measure latency, token usage, and cost.

Updated Jun 5, 2026
Search Tags:#performance#llm#comparison#latency#benchmark#cost
One-click install
npx skills add https://github.com/smarmen86/claude-code-kit --skill benchmark-models-smarmen86
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: benchmark-models
Source: https://github.com/smarmen86/claude-code-kit/tree/main/skills/benchmark-models
Command: npx skills add https://github.com/smarmen86/claude-code-kit --skill benchmark-models-smarmen86

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill removes the guesswork from choosing an AI model by providing empirical data on latency, cost, and output quality for your specific tasks.

Core Features & Use Cases

  • Cross-Model Comparison: Runs the same prompt through Claude, GPT, and Gemini simultaneously to generate a side-by-side performance report.
  • Quality Benchmarking: Optionally uses an LLM judge to score output quality, ensuring you select the model that best fits your accuracy requirements.
  • Use Case: If you are unsure whether to use Claude or GPT for a complex engineering task, this skill will run your prompt through both and provide a table showing which one is faster, cheaper, and more accurate.

Quick Start

Invoke the benchmark-models skill to compare how different AI models handle your current project task.

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 my engineering tasks?▼

To compare AI model performance, this skill executes identical prompts across multiple model providers simultaneously, measuring latency, token usage, and cost efficiency to generate a side-by-side performance report.

What is the best way to benchmark LLM latency and cost across different providers?▼

The best way to benchmark LLM latency and cost is to run your specific prompt through Claude, GPT, and Gemini simultaneously, using local gstack configurations to compare performance metrics and output quality.

How does an LLM judge evaluate output quality during model comparison?▼

An LLM judge evaluates output quality during model comparison by scoring the responses generated from your identical prompts, ensuring you select the model that best fits your accuracy requirements.

Can I use this to measure token usage and cost efficiency for specific workflows?▼

Yes, you can measure token usage and cost efficiency for specific workflows by running your project task through multiple AI model providers to receive comparative performance metrics.

Do I need local gstack configurations to run a cross-model comparison?▼

Yes, you need local gstack configurations because the skill integrates with them to provide comparative performance metrics and facilitate data-driven model selection for your skill-based tasks.

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