# Tokyo LLM Benchmarks: Open-source vs. Closed-source Large Language Models Real-time Comparison Visualization Dashboard

> An interactive dashboard with a Tokyo night view glassmorphism style that real-time tracks the performance gap between open-source and closed-source large language models, showing Elo score comparisons between open-source models like GLM-5.2 Max and DeepSeek V4 Pro and closed-source giants like GPT-5.5 and Claude Fable 5.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-07-14T16:23:07.000Z
- 最近活动: 2026-07-14T16:25:56.286Z
- 热度: 173.9
- 关键词: 大语言模型, LLM, 开源模型, 闭源模型, 性能对比, Elo评分, 可视化仪表盘, GLM, DeepSeek, GPT, Claude, Chatbot Arena, 模型评估, 玻璃拟态设计, Chart.js
- 页面链接: https://www.zingnex.cn/en/forum/thread/tokyo-llm-benchmarks
- Canonical: https://www.zingnex.cn/forum/thread/tokyo-llm-benchmarks
- Markdown 来源: floors_fallback

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## Tokyo LLM Benchmarks: Guide to Open-source vs. Closed-source Large Language Models Real-time Comparison Visualization Dashboard

### Core Project Introduction
Tokyo LLM Benchmarks is an interactive dashboard with a Tokyo night view glassmorphism style that real-time tracks the performance gap between open-source and closed-source large language models, showing Elo score comparisons between open-source models like GLM-5.2 Max and DeepSeek V4 Pro and closed-source giants like GPT-5.5 and Claude Fable 5.

### Basic Information
- Original Author/Maintainer: David Linacre (GitHub: @LIN4CRE)
- Source Platform: GitHub
- Original Title: LLM-Tokyo-Benchmarks
- Original Link: https://github.com/LIN4CRE/LLM-Tokyo-Benchmarks
- Release Time: July 14, 2026
- License: MIT
- Online Demo: https://LIN4CRE.github.io/LLM-Tokyo-Benchmarks/

## Background: Rise of Open-source Models and Project Inception

In the field of large language models in 2026, the performance gap between open-source models and closed-source commercial models has narrowed significantly. Open-source architectures like GLM-5.2 Max and DeepSeek V4 Pro are competing head-on with closed-source giants like GPT-5.5 and Claude Fable 5. This fierce competition makes tracking model performance more important and difficult, so the Tokyo LLM Benchmarks project was born as a real-time interactive visualization dashboard to help understand the competition between open-source and closed-source models.

## Project Design and Overview

Tokyo LLM Benchmarks adopts the "Tokyo Night View" glassmorphism design theme, using neon-style visuals to distinguish model types: open-source models use glowing neon green, closed-source models use high-contrast neon orange, which is both aesthetically pleasing and easy for quick identification. The overall design creates an immersive Tokyo night view atmosphere, enhancing the user experience.

## Core Features and Technical Implementation

### Key Features
1. **Exquisite Dark Glass UI**: CSS4 glassmorphism effects + dynamic background light spots, clear and easy to read.
2. **Interactive Data Visualization**: Uses Chart.js to implement 24-hour rolling average Elo score tracking, showing performance change trends.
3. **Simulated Terminal Synchronization**: Simulates connecting to arena.ai and llm-stats.com data sources via the "Scrape and Sync" button; the terminal output stream adds a technical feel.
4. **Categorized Model Display**: 
   - Closed-source Giants: Claude Fable 5 (Elo1509, $10 per million tokens), GPT-5.5 Pro (Elo1506, $30 per million tokens), Gemini3.1 Pro (Elo1485, $2 per million tokens)
   - Open-source Heroes: GLM-5.2 Max (Elo1469, Free/Self-hosted), Mimo V2.5 Pro (Elo1466, Free/Self-hosted), DeepSeek V4 Pro (Elo1457, Free/Self-hosted)

## Technical Architecture

The project's tech stack is concise and efficient:
- HTML5: Semantic structure, ensuring accessibility and SEO-friendliness.
- CSS4: Custom properties, Flex/Grid layout, advanced background blur filters, keyframe animations.
- Native JavaScript (ES6+): DOM operations and terminal logic, no heavy frameworks.
- Chart.js: Responsive Canvas rendering, lightweight and powerful.
- GitHub Actions: Continuous integration/deployment; pushing to the main branch automatically deploys to GitHub Pages.
No complex build steps are needed—just open the HTML file to run, lowering the barrier to use.

## Practical Significance and Application Scenarios

1. **Model Selection Reference**: Open-source models (e.g., GLM-5.2 Max) have Elo scores close to closed-source models and zero cost, providing a decision-making basis for teams with limited budgets.
2. **Open-source Community Confidence**: Shows the possibility of open-source models catching up with closed-source ones, inspiring developers to participate in open-source model optimization.
3. **Technical Trend Insight**: 24-hour rolling data tracking helps observe short-term fluctuations and long-term trends of model performance, providing data sources for researchers.

## Limitations and Future Outlook

### Limitations
1. Data sources are based on simulation logic, not real-time API access.
2. Elo scores cannot fully represent performance in actual application scenarios.
3. Data updates depend on manual scraping by maintainers.

### Future Improvement Directions
- Integrate real-time APIs to achieve true real-time updates.
- Add evaluation dimensions such as inference speed, memory usage, and multilingual support.
- Add historical data comparison functionality.
- Support user-customized model comparisons.

## Conclusion and Community Invitation

Tokyo LLM Benchmarks combines aesthetic value and technical practicality, showing the strong performance of open-source models in 2026 and providing developers with an intuitive model comparison tool. The project is open-source (under MIT license), and community contributions are welcome. You can visit the [online demo](https://LIN4CRE.github.io/LLM-Tokyo-Benchmarks/) to experience it, or clone the repository to run it locally.