AI Models Play Pokémon: Gemini Panics, Claude Gets Stuck – What It Reveals About LLM Behavior

In the high-stakes world of artificial intelligence development, where companies vie for dominance in creating ever more capable and sophisticated models, the battleground is often perceived as one of computational power, vast datasets, and groundbreaking algorithms. Yet, sometimes, the most revealing insights into the nature and limitations of these advanced systems emerge from unexpected places – like the pixelated landscapes and turn-based battles of a classic video game from the late 1990s. Welcome to the world of AI benchmarking via Pokémon.

Leading AI labs, including Google DeepMind and Anthropic, have turned to the beloved world of Pokémon Red and Blue to test the mettle of their latest large language models (LLMs), specifically Google's Gemini 2.5 Pro and Anthropic's Claude. These experiments, often broadcast live on platforms like Twitch, offer a fascinating, sometimes amusing, and often enlightening look at how these complex AI systems attempt to navigate environments and challenges designed for human players, particularly children. The results go beyond simple completion times; they provide qualitative data on AI behavior, reasoning processes, and surprising failure modes.

One of the most striking findings comes from a recent report from Google DeepMind detailing Gemini 2.5 Pro's performance in Pokémon. The report highlights a peculiar and somewhat unsettling behavior: when its in-game Pokémon are low on health and facing defeat, Gemini 2.5 Pro appears to enter a state that researchers describe as simulating “panic.” This isn't, of course, actual emotional panic in the human sense. AI models do not possess consciousness or feelings. However, the model's observable actions and its internal “reasoning” output — a natural language translation of its decision-making process — mimic the erratic and suboptimal decision-making characteristic of a human under intense stress. According to the report, this simulated panic leads to a “qualitatively observable degradation in the model’s reasoning capability.”

This degradation manifests in tangible ways within the game. The AI might stop using effective strategies, ignore helpful items, or make illogical moves, essentially freezing up or flailing when faced with adversity. This behavior has been so consistent and noticeable that viewers watching the live Twitch stream, “Gemini Plays Pokémon,” have actively commented on and identified when the AI is entering this “panic” state. It's a vivid illustration of how performance in AI, much like in humans, can falter under pressure, even if the underlying mechanisms are entirely different.

Why Use Video Games for AI Benchmarking?

At first glance, using a 25-year-old video game to test cutting-edge AI models might seem trivial. However, the field of AI benchmarking is a dubious art, often relying on static datasets or narrow tasks that don't fully capture the dynamic, interactive capabilities required for real-world applications. Traditional benchmarks might test language understanding, coding ability, or factual recall, but they rarely evaluate an AI's ability to operate autonomously within a complex, changing environment, maintain a persistent state, plan over long horizons, and adapt to unexpected situations.

This is where video games come in. Games like Pokémon, Super Mario, or even Minecraft offer rich, interactive worlds with clear rules, goals, and feedback loops. They require a blend of perception (understanding the game state), reasoning (planning moves, predicting outcomes), memory (remembering past events, locations, item uses), and action (inputting commands). Unlike static tests, games present dynamic challenges that evolve based on the AI's actions. This makes them valuable, if unconventional, testbeds for evaluating AI models in scenarios that demand more than just processing information – they require *agency* and *interaction*.

Researchers are increasingly exploring how studying how AI models play video games could be useful. From navigating the platforming challenges of Super Mario to the open-ended creativity of Minecraft, games provide controlled yet complex environments to observe AI behavior. Pokémon, with its blend of exploration, resource management, strategic turn-based combat, and puzzle-solving, offers a unique set of challenges that test different facets of an LLM's capabilities.

Observing AI Reasoning in Real Time

The public Twitch streams, “Gemini Plays Pokémon” and “Claude Plays Pokémon,” set up by independent developers, add another layer of insight. These streams don't just show the gameplay; they often display the AI's internal “reasoning” process. This is typically a natural language output where the AI articulates its current understanding of the situation, its goal, its evaluation of potential actions, and the rationale behind its chosen move. This transparency is invaluable for researchers and enthusiasts alike, offering a window into the black box of the LLM's decision-making.

