LLM API Test: Open Source Benchmark Tool for GPT-4, Gemini & More

LLM API Test is an MIT-licensed open-source web tool designed to benchmark and compare the performance of large language model APIs such as GPT-4 and Gemini. It provides real-time metrics like first-token latency, tokens per second (TPS), success rates, and output quality, helping developers and researchers evaluate LLMs for production use, optimization, or academic research.

文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html

• Protocol Support: Built-in support for OpenAI (GPT-3.5, GPT-4, Turbo) and Google Gemini (Pro, Pro Vision); extendable to any OpenAI-compatible custom endpoint.
• Key Metrics:
  • First-token latency: How fast the first token is returned.
  • Output speed: Measured in tokens per second (Token/s).
  • Success rate: Percentage of successful API calls.
  • Output quality: Optional side-by-side comparison of model responses.
• User Experience: Responsive interface compatible with desktop and mobile. Real-time visual charts update as tests run. Historical logs are stored for trend tracking.

文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html

As ultra-low-latency models like GPT-4.1 nano emerge, performance becomes a competitive advantage. Projects like lmspeed.net reveal global API response time differences, encouraging developers to evaluate:文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html

• Vendor infrastructure performance
• Latency across regions
• Trade-offs between quality and cost

Leading organizations such as NVIDIA and MLCommons have launched initiatives like GenAI-Perf and MLPerf Client 1.0 to standardize evaluation tools. LLM API Test complements these efforts with a practical, easy-to-use frontend.文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html

• Vendor Selection: Compare models like GPT-4 Turbo vs Gemini Pro in terms of speed, quality, and pricing to guide procurement.
• Cost-Performance Optimization: Follow guides like TechRadar’s 2025 GenAI report to balance speed, quality, and API cost with real data.
• Academic Research: Validate performance claims made in LLM studies, benchmark tool use (LangChain, Toolformer, etc.).

• Local Setup:

  git clone https://github.com/qjr87/llm-api-test
  cd llm-api-test
  npm install
  npm start

  Visit http://localhost:8000 in your browser.

• Static Hosting: Deploy on Vercel, Netlify, or GitHub Pages. A simple Dockerfile is included for containerized deployment.

1. Go to the Configuration panel.
2. Select the LLM protocol (OpenAI or Gemini) and paste your API URL & key.
3. Specify models to test (e.g., gpt-4, gemini-pro).
4. Set test rounds, concurrency, and prompt content.
5. Click Start Test and watch real-time metrics unfold!

• Live Demo: https://llmapitest.com
• GitHub Repository: https://github.com/qjr87/llm-api-test

LLM API Test is the go-to tool for developers, researchers, and businesses needing insight into large language model API performance. Whether you're fine-tuning your app’s latency or preparing a paper on AI inference efficiency, this open-source project gives you the tools you need—visually, reliably, and transparently.文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html 文章源自resohive.com-https://resohive.com/llm-api-test-open-source-benchmark-tool-for-gpt-4-gemini-more.html