BlindBench: An LLM Reasoning Error Diagnosis System Under a Blind Testing Framework

BlindBench is a tool for comparing the performance of large language models (LLMs) through blind testing. Its core lies in hiding model identities to avoid brand bias, focusing on the objective evaluation of answer authenticity (Truth Score) and reasoning logic integrity (Reasoning Failure Check). It supports parallel testing of over 100 mainstream AI models, providing brand-free performance references for academia, enterprises, and ordinary users.

With the rapid development of LLMs, traditional benchmark tests can hardly avoid the influence of brand effects and marketing rhetoric, leading to insufficiently objective evaluations. BlindBench proposes an innovative solution: hiding model identities through a blind testing mechanism to let evaluations return to content quality itself, solving this industry pain point.

BlindBench is provided as a Windows 10/11 desktop application with a simple interface that requires no programming background. The testing process includes: 1. Select models to be tested (the system automatically hides identity metadata); 2. Run blind tests to collect outputs; 3. Score answer authenticity and check reasoning logic; 4. Summarize results into a leaderboard. In addition, it does not collect personal information by default, and users can anonymously share test results to promote community collaboration.

The blind testing methodology of BlindBench demonstrates value in multiple scenarios:

• Researchers: Eliminate brand interference and obtain objective research conclusions;
• Enterprise users: Avoid marketing misdirection during technology selection and make decisions based on real data;
• Ordinary users: Intuitively reference model capabilities through the leaderboard and choose suitable tools.

BlindBench represents an evaluation concept that returns to the essence. Against the backdrop of rapid iteration of LLM capabilities and fierce market competition, it provides a valuable reference framework for objectively evaluating model performance, helping various users gain true and reliable insights into model capabilities.

Currently, BlindBench has Windows platform limitations, and users need to keep their systems updated to ensure compatibility. In the future, it can expand cross-platform support, add evaluation dimensions such as response speed and resource consumption, introduce a fine-grained error classification system, and connect to academic benchmark datasets to enhance the comparability and authority of evaluations.