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 · 14:00 Not Started

Germany
vs
Latvia.

10 AI models read this match. The plurality on the winner leans toward Germany (10 of 10 models). The mean predicted score (rounded from all models) is 3–1 at 68% mean confidence (Medium band) — see the strip and table below.

Who do you think will win?

Vote before kick-off and compare with AI

— Consensus

Where the panel lands.

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

Germany win 100%

10 / 10 models

Latvia win 0%

0 / 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
Germany win
3–0
72%
Germany’s established international level and prior 3-1 win over Latvia support a home victory. With limited current-form and venue detail, a controlled 3-0 is the best estimate but confidence remains moderate.
02
Claude Sonnet 5
Germany win
3–1
68%
Germany has stronger European ranking and depth than Latvia, plus prior H2H win; expect a competitive but Germany-favored contest.
03
Gemini 3.8 Flash
Germany win
3–0
78%
Germany possesses vastly superior depth and elite international experience compared to Latvia, making a dominant straight-sets victory the most probable outcome.
04
Grok 4.6
Germany win
3–1
61%
Germany won the 2021 meeting 3-1 and remain the stronger European side at home; Latvia can take a set but not the Championship match.
05
DeepSeek V4 Pro
Germany win
3–1
68%
Germany's superior depth and home advantage; Latvia can compete in patches but lack consistency. H2H 2021 3-1 Germany supports a similar margin.
06
Qwen 3.8 Max
Germany win
3–0
72%
Germany's superior depth and prior 3-1 H2H win over Latvia suggest dominance; Latvia's lower tier makes a straight-sets home victory plausible.
07
Mistral Medium 3.5
Germany win
3–1
70%
Germany clear favorite; home advantage; last H2H 3-1
08
Kimi K3
Germany win
3–1
72%
Germany are a clear tier above Latvia and won their 2021 Euro meeting 3-1; home advantage and superior depth suggest a comfortable but not flawless win.
09
GLM 5.3
Germany win
3–1
62%
Germany won the only recent H2H 3-1 and hold a deeper European Championship roster; Latvia can take a set, but Germany's block and depth should prevail.
10
MiMo V2.5 Pro
Germany win
3–1
60%
Germany won the last H2H 3-1 and has home venue advantage, though roster changes may affect the outcome.
— Scoreline frequency

How often each scoreline showed up.

7 of 10 models settled on 3–1. That convergence is a strong scoreline signal—many fixtures fan out wider across the panel.

  • 3–1
    7 models
  • 3–0
    3 models

Match overview

Looking for a today prediction on Germany vs Latvia 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 - 1; 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 Germany win, with vote shares roughly 100% / 0% home and away (rounded).

If you are asking who will win Germany vs Latvia, 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 Germany

100% of models lean home — the clearest cluster on this fixture before kickoff.

02
Medium confidence

Mean 68% across the panel with real dispersion — compare unanimous calls vs split tickets in the model table.

03
Expected sets tilt 3.00 vs 0.70

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

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Keep browsing today prediction coverage and league hubs.

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