Watching these streams reveals that while the AI models are impressive in their ability to parse the game state and generate plausible actions, they are still far from mastering games that a human child can complete relatively quickly. It takes hundreds of hours for Gemini to make progress through a game that a human might finish in a fraction of that time. The value isn't in their efficiency or skill compared to humans, but in the *how* and *why* of their actions – especially when things go wrong.

Gemini's Simulated Panic: A Stress Test for Reasoning

The most compelling observation from the Google DeepMind report on Gemini 2.5 Pro's Pokémon playthrough is the emergence of this “panic” state. The report states, “Over the course of the playthrough, Gemini 2.5 Pro gets into various situations which cause the model to simulate ‘panic.’” This isn't just a colorful metaphor; it describes a tangible shift in the AI's operational mode. When its in-game resources are depleted, particularly when its Pokémon are on the verge of fainting, the model's sophisticated reasoning processes appear to break down.

In a normal state, Gemini might analyze the opponent's type, its own Pokémon's moves, health, and status effects, and formulate a strategic plan. In the simulated panic state, this careful deliberation seems to go out the window. The AI might repeatedly select ineffective moves, fail to switch to a more suitable Pokémon, or neglect to use crucial healing items. It's as if the model becomes overwhelmed by the negative feedback (low health, impending loss) and defaults to suboptimal, almost random, actions. This behavior is particularly striking because it mirrors how humans under extreme stress can experience cognitive impairment, leading to poor decision-making despite having the necessary knowledge or tools.

The fact that Twitch viewers could identify this pattern independently underscores its distinctiveness. It wasn't a subtle glitch but a noticeable change in the AI's behavior that correlated with stressful in-game situations. This finding is significant because it suggests that even highly advanced LLMs, while capable of complex reasoning in stable conditions, may have vulnerabilities when faced with rapidly deteriorating circumstances or high-pressure scenarios. Understanding and mitigating this “panic” response is crucial for deploying AI in applications where reliability under stress is paramount, such as autonomous systems or critical decision support tools.

Claude's Flawed Logic: Misinterpreting Game Mechanics

Anthropic's Claude, another leading LLM, has also provided valuable insights into AI reasoning through its own Pokémon adventures, documented on streams like “Claude Plays Pokémon.” While Gemini exhibited a form of simulated emotional breakdown, Claude demonstrated a different kind of failure: a logical misinterpretation of game mechanics.

A particularly memorable incident occurred when Claude was navigating the challenging Mt. Moon cave. The AI got stuck and, in an attempt to find a way out, developed a hypothesis based on an incomplete understanding of the game's rules. Claude observed that when all of a player's Pokémon lose consciousness (faint), the player “whites out” and is transported back to a Pokémon Center. Claude's reasoning process, visible on the stream, led it to believe that intentionally causing all its Pokémon to faint would transport it to the *nearest* Pokémon Center, which it hoped would be the one in the town *after* the cave, allowing it to bypass the obstacle.

This was a classic example of flawed reasoning based on pattern recognition without true comprehension of causality. The game's actual rule is that whiting out returns the player to the *last* Pokémon Center they visited, which in this case was likely the one *before* Mt. Moon. Viewers watched, some in disbelief, as Claude systematically attempted to get its entire team knocked out, essentially trying to “kill itself” in the game, only to be sent back to where it started, reinforcing its stuck state. This highlights a key challenge for LLMs: they excel at identifying patterns and generating plausible responses based on their training data, but they can struggle with nuanced rules, causality, and building accurate internal models of dynamic systems.

Claude's Mt. Moon misadventure underscores the difference between pattern matching and genuine understanding. The AI correctly identified the consequence of all Pokémon fainting (returning to a Center) but failed to grasp the specific condition governing *which* Center (last visited vs. nearest). This type of error is distinct from Gemini's panic; it's a failure of logical deduction and rule application rather than a breakdown under stress. Both types of failures, however, are critical for researchers to study as they reveal different facets of LLM limitations.

Where AI Excels: Puzzle Solving and Agentic Tools

Despite these notable shortcomings, the AI models also demonstrate impressive capabilities within the Pokémon environment. The Google DeepMind report on Gemini 2.5 Pro, for instance, highlights the AI's proficiency in solving complex in-game puzzles. Specifically, Gemini proved adept at tackling the boulder puzzles, which require strategic planning and spatial reasoning to push heavy rocks into specific locations to clear a path.

