Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression

Recent years have witnessed the rapid advancements of large language models (LLMs) and their expanding applications, leading to soaring demands for computational resources. The widespread adoption of test-time scaling further intensifies the tension between model capability and resource consumption, highlighting the importance of inference efficiency. However, a unified metric that accurately reflects an LLM's efficiency across diverse model sizes and architectures remains absent. Motivated by the correlation between compression and intelligence, we introduce information capacity, a measure of model efficiency based on text compression performance relative to computational complexity. A distinctive feature of information capacity is its incorporation of tokenizer efficiency, which affects inference costs but is often neglected in LLM evaluations. We assess the information capacity of 52 open-source models and observe a consistent information capacity among different-sized models within a series. Experiments on 5 heterogeneous datasets reveal strong linguistic bias in mainstream LLMs. Three major factors of information capacity include tokenizer efficiency, pretraining data, and the mixture-of-experts architecture. Empirical results verify the accuracy of performance prediction across model sizes based on information capacity and show the correlation between information capacity and benchmark scores.