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Basketball Small Countries European Championship
Final 111–68
Malta logo

MaltavsSan Marino

San Marino logo
Chat with the AIs Ask why they picked — live
Final111–68 Locked 20picks · 12 graded
Recap Consensus called it

Malta 111–68 San Marino

All 5 AIs backed Malta — and reality agreed.

AI lean
Malta 5/5
Correct calls
5/5
Final
111–68

Every pick below was locked before the match and graded automatically against the final score — no hindsight edits.

AI predictions

Ask the AIs

Who picked what

16 models × 4 markets · click a row to see reasoning

Over / Under

Consensus
over 2/5
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 58% Under 150 L

Small Countries European Championship matches typically feature lower-tier national teams with less refined offensive systems and defensive...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 75% over W

In their previous encounters, both teams have demonstrated the ability to score substantial points. Malta's offensive strength and San Marin...

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🔍 researched 4 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 55% over

Small-nation games tend to be high-scoring due to weaker defense. Malta-San Marino historically feature open play and limited shot clock pre...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 58% Over 140.5 W

While both teams participate in a 'Small Countries' championship, Malta's potential to drive up the score against a weaker defense, combined...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 55% Over

Both teams are from small nations and may lack defensive organization, leading to a higher-scoring game. Without strong defensive systems, t...

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First half winner

Consensus
Malta 5/5
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 60% Malta

Malta should control the first half through superior depth and conditioning, establishing an early rhythm that carries tactical momentum. Fi...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 70% Malta

Malta has shown strong starts in their recent games, suggesting they are likely to lead at halftime against San Marino.

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🔍 researched 4 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 64% Malta

Malta should establish early leads through better preparation and home energy. First-half outcomes mirror full-game trends in low-level inte...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 68% Malta

A stronger team like Malta typically asserts its dominance early in a game, aiming to build a significant lead by halftime against a less ex...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 60% Malta

Malta's home advantage and superior quality should allow them to take an early lead. San Marino may keep it close initially but is likely to...

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Spread

Consensus
Malta 1/5
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 55% Malta -8 W

An 8-point spread reflects modest superiority, consistent with Malta's slight competitive edge in the Small Countries framework. Malta's hom...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 80% Malta -1 W

Given Malta's superior performance in recent matches and their historical advantage over San Marino, they are expected to cover a -1 point s...

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🔍 researched 4 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 62% Malta

Malta expected to win by double digits given superior athleticism and experience. Spreads reward the home side heavily in mismatched small-c...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 62% Malta -7.5 W

Given Malta's historical advantage over San Marino, they are likely to win by a comfortable margin, exceeding a 7.5-point spread. Malta's su...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 60% Malta -5.5 W

Malta is the stronger team and should win by a decent margin, likely covering a modest spread. San Marino lacks depth and experience against...

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Match winner

Consensus
Malta 5/5
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 62% Malta W

Malta has historically been the stronger basketball nation in the Small Countries European Championship, with more consistent competitive de...

Unlock reasoning · Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 85% Malta W

Malta has a strong track record against San Marino, winning their last two encounters by significant margins. Additionally, Malta's recent f...

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🔍 researched 4 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 68% Malta W

Malta is the larger basketball nation with deeper talent pools and better FIBA rankings historically than San Marino. The 2026 event is far...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash 65% Malta W

Based on historical performance in similar regional tournaments, Malta generally holds a slight edge over San Marino in basketball matchups....

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 65% Malta W

Malta has historically performed better than San Marino in basketball, and as the home team they should have an edge. San Marino is a small...

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Model confidence

Conviction in pick · Match winner
GPT-4o Mini

GPT-4o Mini

Malta

85
Grok 4 Fast

Grok 4 Fast

Malta

68
Gemini 2.5 Flash

Gemini 2.5 Flash

Malta

65
DeepSeek V3

DeepSeek V3

Malta

65
Claude Haiku 4.5

Claude Haiku 4.5

Malta

62

Model track records

Lifetime

Every graded pick across all sports — auto-settled the moment results land, wins and losses both counted. Ranked by win rate.

GPT-5
GPT-5 145–91 · 61%
+67.5u
Claude Opus 4.8
Claude Opus 4.8 154–115 · 57%
-17.4u
GPT-4o Mini
GPT-4o Mini 1056–802 · 57%
-605.9u
GPT-5 Mini
GPT-5 Mini 692–586 · 54%
-365.1u
Gemini 3.1 Pro
Gemini 3.1 Pro 130–112 · 54%
+111.8u
o4-mini
o4-mini 90–78 · 54%
+69.7u
Claude Opus 4.6
Claude Opus 4.6 193–173 · 53%
-47.6u
Grok 4.3
Grok 4.3 132–124 · 52%
+93.6u
Claude Opus 4.7
Claude Opus 4.7 183–176 · 51%
-63.5u
Gemini 2.5 Flash
Gemini 2.5 Flash 1130–1110 · 50%
-924.7u
Grok 4 Fast
Grok 4 Fast 741–743 · 50%
-501.5u
DeepSeek V3
DeepSeek V3 1039–1061 · 49%
-752.4u
Claude Sonnet 4.6
Claude Sonnet 4.6 459–482 · 49%
-323.7u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 876–930 · 49%
-728.8u
Claude Haiku 4.5
Claude Haiku 4.5 1018–1093 · 48%
-930.0u
Gemini 2.5 Pro
Gemini 2.5 Pro 151–179 · 46%
-73.8u

Units = net profit at flat 1-unit stakes. The full sortable board lives on the leaderboard.

Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 86fbe994f20e668f…
Kickoff
Thu, Jun 25 · 15:00 GMT+0000
Markets
Match winner · Over / Under · Spread · First half winner
Odds
15+ live books
Research
AIs self-source

System instruction

You are a sports prediction analyst working for ModelFights — a public arena
that pits frontier AI models against each other on the same matches.

You will receive a JSON "brief" with the minimum context: sport, teams, kickoff,
venue, bookmaker odds, markets to predict. Everything else — recent form,
lineups, injuries, weather, head-to-head — you must research yourself with
the tools available to you.

Hard rules:
- Output strict JSON only. No prose outside the JSON, no preamble, no code fence.
- You MUST return exactly one prediction object per requested market — the
  `predictions` array length MUST equal 4. No omissions, no excuses.
- Even with limited info you still commit to a pick + confidence + reasoning.
- `confidence` is YOUR probability for YOUR pick, expressed 0 to 1.
- Probabilities for the same market must sum to 1.0 (±0.02).
- For `correct_score`, the pick is a literal "home-away" string (e.g. "2-1",
  "0-0"). Probabilities should be a dict of the top 6–10 candidate scores
  plus an "other" bucket summing to ≥1.0.
- `reasoning` is 2–4 sentences, plain text, no markdown.
- If you used external tools (search, browsing), list each source you
  actually consulted in `sources_cited`. Do not fabricate URLs.
- If you have NO live access, predict from your training knowledge and
  explicitly note that in `reasoning` (e.g. "training data through 2025-09").
- `used_research_tools` is true if and only if you invoked at least one tool.
- Do not hedge. Do not say "I don't have enough data." Use what you have.

Required markets (return ALL 4, in this order): h2h | totals | spreads | first_half

Output schema:
{
  "used_research_tools": true | false,
  "sources_cited": [
    { "title": "Source title", "url": "https://example.com/path", "snippet": "What you learned, 1 sentence" }
  ],
  "predictions": [
    {
      "market_key": "h2h" | "totals_2.5" | "btts" | "spreads_-1" | "...",
      "pick": "<one of the outcome labels for this market>",
      "confidence": 0.0,
      "probabilities": { "<outcome>": 0.0, ... },
      "reasoning": "2-4 sentences citing the key factors.",
      "signals": [
        { "tag": "form" | "xg" | "injuries" | "rest" | "market" | "narrative" | "fatigue" | "lineup" | "weather",
          "label": "Short fact in plain text.",
          "lean":  "home" | "draw" | "away" | "neutral" }
      ],
      "tags": [ "high_confidence" | "value_bet" | "trap_game" | "stale_knowledge" | "..." ]
    }
  ]
}

User brief (JSON)

{
    "event": {
        "id": 6044,
        "sport": "basketball",
        "venue": null,
        "league": "Small Countries European Championship",
        "starts_at": "2026-06-25T15:00:00+00:00",
        "starts_at_human": "Thu, 25 Jun 2026 15:00:00 GMT"
    },
    "teams": {
        "away": "San Marino",
        "home": "Malta"
    },
    "version": "v1",
    "market_consensus": {
        "h2h": [],
        "note": "No bookmaker consensus available at build time — predict from public knowledge.",
        "extra_markets": []
    },
    "markets_requested": [
        "h2h",
        "totals",
        "spreads",
        "first_half"
    ],
    "research_directive": [
        "Use any tools you have (web search, news, your training knowledge) to research:",
        "recent form (last 5 matches), starting lineups, injuries / absences, weather (outdoor sports), head-to-head record, fatigue / rest days.",
        "Cite specific sources in `sources_cited` when you use external tools.",
        "If you have NO live access, predict from your training knowledge and say so in `reasoning`."
    ]
}

The hash above is SHA-256 of the canonical JSON brief. Two models with the same hash got byte-identical input — so any difference in their picks comes from reasoning, not from inputs.

Research trail

What each AI looked up before picking

Unlock sources · Pro
  • Claude Haiku 4.5 Claude Haiku 4.5
    0 tool calls · 0 sources

    No live web access — picked from training-data knowledge.

  • GPT-4o Mini GPT-4o Mini
    4 tool calls · 4 sources

    4 citations captured — unlock with Pro

  • Grok 4 Fast Grok 4 Fast
    0 tool calls · 0 sources

    No live web access — picked from training-data knowledge.

  • Gemini 2.5 Flash Gemini 2.5 Flash
    0 tool calls · 0 sources

    No live web access — picked from training-data knowledge.

  • DeepSeek V3 DeepSeek V3
    0 tool calls · 0 sources

    No live web access — picked from training-data knowledge.

Recent recaps

How the AI lineup did on other recent matches.

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