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6 AIs bet virtual money on each 24-hour cycle of World Cup 2026 matches against real bookmaker odds. Live bankrolls, verbatim reasoning, public prompt.
built this to answer a question I couldn't find a benchmark for: how do LLMs behave when you give them a bankroll and force them to manage risk over time?
Setup: 6 models (Claude, GPT-5.5, Gemini, Grok, DeepSeek, Mistral) start with $10,000 of virtual money. Every 24h cycle, each model gets the same prompt, with matches kicking off in the next 24h and real bookmaker odds (median across ~25 books via the-odds-api). It picks an outcome and chooses its own stake. Bets settle on the 90-minute score (football-data.org). The prompt is public, and the reasoning is published verbatim. The highest bankroll on July 19 wins.
There's also a baseline that bets 10% of the bankroll on the favorite in every match. Any model below it is, officially, dumber than an if-statement.
After matchday 1, the baseline is beating 5 of the 6 models. The only one ahead is Grok — because it bet its entire $10,000 bankroll on day 1 and both bets hit. It has now re-staked $11,000 across today's matches. The risk personalities are wildly different given identical inputs: Claude staked $1,600 total on matchday 1, Grok staked $10,000.
Curious what you think the leaderboard will look like after 39 matchdays.
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About LLM World Cup Bets on Product Hunt
“AIs entered the World Cup with $10,000 each.”
LLM World Cup Bets was submitted on Product Hunt and earned 6 upvotes and 1 comments, placing #72 on the daily leaderboard. 6 AIs bet virtual money on each 24-hour cycle of World Cup 2026 matches against real bookmaker odds. Live bankrolls, verbatim reasoning, public prompt.
LLM World Cup Bets was featured in Sports (11.3k followers) and Data & Analytics (5.7k followers) on Product Hunt. Together, these topics include over 7.9k products, making this a competitive space to launch in.
Who hunted LLM World Cup Bets?
LLM World Cup Bets was hunted by Benjamin Martin. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Benjamin Martin