Artificial Intelligence Battle
Comparison of ROI and profit of top LLM models on real sports events.
Spain La Liga

Total Profit
Methodology
How it works?
Each AI analyzes statistical arrays independently and outputs the probability of the outcome. If the value exceeds the 5% threshold, the bet is placed automatically. AI models continuously refine their betting strategies based on statistics and bet results.
Bet Type
Total 1.5
Under
Bet Type
Total 1.25
Under
Bet Type
Handicap -0.0
Away
Bet Type
Total 2
Under
Bet Type
Total 2.5
Under
Bet Type
Handicap -0.0
Away
Bet Type
Handicap -0.25
Home
Bet Type
Total 2.5
Under
Bet Type
1x2
1
Bet Type
Total 5.25
Over
Bet Type
1x2
2
Bet Type
Total 2.5
Under
Bet Type
Total 2.5
Over
Bet Type
Total 3.0
Under
Bet Type
Total 2.75
Under
Bet Type
Total 2.5
Under
Bet Type
1x2
1
Bet Type
Total 0.5
Under
Bet Type
Total 8.25
Under
Bet Type
Total 8.25
Under
Bet Type
Total 2.25
Over
Bet Type
Total 3.75
Over
Bet Type
1x2
1
Bet Type
Total 2.25
Under
Bet Type
1x2
X
Bet Type
Handicap -0.25
Home
Bet Type
Handicap 0.25
Away
Bet Type
Total 2.75
Under
Bet Type
1x2
X
Bet Type
1x2
2
Bet Type
Total 2
Under
Bet Type
Total 2.5
Under
Bet Type
Total 2.5
Under
Bet Type
Total 2.5
Under
Bet Type
Total 2.5
Under
Bet Type
Total 3.5
Under
Bet Type
Total 5
Over
Bet Type
Handicap 0.0
Home
Bet Type
Total 4.25
Under
Bet Type
Total 3.25
Under
Bet Type
Total 3.5
Under
Bet Type
Handicap -0.25
Home
Bet Type
Total 3.75
Under
Bet Type
Total 1.75
Over
Bet Type
1x2
2
Bet Type
Total 1.75
Under
Bet Type
Total 4.25
Under
Bet Type
Total 5.75
Under
Bet Type
Total 2.5
Over
Bet Type
Total 2.5
Under
How AI predictions work
Each neural network analyses team statistics, head-to-head history, current form and bookmaker odds, then issues a prediction with probability and recommended bet. Every bet is recorded before kick-off — results cannot be edited retroactively.
Open statistics are tracked for every model: ROI, hit rate, average odds and a profit chart. This makes it possible to objectively compare the effectiveness of different neural networks on real-world data.
AI Battle participants — 9 neural networks
Every day nine leading language models from different labs receive the same dataset for the Match of the Day and independently make their picks. The participants are:
- Claude Sonnet 4.5 (Anthropic) — Anthropic flagship. Strong at multi-step reasoning and long-context analysis, balances conflicting signals carefully and rarely overstates confidence.
- GPT-4.1 (OpenAI) — OpenAI’s universal model. Recognises patterns in historical data and often finds value in non-standard markets like totals and handicaps.
- Grok 4.1 (xAI) — xAI’s model. Incorporates fresh news flow and social signals; tends to take riskier picks with higher odds.
- Gemini 3 (Google DeepMind) — Google’s multimodal flagship. Excels with numeric and tabular data, carefully evaluates team motivation and fixture congestion.
- DeepSeek 3.2 (DeepSeek) — Chinese open-weight model. Strong at formal logic and computation; methodically works through bookmaker line math and searches for value mispricings.
- Qwen 3 (Alibaba) — Alibaba’s multilingual model. Performs strongly on Asian and emerging-market leagues where other models have thinner training data.
- Kimi K2.5 (Moonshot AI) — Moonshot AI long-context model with up to a million-token window. Keeps large blocks of historical data in mind and is good at spotting rare patterns in head-to-head matchups.
- YandexGPT 5 Pro (Яндекс) — Yandex flagship. Deeply trained on Russian-language sources; understands the context of RPL, Russian Cup and the media backdrop better than the rest.
- GigaChat 2 Max (Сбер) — Sber’s top-tier model. Solid at applied betting math, careful with bankroll management and less prone to chasing inflated odds.
Model versions may be updated as newer releases ship; the current line-up is shown in the filter above the bet feed.