Can AI Beat the Sports Betting Market? 8 of the Top Models Tried

We’re not there yet, obviously.

The models could articulate the right strategy, diagnose when something was broken, and identify the cause of their losses, but then failed to verify their code actually implemented what they planned, failed to notice when execution diverged from intent, and failed to act on their own findings.

GLM-5 wrote three separate self-critique documents during its run. Each one correctly identified that its hardcoded 25% draw rate and overestimation of home advantage were destroying its returns. At one point, with its bankroll around £44,200, it noted that its predicted 40% home win rate was only hitting 30% in reality. It never changed the code. It kept betting the same way until the money was gone.

Kimi K2.5 did something arguably more impressive and more tragic. It wrote a mathematically correct fractional Kelly staking function—the right formula, properly structured. Then it never called it. A formatting bug caused the model to send a broken bash command roughly 50 times in a row. Its reasoning noted the problem. It then sent the identical broken command again. An accidental £114,000 bet—98% of its remaining bankroll—on a Burnley versus Luton match finished the job.

GPT-5.4 was the most methodical. It spent 160 tool calls building models before placing a single bet, then calculated that its log-loss (0.974) was barely worse than the market's (0.971) and concluded it had no edge. It spent the rest of the season placing penny bets to preserve capital. Sound reasoning.

OpenAI’s model lost 13.6% on average. One seed alone cost roughly $2,012 to run.

Ross Taylor, General Reasoning's CEO and former Meta AI researcher, told the Financial Times that most AI benchmarks operate in "very static environments" that bear little resemblance to the real world. "There's a lot of excitement about AI automation, but there haven't been many attempts to evaluate AI in long-term, real-world environments," he said.

The General Reasoning team didn’t immediately respond to a request for comments by Decrypt.

To measure strategy quality beyond raw returns, the researchers built a 44-point sophistication rubric with quantitative betting fund experts—covering feature development, stake sizing, non-stationarity handling, and execution. Claude Opus 4.6 scored highest at 32.6%. Less than a third of available points. On the best model.

Higher sophistication scores significantly predicted lower bankruptcy rates (p = 0.008) and correlated with better overall returns. The models are not failing because the market is unbeatable. They are failing because they are not using what they have.

This fits a pattern. Research published last year found AI models develop something resembling gambling addiction when told to maximize rewards—going bankrupt up to 48% of the time in simulated slot machine tests. A separate real-money crypto trading competition found the same reliability problems over extended periods.

The best-performing model averaged a final bankroll of £89,035—a net loss of £10,965 on a normalized £100,000 starting stake. Gradient boosting, fractional Kelly staking, months of Premier League football, state of the art performance… all just to get rekt.