arXiv:2511.05722v3 Announce Type: replace-cross Abstract: Large language models (LLMs) such as GPT-5 and Gemini 3 have pushed the frontier of automated reasoning and code generation. Yet current benchmarks emphasize accuracy and output quality, neglecting a critical dimension: efficiency of token usage. The token efficiency is highly variable in practical. Models solving the same problem with similar accuracy can exhibit up to a \textbf{5.0$\times$} difference in token length, leading to massive gap of model reasoning ability. Such variance exposes significant redundancy, highlighting the crit
The rapid advancement and deployment of large language models have brought their practical application and associated costs into sharper focus, necessitating new evaluation metrics.
Evaluating LLMs purely on accuracy overlooks a critical economic dimension, token efficiency, which directly impacts operational costs and scalability for businesses and researchers.
The introduction of benchmarks like OckBench shifts the focus from mere output quality to the operational efficiency of LLMs, potentially altering model development priorities and procurement decisions.
- · LLM developers focused on efficiency
- · Businesses deploying LLMs at scale
- · AI research in token optimization
- · LLMs with high token inefficiency
- · Cloud providers charging per token
Developers will prioritize token efficiency alongside accuracy, leading to more cost-effective LLMs.
Reduced operational costs for AI applications will accelerate their adoption across various industries.
Increased competition among LLM providers based on price-performance, making AI more accessible and ubiquitous.
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Read at arXiv cs.AI