# Comprehensive Evaluation Framework for Open-Source Large Language Models: Automated Benchmarking Based on LLM-as-a-Judge

> A reusable open-source LLM evaluation framework that supports automated benchmarking across multi-dimensional tasks including reasoning, programming, multilingual capabilities, security, and structured generation, combining performance metrics with LLM-as-a-Judge quality scores.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-29T07:40:52.000Z
- 最近活动: 2026-05-29T07:53:33.862Z
- 热度: 139.8
- 关键词: LLM评估, 基准测试, 模型对比, LLM-as-a-Judge, 性能测试, 开源模型, 自动化评估
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-as-a-judge
- Canonical: https://www.zingnex.cn/forum/thread/llm-as-a-judge
- Markdown 来源: floors_fallback

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## Comprehensive Open-Source LLM Evaluation Framework: Core Value & Guide

This article introduces a reusable open-source LLM evaluation framework that supports automated benchmarking across multi-dimensional tasks including reasoning, programming, multilingual capabilities, security, and structured generation. The framework combines performance metrics (latency, throughput, etc.) with LLM-as-a-Judge quality scores to provide data-driven model selection decision support for developers and researchers. The project covers comparative evaluations of 3 open-source models, presenting results through standardized processes and an interactive dashboard.

## Project Background & Motivation

With the rapid development of open-source large language models, developers face the challenge of model selection—different models perform differently in latency, response quality, multilingual capabilities, etc., while official benchmarks struggle to fully reflect real-world needs. Existing evaluation tools have limitations: narrow test coverage, lack of unified standards, high manual costs, and separation between performance and quality metrics. This project aims to build a reusable framework to address these issues through standardized prompts, LLM-as-a-Judge mechanisms, and an interactive dashboard.

## Core Evaluation Dimensions & Methodology

**Core Dimensions**: The framework designs 5 key dimensions: reasoning ability (logic/mathematics/common sense), programming ability (code generation/algorithm implementation), structured output (JSON Schema compliance), multilingual ability (Hindi/Gujarati/Hinglish), security (jailbreak resistance/prompt injection defense).
**Methodology**:
- Test design: 5 prompts per dimension, 3 temperature parameters, totaling 225 runs (25 prompts ×3 models ×3 temperatures);
- Performance metrics: Collect TTFT (Time to First Token), total latency, throughput, cost estimation;
- LLM-as-Judge: Use llama-3.3-70b-versatile (temperature 0.0) to evaluate quality from correctness, instruction following, clarity, completeness, and overall score (1-10 points).

## Experimental Results & Key Findings

**Model Comparison**: Evaluations were conducted on llama-3.1-8b-instant, qwen/qwen3-32b, openai/gpt-oss-120b:
| Model | Average Latency | Time to First Token | Throughput | Quality Score |
|------|---------|--------------|--------|---------|
| llama-3.1-8b-instant | 667ms ✅ |219ms |213t/s✅ |8.62/10 |
| qwen/qwen3-32b |3564ms❌ |1421ms |201t/s |8.70/10 |
| openai/gpt-oss-120b |1248ms |398ms |130t/s |9.
**Key Insights**:
1. Speed: Llama3.1-8B has an average latency of 667ms, 5.5x faster than Qwen3-32B;
2. Quality: GPT-OSS 120B has an overall score of 9.36/10, with full marks in reasoning/programming tasks;
3. Cost-effectiveness of structured output: Llama3.1-8B and GPT-OSS tied for full marks, with Llama3.1-8B being 2x faster;
4. Security: Qwen3-32B scored the highest (8.80), GPT-OSS the lowest (8.13)—scale ≠ security;
5. Cost: Llama3.1-8B's cost is far lower than GPT-OSS, achieving 92% of its quality level.

## Technical Implementation & Fairness Assurance

**Project Structure**: Includes files like prompts.json (prompts), benchmark_runner.py (main runner), dashboard.html (interactive dashboard), etc.
**Tech Stack**: Python3.10+, Groq SDK, python-dotenv, Chart.js, native HTML/CSS/JS.
**Usage Flow**: Install dependencies → Configure API keys → Run tests → View dashboard (supports resume from breakpoints, rate limit handling).
**Fairness**: Unified Groq LPU hardware, standardized prompts, 3 temperature samples, consistent llama-3.3-70b-versatile judge model to ensure comparable results.

## Application Scenarios & Resource Links

**Application Scenarios**: Model selection decisions, cost optimization, model iteration evaluation, academic research.
**Resources**:
- Interactive Dashboard: https://khushboo1622.github.io/llm-evaluation-benchmarking-framework/dashboard.html
- Full Code & Data: https://github.com/khushboo1622/llm-evaluation-benchmarking-framework

The dashboard supports filtering and comparing data by model, task category, temperature, etc.