VideoGameBench: Can Vision-Language Models complete popular video games?

TL;DR

VideoGameBench evaluates vision-language models' ability to play classic video games using only visual inputs and high-level objectives, revealing current models' limited capabilities in real-time gameplay.

Contribution

Introduces VideoGameBench, a novel benchmark for assessing vision-language models on real-time video game tasks with minimal auxiliary information.

Findings

Models complete less than 1% of the games in the benchmark.

Inference latency significantly hampers model performance in real-time settings.

VideoGameBench Lite reduces latency by pausing the game during model inference.

Abstract

Vision-language models (VLMs) have achieved strong results on coding and math benchmarks that are challenging for humans, yet their ability to perform tasks that come naturally to humans--such as perception, spatial navigation, and memory management--remains understudied. Real video games are crafted to be intuitive for humans to learn and master by leveraging innate inductive biases, making them an ideal testbed for evaluating such capabilities in VLMs. To this end, we introduce VideoGameBench, a benchmark consisting of 10 popular video games from the 1990s that VLMs directly interact with in real-time. VideoGameBench challenges models to complete entire games with access to only raw visual inputs and a high-level description of objectives and controls, a significant departure from existing setups that rely on game-specific scaffolding and auxiliary information. We keep three of the games secret to encourage solutions that generalize to unseen environments. Our experiments show that frontier vision-language models struggle to progress beyond the beginning of each game. We find inference latency to be a major limitation of frontier models in the real-time setting; therefore, we introduce VideoGameBench Lite, a setting where the game pauses while waiting for the LM's next action. The best performing models, Gemini 2.5 Pro and Claude 3.7 Sonnet, complete only 0.48% of VideoGameBench and 1.6% of VideoGameBench Lite. We hope that the formalization of the human skills mentioned above into this benchmark motivates progress in these research directions.