Despite the buzz surrounding artificial intelligence, even the most advanced vision-language models—GPT-4o, Claude Sonnet 3.7, and Gemini 2.5 Pro—struggle with a decades-old challenge: playing the classic first-person shooter Doom.

On Thursday, a new research project introduced VideoGameBench, an AI benchmark designed to test whether state-of-the-art vision-language models can play—and beat—a suite of 20 popular video games, using only what they see on the screen.

“In our experience, current state-of-the-art VLMs substantially struggle to play video games because of high inference latency,” the researchers said. “When an agent takes a screenshot and queries the VLM about what action to take, by the time the response comes back, the game state has changed significantly and the action is no longer relevant.”

The researchers stated that they used classic Game Boy and MS-DOS games due to their simpler visuals and diverse input styles, like a mouse and keyboard or game controller, which better test a vision-language model’s spatial reasoning capabilities than text-based games.

VideoGameBench was developed by computer scientist and AI researcher Alex Zhang. The suite of games includes classics like Warcraft II, Age of Empires, and Prince of Persia.

According to the researchers, delayed responses are most problematic in first-person shooters like Doom. In these fast-paced environments, an enemy visible in a screenshot may already have moved—or even reached the player—by the time the model acts.

For software developers, Doom has long served as a litmus test for technological capability in gaming environments. Lawnmowers, Bitcoin, and even human gut bacteria have faced down the demons from hell with varying levels of success. Now it’s AI’s turn.

“What has brought Doom out of the shadows of the 90s and into the modern light is not its riveting gameplay, but rather its appealing computational design,” MIT biotech researcher Lauren Ramlan previously told Decrypt. “Built on the id Tech 1 engine, the game was designed to require only the most modest of setups to be played.”

In addition to struggling with understanding game environments, the models often failed to perform basic in-game actions.

“We observed frequent instances where the agent had trouble understanding how its actions—such as moving right—would translate on screen,” the researchers said. “The most consistent failure across all frontier models we tested was an inability to reliably control the mouse in games like Civilization and Warcraft II, where precise and frequent mouse movements are essential.”

To better understand the limitations of current AI systems, VideoGameBench emphasized the importance of evaluating their reasoning abilities in environments that are both dynamic and complex.

“Unlike extremely complicated domains like unsolved math proofs and olympiad-level math problems, playing video games is not a superhuman reasoning task, yet models still struggle to solve them,” they said.

Edited by Andrew Hayward