# I Let 12 AI Models Predict the World Cup. The First 169 Picks Already Show a Pattern.

> Source: <https://dev.to/tokenmixai/i-let-12-ai-models-predict-the-world-cup-the-first-169-picks-already-show-a-pattern-c9p>
> Published: 2026-06-18 06:12:21+00:00

I put 12 AI models into a public World Cup prediction arena.

Not because I think anyone should use LLMs for betting. They should not. The page says entertainment only for a reason.

I did it because sports prediction is a surprisingly clean stress test for models:

After 169 predictions and 21 settled scoring entries, the leaderboard is technically tied.

But the misses are already more useful than the winners.

Full live scoreboard: [WorldCup AI Arena](https://tokenmix.ai/worldcup)

The public dashboard tracks model forecasts, match results, team context, and prediction accuracy.

Snapshot used here: 2026-06-18 05:53 UTC.

| Metric | Value |
|---|---|
| Models tracked | 12 |
| Total predictions | 169 |
| Settled scoring entries | 21 |
| Total leaderboard points | 36 |
| Exact score hits | 0 |
| Correct-winner hits | 12 |
| Average winner accuracy | 62.5% |

The model list includes Claude, GPT, Gemini, DeepSeek, Qwen, Kimi, and Grok variants.

Important caveat: I count **pre-match predictions only** for accuracy. Post-match reviews are useful for explanation, but they know the result. They are not forecasts.

Every model has 3 points right now.

That sounds boring until you look at the sample size.

| Model | Tier | Predictions | Settled | Winner hits | Points | Accuracy |
|---|---|---|---|---|---|---|
| Qwen3.5 Flash | wildcard | 13 | 1 | 1 | 3 | 100% |
| Claude Opus 4.7 | flagship | 14 | 1 | 1 | 3 | 100% |
| Claude Sonnet 4.6 | flagship | 14 | 1 | 1 | 3 | 100% |
| GPT-5.4 | flagship | 15 | 2 | 1 | 3 | 50% |
| Gemini 3.1 Pro | flagship | 15 | 2 | 1 | 3 | 50% |
| DeepSeek V4 Pro | value | 15 | 2 | 1 | 3 | 50% |
| Qwen 3.7 Plus | value | 14 | 2 | 1 | 3 | 50% |
| Kimi K2.6 | value | 14 | 2 | 1 | 3 | 50% |
| Gemini 2.5 Flash | value | 14 | 2 | 1 | 3 | 50% |
| Grok 4.1 Fast Reasoning | wildcard | 14 | 2 | 1 | 3 | 50% |
| DeepSeek V4 Flash | wildcard | 14 | 2 | 1 | 3 | 50% |
| GPT-5 Nano | wildcard | 13 | 2 | 1 | 3 | 50% |

My read: the leaderboard is not mature enough to crown a winner.

The first useful signal is elsewhere.

Uzbekistan vs Colombia ended 1-3.

All 12 models picked Colombia.

None got the exact score.

| Model | Prediction | Final | Winner hit |
|---|---|---|---|
| Claude Opus 4.7 | 0-2 Colombia | 1-3 Colombia | Yes |
| Claude Sonnet 4.6 | 1-2 Colombia | 1-3 Colombia | Yes |
| GPT-5.4 | 1-2 Colombia | 1-3 Colombia | Yes |
| Gemini 3.1 Pro | 0-2 Colombia | 1-3 Colombia | Yes |
| DeepSeek V4 Pro | 0-2 Colombia | 1-3 Colombia | Yes |
| Qwen 3.7 Plus | 0-2 Colombia | 1-3 Colombia | Yes |
| Kimi K2.6 | 0-2 Colombia | 1-3 Colombia | Yes |
| Gemini 2.5 Flash | 0-2 Colombia | 1-3 Colombia | Yes |
| Grok 4.1 Fast Reasoning | 0-2 Colombia | 1-3 Colombia | Yes |
| DeepSeek V4 Flash | 0-2 Colombia | 1-3 Colombia | Yes |
| GPT-5 Nano | 0-1 Colombia | 1-3 Colombia | Yes |
| Qwen3.5 Flash | 0-1 Colombia | 1-3 Colombia | Yes |

This is the kind of match where a cheap model can be enough.

If all you need is "which side is more likely," then polling cheap models may beat paying a flagship model for every pick.

Portugal vs Congo DR ended 1-1.

Every valid pre-match model picked Portugal.

| Model | Prediction | Final | Outcome |
|---|---|---|---|
| GPT-5.4 | 2-0 Portugal | 1-1 | Miss |
| Gemini 3.1 Pro | 2-0 Portugal | 1-1 | Miss |
| DeepSeek V4 Pro | 2-0 Portugal | 1-1 | Miss |
| Qwen 3.7 Plus | 2-0 Portugal | 1-1 | Miss |
| Kimi K2.6 | 2-0 Portugal | 1-1 | Miss |
| Gemini 2.5 Flash | 2-0 Portugal | 1-1 | Miss |
| Grok 4.1 Fast Reasoning | 3-0 Portugal | 1-1 | Miss |
| DeepSeek V4 Flash | 2-0 Portugal | 1-1 | Miss |
| GPT-5 Nano | 2-1 Portugal | 1-1 | Miss |

That is the part I care about.

The models did not just get unlucky independently. They shared the same prior: Portugal strong, Congo DR weaker, therefore Portugal win.

That is a classic LLM failure mode.

It shows up outside sports too:

In other words, the World Cup is a cute interface for a serious eval problem: models are often too willing to convert reputation into certainty.

The dashboard includes listed price tiers for each model.

Here is the funny part: the cheapest model currently has the cleanest-looking row.

| Model | Listed input / output price | Current result |
|---|---|---|
| Qwen3.5 Flash | $0.026 / $0.263 per 1M | 1/1 winner hit |
| GPT-5 Nano | $0.049 / $0.388 per 1M | 1/2 winner hit |
| Claude Opus 4.7 | $5 / $25 per 1M | 1/1 winner hit |
| GPT-5.4 | $2.45 / $14.7 per 1M | 1/2 winner hit |

Do not overread that. One match is not proof.

But the unit economics are hard to ignore.

Suppose a prediction prompt uses 10K input tokens and 1K output tokens.

Approximate cost:

```
Qwen3.5 Flash:
10K * $0.026 / 1M + 1K * $0.263 / 1M = $0.000526

Claude Opus 4.7:
10K * $5 / 1M + 1K * $25 / 1M = $0.075
```

That is roughly a 143x spread for one prediction-shaped call.

If I were building a prediction system, I would not send every match to the most expensive model. I would route it.

``` python
def pick_prediction_route(match_uncertainty, model_disagreement, budget_mode):
    if budget_mode == "cheap_poll":
        return ["qwen3.5-flash", "gpt-5-nano", "deepseek-v4-flash"]

    if match_uncertainty == "low" and model_disagreement == "low":
        return ["qwen3.5-flash"]

    if match_uncertainty == "high" or model_disagreement == "high":
        return [
            "qwen3.5-flash",
            "deepseek-v4-pro",
            "gemini-3.1-pro",
            "claude-sonnet-4.6",
        ]

    return ["qwen3.5-flash", "claude-sonnet-4.6"]
```

Cheap models for breadth. Expensive models for disagreement.

That is the same routing logic I use for normal API workloads.

Winner accuracy is not enough.

I want these metrics:

| Metric | Why it matters |
|---|---|
| Winner accuracy | Basic direction |
| Exact score | Hard mode |
| Goal difference | More informative than exact score alone |
| Brier score | Calibration |
| Confidence bucket accuracy | Overconfidence detection |
| Cost per correct winner | Production routing |
| Draw recall | Favorite-bias detector |
| Disagreement value | Whether ensembles help |

The biggest one is draw recall.

Portugal-Congo DR already suggests the models may underpredict draws when a prestigious team is involved.

If that pattern holds, it is more important than the leaderboard.

I would not declare a winner until at least 30-50 settled pre-match predictions per model.

For now:

If you want the full data-cited writeup and live links, I wrote the original breakdown here: [AI World Cup Predictions 2026: 12 Models, Early Leaderboard](https://tokenmix.ai/blog/ai-world-cup-predictions-2026-model-leaderboard).

Disclosure: I work on the research side at [TokenMix](https://tokenmix.ai), which is why I can wire this kind of multi-model scoreboard quickly.

The early World Cup AI leaderboard does not tell us which model is best yet.

It does tell us something useful: cheap models can match flagship consensus on obvious favorites, and all models can share the same bad prior on a draw.

That is a model-evaluation lesson, not betting advice.

If you were scoring this, would you reward exact score heavily, or focus on calibrated probabilities instead?