# LLM Selection Tool: A Model Comparison Tool Based on Multi-Dimensional Percentile Scoring

> An interactive large language model comparison tool based on Artificial Analysis data. It supports custom metric weights and real-time reordering, and intuitively displays the trade-off relationships between models in dimensions like intelligence, price, speed, and latency through 2D/3D Pareto frontier charts.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-21T01:33:24.000Z
- 最近活动: 2026-05-21T01:47:44.734Z
- 热度: 150.8
- 关键词: LLM, 大语言模型, 模型对比, Artificial Analysis, 帕累托前沿, 模型选型, AI基础设施, Python工具
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-3c23c22a
- Canonical: https://www.zingnex.cn/forum/thread/llm-3c23c22a
- Markdown 来源: floors_fallback

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## LLM Selection Tool: Guide to Multi-Dimensional Model Comparison Tool

This article introduces the open-source project llm-comparison, which is based on authoritative evaluation data from Artificial Analysis. It solves the multi-dimensional trade-off problem in LLM selection, supports custom metric weights and real-time reordering, and intuitively displays the relationships between models in dimensions like intelligence, price, speed, and latency through 2D/3D Pareto frontier charts.

## Project Background and Core Issues

The current LLM market is growing explosively, and traditional single-metric comparisons cannot reflect real-world scenarios. Production-level applications need to consider dimensions such as intelligence level, cost-effectiveness, response speed, first-token latency, and context window simultaneously. There are trade-offs between these metrics (improvement in one may lead to decline in another), and this tool aims to solve the problem of multi-dimensional optimal selection.

## Technical Implementation and Core Algorithm

The project is implemented using pure Python standard libraries with no external dependencies; the code structure is separated (command-line entry, core logic, HTML templates). The core algorithm is direction-aware percentile scoring: for metrics where higher values are better, original percentiles are used; for those where lower values are better, inverse percentiles are used. The composite score is the average of selected dimensions, ensuring dimensionless, consistent direction, and fairness.

## Visualization Features and Use Cases

Visualizations include: basic table view (sortable), 2D Pareto scatter plot (trade-off between two dimensions), 3D interactive scatter plot (trade-off between three dimensions). Use case examples: budget-sensitive (intelligence + price), real-time interaction (speed + latency + intelligence), comprehensive comparison (multiple metrics).

## Data Update and Maintenance

Data source: Artificial Analysis. Update steps: 1. Copy the ranking data to input.txt; 2. Run convert_results.py to generate results.csv. A semi-automated mechanism ensures data accuracy.

## Limitations and Improvement Directions

Limitations: manual data acquisition, equal weights, no historical trends, limited visualization forms. Improvement directions: automated data acquisition, custom weights, historical trend analysis, more visualization types (radar charts, heatmaps, etc.).

## Summary and Insights

The project solves practical selection problems, and its design philosophy is worth learning (single dependency, data-driven, flexible and easy to use, visualization-first). It provides a lightweight and complete starting point for LLM selection teams, which can adjust the logic or integrate into CI/CD processes.