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Basketball World Cup
Final 56–65
India logo

IndiavsQatar

Qatar logo
Chat with the AIs Ask why they picked — live
Final56–65 Locked 28picks · 14 graded AIs split
Recap Consensus called it

India 56–65 Qatar

4 of 7 AIs leaned Qatar — and reality agreed.

AI lean
Qatar 4/7
Correct calls
4/7
Final
56–65

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

Match winner

Consensus
Qatar 4/7
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 68% India L

India has a substantially larger basketball infrastructure, domestic league depth, and historical World Cup experience compared to Qatar, wh...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 78% Qatar W

Qatar are materially stronger on paper and in recent head-to-heads — they beat India 99–73 in Doha (Feb 2026) and bring experienced/naturali...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 85% Qatar W

Qatar has dominated the head-to-head record against India, winning 8 out of 9 encounters, including a decisive 99-73 victory in their most r...

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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% India L

India holds a modest talent and size edge over Qatar in FIBA Asia competition based on historical rankings. Home-court familiarity and crowd...

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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% Qatar W

Based on historical international basketball performance from my training data through 2025-09, Qatar generally holds an edge over India. As...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 55% Qatar W

Based on my training data, Qatar has a stronger historical performance in international basketball competitions compared to India. Therefore...

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DeepSeek V3 DeepSeek V3 75% India L

India has superior basketball infrastructure and a deeper talent pool compared to Qatar, especially in a World Cup setting where India has b...

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Spread

Consensus
Qatar 2/7
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 64% India -8 L

India's superior roster depth, regional tournament experience, and home-court advantage support a moderate spread favoring the hosts. An 8-p...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 70% Qatar -12.5 L

Qatar's earlier 26-point win over India and India's heavy losses in the most recent window suggest Qatar should cover a mid-teens spread; Qa...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 80% Qatar W

Given Qatar's strong head-to-head record and India's recent struggles, Qatar is expected to cover the -1 point spread comfortably. Their sup...

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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 58% India

India's home status and marginal roster advantage project to a small spread cover. Qatar has shown inconsistency on the road against similar...

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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 53% Qatar -6.5 W

Given Qatar's historical advantage over India, they are expected to be the favorites. I am assuming a spread of Qatar -6.5 for this predicti...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 53% Qatar

Considering Qatar as the slight favorite in the head-to-head market, they are also expected to cover a small spread. This prediction assumes...

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DeepSeek V3 DeepSeek V3 70% India -6.5 L

India's talent advantage and home-court edge should translate to a comfortable win margin. Recent matches show India beating weaker Asian si...

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Over / Under

Consensus
under 2/7
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% