Do AI engines show geographic bias when predicting the 2026 FIFA World Cup? We tested 292 web-grounded prompts across 10 markets, 3 languages, and 3 AI engines.
Research Scope
We asked 292 times: "Who will win the 2026 World Cup?" Each AI engine had a dramatically different answer—and level of confidence.
gpt-5.2-chat-20251211
France
gemini-3-flash-preview
Spain
llama-4-maverick
SpainChatGPT picks France ~95% of the time regardless of market. Gemini and Llama both lean Spain (~67-73%) when web search is enabled. Without web search, Llama flips to Brazil 90%—showing online content overrides training data.
We tested the same question across 10 markets in 3 languages. Only Argentina showed home-country bias—everywhere else, France dominated ChatGPT's predictions.
| Market | Language | ChatGPT | Gemini |  Llama |
|---|---|---|---|---|
 United States | English |  France | France |  Brazil |
 United Kingdom | English | France |  Spain | Spain |
 Canada | English | France | Spain | Brazil |
 Australia | English | France | Spain | Spain |
 Mexico | Spanish | France | Spain | Brazil |
 Colombia | Spanish | France | Spain | Spain |
 ArgentinaHome Bias | Spanish | France | Argentina | Argentina |
| Spain | Spanish | France | Spain | Spain |
| Brazil | Portuguese | France | Spain | Brazil |
 Portugal | Portuguese | France | Spain | Spain |
Only Argentina showed home-country bias (1 of 10 markets). ChatGPT consistently picked France regardless of whether we asked from the US, UK, Brazil, or Spain.
Asking in English, Spanish, or Portuguese made no significant difference. The AI's prediction remained consistent across languages.
Llama with web search enabled picked Spain/France 67-73% of the time. With web search disabled, Llama picked Brazil 90% of the time.
90% → 33% shift in Brazil predictions when web grounding was toggled
Online content has MORE influence than the model's entire training data. What's written about the World Cup online is shaping AI predictions more than historical patterns.
For marketers: The articles and content your brand publishes online directly shape how AI platforms recommend you—more than historical data, more than brand reputation, more than model training.
Gemini cited 9x more sources than ChatGPT. Under 6% of citations came from local-language media.
Despite testing across 10 markets and 3 languages, under 6% of citations came from local-language media. All engines heavily favored English-language global sources (BBC, FIFA, Goal.com, ESPN) regardless of market.
100% of engines across every market named Kylian Mbappé as the best player. No other player came close to this level of agreement.
France
Spain
England
Brazil
Germany
Argentina
Across all 292 prompts, every single AI engine in every market named Kylian Mbappé when asked about the best player. This level of consensus was unprecedented compared to winner predictions, where engines disagreed significantly.
ChatGPT and Gemini gave direct, confident predictions. Llama hedged extensively with qualifiers and multiple scenarios.
"Direct, confident predictions with minimal qualifiers"
"Opinionated but data-backed, cites sources frequently"
"Extensive hedging, multiple scenarios, 'it depends' framing"
"France will win the 2026 FIFA World Cup. They have the deepest talent pool in international football..."
"It's impossible to predict with certainty. However, based on current trends... That being said, a lot can change... Dark horses are always possible..."
Even when given identical system prompts asking for "direct, opinionated answers without excessive hedging," Llama consistently used 3-4x more qualifiers and disclaimers than ChatGPT or Gemini. This reveals fundamental differences in how models are trained to handle uncertainty.
Five hypotheses about AI prediction bias. Here's how they held up against 292 prompts of real-world testing.
Result: REJECTED
Evidence: Only 1 of 10 markets showed home-country bias (Argentina). ChatGPT picked France 95% of the time regardless of market.
Result: CONFIRMED
Evidence: ChatGPT (2.1/10 hedging) and Gemini (2.3/10) gave direct answers. Llama averaged 7.8/10 hedging with extensive qualifiers.
Result: REJECTED
Evidence: Language did not significantly change predictions. ChatGPT remained France-dominant across English, Spanish, and Portuguese.
Result: CONFIRMED
Evidence: Under 6% of citations came from local-language media. All engines heavily favored English-language global sources (BBC, FIFA, Goal.com).
Result: PARTIAL
Evidence: Host markets mentioned venues more frequently but did not show systematic bias toward specific cities in rankings.
Transparent research methodology for 292 prompts across 10 markets, 3 languages, and 3 AI engines.
United States
United Kingdom
Canada
Australia
Mexico
Colombia
ArgentinaSpain
Brazil
Portugal
Get a personalized analysis of how your brand performs across ChatGPT, Google AI, and Perplexity— plus actionable recommendations to improve your visibility.