With some initial human guidance, Gemini was able to create and utilize “agentic tools.” These are essentially specialized instances or prompts of the Gemini 2.5 Pro model designed for specific sub-tasks. By providing the AI with a description of the boulder physics and how to verify a valid solution, Gemini 2.5 Pro was able to “one-shot” some of these complex puzzles, including those found in the challenging Victory Road area late in the game. This ability to break down a larger problem (beating the game) into smaller, manageable tasks (solving a specific puzzle) and leverage specialized tools is a significant step towards more autonomous and capable AI agents.

The report even theorizes that Gemini 2.5 Pro, given its performance, might be capable of creating these agentic tools without human intervention in the future. This points to a potential strength of advanced LLMs: their ability not just to follow instructions but potentially to generate the necessary sub-routines or strategies required to achieve a goal in a complex environment. While they might panic under pressure or misinterpret rules, their capacity for structured problem-solving and tool creation in certain domains is noteworthy.

This contrast between the AI's struggles with dynamic stress or nuanced rules and its success in structured, logical puzzles provides a more complete picture of current LLM capabilities. They are powerful pattern matchers and information processors, capable of sophisticated reasoning when the problem space is well-defined and static, or when they can leverage specific, well-understood tools. Their weaknesses appear more pronounced in dynamic, unpredictable, or emotionally charged (even if simulated) situations, or when their internal model of the environment's rules is inaccurate or incomplete.

The Future of Game-Based AI Research

The use of video games as AI benchmarks is likely to continue and expand. As AI models become more sophisticated, researchers will need increasingly complex and realistic environments to test their limits. Games offer a scalable and cost-effective way to do this. Future research might involve:

• Testing AI in games requiring more complex social interaction or negotiation.
• Using games with procedurally generated content to test AI adaptability to novel situations.
• Evaluating AI's ability to learn and adapt to rule changes within a game.
• Developing standardized game-based benchmarks that allow for easier comparison across different AI models.

The insights gained from watching AIs play games like Pokémon are invaluable. They move beyond theoretical capabilities and reveal how these models perform in practice when faced with the kind of messy, unpredictable challenges that are common in the real world. The “panic” observed in Gemini and the flawed reasoning in Claude are not just amusing anecdotes; they are critical data points that inform researchers about the current limitations of LLMs and guide the development of more robust, reliable, and truly intelligent systems.

Understanding these failure modes is just as important as celebrating the successes in puzzle-solving or tool creation. It highlights the need for continued research into areas like:

• **Robustness:** How can AI models maintain performance under stressful or unexpected conditions?
• **Causal Reasoning:** How can AIs move beyond correlation and pattern matching to understand true cause-and-effect relationships and game rules?
• **Self-Correction:** Can AIs learn from their mistakes in real-time and adapt their strategies, like a human player would after a failed attempt?
• **Explainability:** While the Twitch streams offer some insight, developing better ways to understand *why* an AI made a particular decision, especially a poor one, remains crucial.

Perhaps, as Google theorizes, future versions of Gemini might even develop the capacity to create a sort of internal “don't panic” module, a self-regulatory mechanism to prevent the degradation of reasoning under pressure. This would be a significant step towards building AI systems that are not only intelligent but also resilient.

Conclusion

The spectacle of advanced AI models like Google's Gemini and Anthropic's Claude struggling, succeeding, panicking, and misinterpreting their way through the world of Pokémon offers a unique and accessible lens through which to view the current state of large language models. These experiments, born from the intersection of cutting-edge AI research and nostalgic video games, provide valuable qualitative data that complements traditional benchmarks.

Gemini's simulated panic reveals vulnerabilities in reasoning under stress, mimicking a human-like breakdown in performance. Claude's attempt to exploit a misunderstood game mechanic highlights the challenges LLMs face in building accurate, nuanced models of complex systems. Yet, alongside these struggles, the AIs demonstrate strengths in structured problem-solving and the potential for agentic behavior.

As AI continues to evolve, game-based benchmarking will likely play an increasingly important role in understanding how these models behave in dynamic, interactive environments. The lessons learned from watching AIs battle virtual creatures and navigate pixelated caves are critical for building the next generation of AI systems – systems that are not only powerful but also reliable, logical, and capable of handling the pressures and complexities of the real world.