GardenBench: A Lightweight, Daily Evaluation Framework for LLM Capabilities
arXiv:2610.23866 (cs.AI) | Submitted 7 September 2026 | Also classified under cs.CL (Computation and Language)
Authors: Benedict Cummins-Mburu, Mariana Garcia Mejia, Oscar Heath, Sabrina Kreyzerman, Ellie Murray, Arusan Surendiran, Rohan Alexander
DOI: 10.48550/arXiv.2610.23866
Overview
GardenBench is a benchmark comprising 11 test suites designed to evaluate the capabilities of Large Language Models (LLMs) across a range of tasks. By systematically prompting LLMs with various lightweight queries, the framework documents day-to-day changes in model performance—including run costs and times—that other benchmarks often miss.
Key Contributions
- Cross-provider comparison: Enables comparison of response accuracy and features across model providers and versions.
- Daily variation tracking: Accounts for day-to-day fluctuations in model accuracy, capturing both model progress and degradation—for instance, when a model is changed post-deployment or when a new model is released.
- Consumer-facing focus: Grounds evaluation in general-use, everyday tasks rather than narrow or highly specialized benchmarks.
- Public transparency: Results are made publicly accessible via a live dashboard, diversifying existing approaches to LLM testing.
The main contribution is an innovative, lightweight, and inexpensive benchmark designed to be consumer-facing and applicable to everyday tasks.
Significance in the 2026 LLM Landscape
As of 2026, the rapid pace of model releases and silent post-deployment updates—sometimes called "model drift" or "undocumented changes"—has made continuous, low-cost monitoring increasingly important. Traditional static benchmarks (e.g., MMLU-style academic suites) typically evaluate models at a single point in time and at considerable expense. GardenBench addresses this gap by offering a daily, lightweight alternative that captures real-world performance variability across providers. This approach complements established model-monitoring practices and aligns with growing industry demand for transparent, ongoing evaluation of deployed LLMs.
Metadata
- Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
- Cite as: arXiv:2610.23866 [cs.AI]
- Version: arXiv:2610.23866v1 [cs.AI]
- Submission history: From Rohan Alexander
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