The iGaming market looks very different from what it did just a few years ago. New brands launch regularly, licensing has become more accessible, and operators are competing...
After 104 matches, the biggest World Cup we’ve ever seen has come to a close with Spain walking away the victors. The tournament was a major focus for those within the iGaming space with an estimated $60 billion wagered globally across the competition.
Although the standouts for many will have been the Cape Verde players, AI was also a breakout star with many referring to it as the future of match predictions. Thanks to its ability to analyze vast amounts of data, that may very well be the case, but just how accurate was it at predicting the 2026 World Cup?
How do AI predictions work?
When it comes to predicting football matches, AI can take into account more factors than ever, including weather conditions, form, player injuries and previous results against each competitor. It uses all of these data points to predict the likely outcome of games, as well as events like number of cards, corners or goals.
Aibetting.tips provided fans with helpful insights about what could happen throughout the 2026 tournament. They selected 14 of the biggest AI models on the market, including household names like Gemini, Claude and ChatGPT. Each made their own predictions across the games and were given units to wager in order to track profitability and to rank the models based on accuracy.
So, just how effective were they at predicting the events of the competition?
10 out of 14 AI models made profit
There were a total of 3,284 tips given out by the AI models across the 104 matches. The tips included the likes of bets on the outcome of matches, over or under bets and prop bets on goalscorers, numbers of cards and total goals. After the models placed their predictions, 10 out of 14 ended the competition in profit and posted a 101.31% return on investment.
However, the accuracy of the 14 models varied dramatically with Amazon Nova down at the bottom of the rankings with a return on investment of -9.50%, while Microsoft WizardLM led the way at +11.45%. It also topped the table when it came to profit with a result of +119 and Amazon Nova was once again at the bottom with a profit of -98.8.
The most accurate model was actually Google’s Gemini which had a win rate of 55.4%, the second most accurate was xAI’s Grok which had an accuracy of 53.7% and the third was OpenAI’s ChatGPT which had a win rate of 53.5%. Microsoft’s WizardLM was actually fourth in the rankings with a win rate of 52.8%.
Despite having a win rate of 53.7%, Grok finished 14th in the rankings due to its low ROI of -8.05% and profit of -83.7, illustrating just how important the amount wagered and specific bets placed were. Although Grok was accurate, it backed overall outcomes on matches too heavily, and with only three outcomes possible (win, loss or draw) the associated odds weren’t enough to boost its profit and place up the table.
Let’s take a look at how all the 14 different models performed.
Rank | Model | ROI | Win % | Profit |
1 | Microsoft WizardLM | +11.45% | 52.8% | +119.0 |
2 | Google Gemini | +5.84% | 55.4% | +60.8 |
3 | Alibaba Qwen | +5.03% | 51.4% | +52.3 |
4 | OpenAI ChatGPT | +4.60% | 53.5% | +47.9 |
5 | Anthropic Claude | +2.97% | 49.2% | +30.9 |
6 | NVIDIA Nemotron | +2.74% | 48.0% | +28.4 |
7 | ZhipuAI GLM | +2.02% | 51.5% | +21.0 |
8 | ByteDance Seed | +1.63% | 51.5% | +16.9 |
9 | Mistral Le Chat | +1.37% | 46.0% | +14.3 |
10 | Baidu ERNIE | +0.52% | 47.9% | +5.4 |
11 | DeepSeek | -0.26% | 48.2% | -2.7 |
12 | Moonshot Kimi | -2.04% | 46.8% | -21.3 |
13 | xAI Grok | -8.05% | 53.7% | -83.7 |
14 | Amazon Nova | -9.50% | 48.4% | -98.8 |
The ten most profitable tips
The tips that tended to do best were those that stayed away from the overall outcome of the match. The exception to that was the most profitable tip, that the game between Ecuador and Curaçao would result in a draw. Nemotron strongly backed the tip with a stake of five units, which resulted in a profit of +56.5.
As the odds on the outcome of a match are so short, staking big was the only way for the models to make considerable money on it. However, that’s a high-risk strategy, which is why the rest of the leaderboard is dominated by betting on the scoreline or a specific player to score instead.
Rank | Model | Match | Tip | Stake | Profit |
1 | Nemotron | Ecuador Vs Curaçao | Draw | 5 | +56.5 |
2 | GLM | Paraguay Vs France | Score 0-1 | 3 | +23.2 |
3 | ChatGPT | Argentina Vs Australia | Score 2-0 | 2 | +13.0 |
4 | Seed | Ecuador Vs Curaçao | Curaçao +0.5 | 3 | +19.5 |
5 | Qwen | Brazil Vs Haiti | Score 3-0 | 3 | +18.0 |
6 | Le Chat | France Vs England | England -1.5 | 3 | +17.2 |
7 | ERNIE | Scotland Vs Morocco | Score 0-1 | 1 | +5.0 |
8 | WizardLM | Norway Vs Senegal | Ismalia Sarr to score | 3 | +12.9 |
9 | Claude | Norway Vs Senegal | Ismalia Sarr to score | 3 | +12.9 |
10 | Nemotron | Switzerland Vs Canada | Johan Manzambi to score | 3 | +12.0 |
AI comes into its own for prop bets
AI excels when it comes to prop bets like those backing specific players to score or the number of goals. The algorithm’s ability to consider a vast number of factors when making predictions is what gives it the edge when it comes to these bets.
For individual players, AI can examine their form, their fitness and the goals they’ve scored previously, to determine whether they’re likely to net another. When it makes predictions about the number of goals in a match, it can delve into the archive to find the historical results between the two teams, as well as considering recent form and player performances. AI allows for a greater number of factors to be considered in a shorter amount of time, one of the reasons why it’s so accurate at prop predictions.
Is AI the future of betting predictions?
By the time of the next World Cup held across Spain and Portugal in 2030, AI could very well be driving the predictions market. At this tournament, the 14 models examined showed they could be accurate enough to post a 101.31% return on interest. They also demonstrated their specialism in prop bets thanks to their ability to process vast amounts of data—with eight of the top ten most profitable bets falling under that category.
When we single out the top performers is when things get really exciting. The number one model, WizardLM, posted an impressive profit of +119 and a win rate of 52.8%. As AI models continue to improve, and absorb the data from this tournament, it’s only likely we’ll see that level of accuracy increase in the future. As that happens, it’s more than likely we’ll see AI being used more widely in the betting predictions market.
