AI vs Pokémon: How Chatbots Are Benchmarked on a Classic Game

Pokémon is Now a Benchmark for AI Chatbots

The complex world of Pokémon, once a beloved pastime for gamers, has unexpectedly become a proving ground for artificial intelligence. What began as a curious experiment at Anthropic has evolved into a standard benchmark for evaluating the capabilities of leading AI models, including those from OpenAI and Google. The challenge isn’t about mastering reflexes, but about demonstrating planning, memory, and adaptability – skills crucial for the next generation of AI agents.

In 2024, researchers at Anthropic initiated an internal project to teach Claude to play Pokémon Red using a text-based interface connected to a game emulator. This wasn’t about specialized training or reinforcement learning; the AI was simply given access to a knowledge base containing information about Pokémon, trainers, and locations, a vision module to interpret the game screen, and a function to simulate button presses. Despite these tools, initial progress was slow. By June 2024, Claude 3.5 Sonnet repeatedly found itself stuck in early-game loops, attempting to flee mandatory battles.

The choice of Pokémon as a testing ground is surprisingly logical. Unlike games requiring rapid reaction times, like Mario, Pokémon is turn-based, removing the pressure of real-time performance. This allows researchers to focus on the AI’s ability to make logical decisions and plan strategically. Pokémon offers a more open-ended environment than classic games like chess or Pong, demanding creativity and flexibility rather than simply recalling pre-defined patterns. A Pokémon playthrough also presents a significant test of long-term memory, requiring the AI to retain information over hundreds of hours of gameplay.

However, the biggest challenge Pokémon presents lies in spatial reasoning. The game’s 2D pixel graphics require the AI to translate visual information into a mental map – a task that proves surprisingly difficult. As David Hershey, an Anthropic employee, explained in 2025, “Claude is still not particularly good at understanding what is even on the screen. It is one of those funny things about humans that we look at these 8×8 pixel heaps of people and can say, ‘That is a girl with blue hair.’” The infamous “Mondberg” – a notoriously confusing area in Pokémon Red – became a symbol of this spatial reasoning problem, with early AI iterations getting hopelessly lost within its confines.

Anthropic, OpenAI, and Google Track AI Progress Through Pokémon

The experiment quickly gained traction beyond Anthropic. OpenAI and Google also began using Pokémon as a benchmark, with developers closely monitoring their models’ progress. Hershey now streams Claude’s Pokémon journey on Twitch under the handle ClaudePlaysPokemon. According to the Wall Street Journal, OpenAI even displayed a live stream of its AI playing Pokémon on a large screen in its headquarters, fostering discussion among developers about tactical decisions.

Google’s Gemini model also benefited from a similar initiative, initially driven by an independent developer. CEO Sundar Pichai showcased Gemini’s success in completing the game during Google I/O, and the progress was even incorporated into official company reports. This demonstrates that Pokémon is no longer considered a mere novelty but a tangible measure of AI advancement.

The models have improved dramatically since 2025. While earlier versions struggled for days within the Mondberg, Gemini 3 Pro and GPT-5.2 have reportedly completed the game and moved on to sequels, according to the Wall Street Journal. A recent demonstration highlighted the strategic capabilities of these models, with one AI defeating another in Pokémon Stadium with a decisive 6-0 victory.

The Path to AI Agents

The growing interest in using Pokémon as a benchmark underscores a crucial point: theoretical knowledge is insufficient. The true test of an AI lies in its ability to apply that knowledge in unpredictable real-world scenarios. Pokémon, with its complex rules and open-ended gameplay, serves as a valuable stepping stone towards developing true AI agents.

The skills honed through mastering Pokémon – managing resources, planning long-term strategies, and adapting to unforeseen circumstances – are directly transferable to more practical applications. The ultimate goal is to create AI capable of automating complex tasks in office environments and beyond. The “final boss” of Pokémon, represents a significant milestone on the path to building more versatile and intelligent AI systems.