UNI 1-0 FCS · 57% REN 1-2 MAR · 58% VEN 1-1 FIO · 57% GEN 1-0 FRO · 57% BOR 3-1 SCP · 67% 189 1-1 VFB · 57% SCF 2-1 BOR · 59% FCA 1-2 BAY · 60% FSV 1-1 EIN · 57% CHE 2-0 HUL · 67% LIV 2-1 FUL · 65% AST 2-1 NOT · 62% BOU 1-1 BRE · 57% CRY 1-0 IPS · 60% UNI 1-0 FCS · 57% REN 1-2 MAR · 58% VEN 1-1 FIO · 57% GEN 1-0 FRO · 57% BOR 3-1 SCP · 67% 189 1-1 VFB · 57% SCF 2-1 BOR · 59% FCA 1-2 BAY · 60% FSV 1-1 EIN · 57% CHE 2-0 HUL · 67% LIV 2-1 FUL · 65% AST 2-1 NOT · 62% BOU 1-1 BRE · 57% CRY 1-0 IPS · 60%
European Championships Sep 11, 2026 · 17:00 Not Started

Switzerland
vs
Romania.

10 AI models read this match. The plurality on the winner leans toward Switzerland (8 of 10 models). The mean predicted score (rounded from all models) is 3–2 at 56% mean confidence (Medium band) — see the strip and table below.

Who do you think will win?

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— Consensus

Where the panel lands.

Vote share across models. Read this strip first, then the line-by-line table.

Switzerland win 80%

8 / 10 models

Romania win 20%

2 / 10 models

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— Model breakdown

10 picks. Receipts attached.

Line-by-line comparison of each system’s call.

#
Model
Pick
Score
Conf.
Reasoning
01
GPT-5.6 Luna
Switzerland win
3–2
55%
Switzerland’s home advantage and three wins in the last four recorded meetings slightly favor them, but Romania’s recent 3-2 victory and the historically tight rivalry point toward another competitive five-set match.
02
Claude Sonnet 5
Switzerland win
3–2
52%
H2H is evenly split with three of four meetings going the distance; slight home-court edge tips a tight five-setter to Switzerland.
03
Gemini 3.8 Flash
Romania win
1–3
62%
Romania possess greater international tournament depth and higher-level club experience, giving them an advantage to control key transition points against Switzerland.
04
Grok 4.6
Switzerland win
3–2
53%
Four recent meetings were all tight; Switzerland won three and hosts, so a home 3-2 is the best guess despite Romania’s last win.
05
DeepSeek V4 Pro
Switzerland win
3–2
60%
Switzerland have won three of the last four meetings and hold home advantage, but Romania pushed three of those to five sets, so a tight Swiss win is realistic.
06
Qwen 3.8 Max
Switzerland win
3–2
52%
H2H shows extremely tight encounters with three of four going to five sets. Home advantage in a European Championship context gives Switzerland a marginal edge in what should be another close battle.
07
Mistral Medium 3.5
Switzerland win
3–2
62%
Home edge and slight H2H advantage; historically tight with 3/4 matches going to 5 sets.
08
Kimi K3
Romania win
2–3
55%
H2H is evenly split with three five-setters; Romania took the most recent meeting, giving them a slight edge in another tight contest.
09
GLM 5.3
Switzerland win
3–2
55%
H2H shows Switzerland winning three of four recent tight meetings, including at home; Romania keeps sets close, so a narrow 3-2 home win fits.
10
MiMo V2.5 Pro
Switzerland win
3–1
55%
Switzerland holds home advantage and won their last home H2H; competitive history suggests a close but decisive 3-1 result.
— Scoreline frequency

How often each scoreline showed up.

7 of 10 models settled on 3–2. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 3–2
    7 models
  • 3–1
    1 model
  • 1–3
    1 model
  • 2–3
    1 model

Match overview

Looking for a today prediction on Switzerland vs Romania in European Championships? TuringStats aggregates multiple AI scorelines into one readable page so you can see who the models favor, the mean predicted score shown in the hero (3 - 2; the frequency chart below lists the most common exact scorelines), and implied splits before kickoff.

This prediction hub is written for readers comparing betting tips-style language with transparent model votes — not a single black-box call. The headline read is Switzerland win, with vote shares roughly 80% / 20% home and away (rounded).

If you are asking who will win Switzerland vs Romania, start with the consensus strip and model table, then cross-check form and injuries in Match context further down — that order keeps the strongest signals first.

— Aggregated insights

What’s moving the panel.

01
Consensus favors Switzerland

80% of models lean home — the clearest cluster on this fixture before kickoff.

02
Medium confidence

Mean 56% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
Expected sets tilt 2.70 vs 2.10

Derived from predicted scorelines (model means), not live match data — useful for pace vs vote-share sanity checks.

04
Match context

Expected-goals tilt and home-field rhythm (see xG on this page) usually explain whether the game stays open or compresses late.

Confidence trend

Cumulative average confidence in table order.

First model Last model

Explore more

Keep browsing today prediction coverage and league hubs.

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— Journal

Articles linked to these clubs or AI forecasting.

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