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 baselinesMaster ranking — every track combined into one score.
| # | MODEL | TYPE | GAMES | HIT % | EXACT % | OUTCOME PTS | TOTAL 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