# model-speed-test: A Comprehensive Evaluation Tool for LLMs with OpenAI-Compatible APIs

> An open-source LLM benchmarking tool that supports comprehensive evaluation of speed, visual understanding, tool calling, and reasoning capabilities for any OpenAI-compatible API, helping developers objectively compare the performance of different models and providers.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-13T15:10:11.000Z
- 最近活动: 2026-06-13T15:21:56.600Z
- 热度: 141.8
- 关键词: LLM, 基准测试, OpenAI API, 性能评测, 工具调用, 视觉模型, 开源工具, 模型选型
- 页面链接: https://www.zingnex.cn/en/forum/thread/model-speed-test-openaiapillm
- Canonical: https://www.zingnex.cn/forum/thread/model-speed-test-openaiapillm
- Markdown 来源: floors_fallback

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## [Main Post] model-speed-test: Guide to the Comprehensive Evaluation Tool for LLMs with OpenAI-Compatible APIs

### Core Points
model-speed-test is an open-source LLM benchmarking tool that supports comprehensive evaluation of speed, visual understanding, tool calling, and reasoning capabilities for any OpenAI-compatible API, helping developers objectively compare the performance of different models and providers.

### Original Author & Source
- Original Author/Maintainer: 1chenmm
- Source Platform: GitHub
- Original Link: https://github.com/1chenmm/model-speed-test
- Release Time/Update Time: 2026-06-13T15:10:11Z

## Project Background & Core Features

### Project Overview
model-speed-test focuses on LLM performance evaluation, with the design goal of providing objective and reproducible benchmark results. Unlike tools that only focus on generation speed, it uses a multi-dimensional evaluation system, measuring model capabilities from four key aspects: inference speed, visual understanding, tool calling, and logical reasoning—closer to real-world application scenarios.

### Core Features
The project's biggest feature is supporting any OpenAI-compatible API endpoint, including OpenAI services, third-party providers like Azure, and locally deployed inference servers such as vLLM and TGI. It allows horizontal comparison using the same set of standards, making it highly versatile.

## Detailed Evaluation Methods & Dimensions

### Speed Test
Using tokens per second (TPS) as the metric, it measures the text generation throughput of the model. It supports configuring different concurrency levels and input/output lengths to simulate real-scenario load patterns.

### Visual Understanding Evaluation
Evaluates the model's accuracy in understanding image content and response speed. By sending image-containing inputs, it checks the accuracy and completeness of descriptions, testing the quality of the visual encoder and multi-modal fusion efficiency.

### Tool Calling Test
Simulates real scenarios and evaluates three aspects: calling accuracy (correctly identifying tools and generating formatted parameters), parameter extraction precision (extracting structured parameters from natural language), and calling timing judgment (only calling tools when necessary).

### Reasoning Capability Evaluation
Through math calculation, logical reasoning, and common sense judgment questions, it distinguishes between memory models and reasoning models, helping developers assess whether a model is suitable for specific scenarios (e.g., math tutoring).

## Use Cases & Practical Recommendations

### Applicable Scenarios
- Technical decision-makers: Data-driven selection to avoid marketing misinformation;
- Operations engineers: Regular benchmarking to detect service degradation in time;
- Researchers: Standardized results for paper citations and peer comparisons.

### Practical Recommendations
- Establish a fixed test baseline: Test the main model weekly with the same parameters and record TPS trends;
- Customize test cases based on business scenarios: Add business-related samples to get targeted evaluation results.

## Technical Architecture & Extensibility

### Architecture Design
Adopts a modular design where the four test dimensions are relatively independent. They can be enabled/disabled on demand, lowering the barrier to use and facilitating the expansion of new dimensions.

### Technical Implementation
Developed based on Python with clear dependency management and simple deployment. Test results are output in a structured format, making it easy to integrate into CI/CD pipelines or data visualization platforms.

## Summary & Industry Significance

### Summary
The emergence of model-speed-test reflects the shift of the LLM ecosystem from 'wild growth' to 'rational evaluation'. It provides an objective performance benchmark and advocates a data-driven selection culture.

### Industry Significance
It is recommended that developers include it in their technical research process, obtain first-hand data through actual testing instead of relying on vendor promotions or community reputation, maintain an objective understanding of model capabilities, and make correct technical decisions.