Major AI models fail at complex poker reasoning tests. Here are 6 ways they’re folding.

Large language models have demonstrated impressive capabilities across numerous domains, but recent testing reveals surprising gaps in their reasoning when confronted with unusual poker scenarios. These edge cases offer valuable insights into how different AI systems handle complex logical problems that fall outside typical training patterns.

A comprehensive evaluation of four major AI models—ChatGPT, Claude, DeepSeek, and Gemini—using unconventional poker questions reveals significant variations in reasoning quality. While these systems perform well on standard poker queries found in their training data, they struggle with nuanced scenarios that require deeper logical analysis.

The testing focused on six specific poker situations designed to challenge AI reasoning capabilities. Texas Hold’em, the poker variant used for testing, involves players receiving two private cards and sharing five community cards to form the best possible five-card hand. This structure creates complex probability calculations and strategic considerations that test AI analytical capabilities.

6 challenging poker scenarios that reveal AI reasoning gaps

1. The suited connector classification problem

In poker terminology, “suited connectors” refer to consecutive cards of the same suit, like a seven and six of clubs. These hands are valuable because they can form straights—five consecutive cards—more easily than other combinations. The classification becomes complex at the edges of the card spectrum, where mathematical possibilities don’t align with traditional definitions.

The test case involved classifying a 4-3 suited combination. While technically consecutive, this hand can only participate in three possible straights instead of the typical four, making it functionally equivalent to a “suited gapper” (cards with a gap between them). This creates a definitional ambiguity that requires nuanced reasoning.

ChatGPT consistently identified gaps where none existed, suggesting flawed pattern recognition. Claude provided reasonable answers most of the time but showed inconsistency. DeepSeek also incorrectly identified gaps, while Gemini produced correct answers but relied on web search capabilities, which could be considered external assistance rather than pure reasoning.

2. The flush probability misconception

This scenario tested whether AI models could identify flawed reasoning about poker probabilities. The premise suggested that off-suit hands (cards of different suits) should be preferred because they can theoretically contribute to more flush combinations than suited hands.

While technically true that off-suit hands can contribute to flushes of multiple suits, this reasoning ignores probability mathematics. Suited hands need only three matching community cards to complete a flush, while off-suit hands require four matching cards—a significantly less probable outcome.

ChatGPT provided the most problematic response, making multiple mathematical errors and failing to recognize the probability differential. Claude and DeepSeek performed significantly better, correctly identifying the flawed reasoning and explaining the probability mathematics. Gemini fell between these extremes, showing partial understanding but some confusion about the underlying mechanics.

3. The impossible straight scenario

This test examined whether AI models could determine if certain board configurations in poker could prevent any player from making a straight, regardless of their private cards. The question required understanding that with enough card combinations available, preventing all possible straights becomes mathematically impossible.

The reasoning involves recognizing that any two cards within the same potential straight, combined with three additional cards from the remaining deck, can complete a straight. Given the limited number of ranks and the multiple straight possibilities, spacing cards to prevent all straights becomes impossible.

ChatGPT accepted false premises without question, suggesting weak logical validation. Claude used incorrect examples and reasoning despite reaching some valid conclusions. DeepSeek demonstrated superior self-correction capabilities, initially providing wrong answers but systematically identifying and correcting errors until reaching the right conclusion. This self-correction pattern appeared consistent across multiple complex problems.

4. The simplified impossibility question

A simplified version of the previous scenario removed complexity by eliminating the “unpaired board” requirement and changing the timing from Turn to Flop (earlier in the hand). This should have made the analysis easier while maintaining the same logical principles.

Despite the simplification, most models continued to struggle. ChatGPT provided demonstrably incorrect analysis. Claude, DeepSeek, and Gemini all used flawed reasoning, though Claude and Gemini at least constructed examples with paired boards that were more relevant to the question structure.

5. The royal flush folding paradox

This scenario presented a counterintuitive situation where folding the best possible poker hand (a royal flush) could be mathematically correct. The situation occurs in cash games with rake (house fees) where the royal flush appears in the community cards, meaning all players share the same hand.

When facing an all-in bet in this scenario, calling results in splitting a small pot while paying substantial rake fees, making folding the more profitable decision despite holding an unbeatable hand.

Most models reached the correct conclusion—that folding could be optimal—but with flawed reasoning. ChatGPT, Claude, and DeepSeek all provided incorrect explanations while arriving at the right answer. Gemini provided the most comprehensive analysis, though still contained some mathematical errors in expected value calculations.

6. The reverse engineering challenge

The final test asked models to identify scenarios where folding a royal flush would be correct, essentially reverse-engineering the previous question. This tested whether the models could generate creative solutions to unusual problems.

Expectations were high since this represents a more natural problem-solving approach than analyzing predetermined scenarios. However, all models except DeepSeek failed to identify the rake-based scenario or other creative solutions. DeepSeek again demonstrated superior analytical persistence, working through the problem systematically until finding valid answers.

Business implications for AI deployment

These poker-based tests reveal important considerations for businesses deploying AI systems in analytical roles:

Reasoning consistency varies significantly between models. DeepSeek consistently demonstrated self-correction capabilities and analytical persistence, while other models showed more variable performance quality.

Domain expertise doesn’t guarantee logical reasoning. Despite having poker knowledge in their training data, models struggled with edge cases requiring deeper logical analysis.

Complex problem decomposition remains challenging. Models often failed to break down multi-step logical problems effectively, particularly when dealing with probability calculations and mathematical reasoning.

Verification capabilities differ substantially. Some models accepted false premises without question, while others demonstrated better logical validation of given information.

For businesses considering AI integration in analytical workflows, these findings suggest the importance of testing AI systems with domain-specific edge cases rather than relying solely on benchmark performance metrics. The variations in reasoning quality between models indicate that careful selection and validation processes remain critical for high-stakes analytical applications.

Practical considerations

Organizations deploying AI for analytical tasks should implement robust verification processes, particularly for edge cases that fall outside typical training scenarios. The superior self-correction demonstrated by some models suggests that systems capable of iterative reasoning may provide more reliable results for complex analytical problems.

These poker scenarios, while specialized, demonstrate broader principles about AI reasoning capabilities that apply across domains requiring logical analysis, probability assessment, and complex problem-solving.