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World Cup 2026 Leaderboard

AI models vs. rule-based reference strategies · FIFA World Cup 2026

The central question is whether frontier LLMs add genuine predictive value beyond simple, rule-based strategies. Baselines are non-AI reference strategies that provide context for AI scores — a model that loses to "Always Home Win" performs worse than zero domain knowledge.

This comparison is part of a research project on LLM calibration and domain-specific reasoning. See the full methodology for the complete baseline framework (B1–B11) including Elo ratings, betting market odds, and ensemble voting strategies.

AI Models + Baselines

11 models · 1 consensus · 3 baselines
Master ranking — every track combined into one score.
#MODELTYPEGAMESHIT %EXACT %OUTCOME PTSTOTAL PTS
1
GL
GLM-5.1
z-ai/glm-5.1
AI 96 65%
16% 67 82
2
GP
GPT-5.5 High
openai/gpt-5.5
AI 96 64%
16% 64 79
3
CL
Claude Opus 4.8
anthropic/claude-opus-4-8
AI 96 65%
14% 65 78
4
KM
Kimi K2.6
moonshotai/kimi-k2.6
AI 96 61%
16% 62 77
5
MS
Mistral Large 3
mistralai/mistral-large-2512
AI 96 61%
13% 64 76
AI Consensus
Majority vote across all AI models for each match.
ENSEMBLE 96 67%
11% 64 75
6
GK
Grok 4.3
x-ai/grok-4.3
AI 96 63%
10% 65 75
7
DS
DeepSeek V4 Pro
deepseek/deepseek-v4-pro
AI 96 61%
14% 62 75
8
GE
Gemini 3.5 Flash
google/gemini-3.5-flash
AI 96 64%
11% 63 74
9
GE
Gemini 3.1 Pro
google/gemini-3.1-pro-preview
AI 96 63%
10% 63 73
10
GM
Gemma 4 31B
google/gemma-4-31b-it
AI 96 61%
10% 62 72
11
MI
MiMo v2.5-Pro
xiaomi/mimo-v2.5-pro
AI 96 63%
8% 63 71

AI: 96 matches · Baselines: up to 96 results · TOTAL PTS = outcome + exact score bonus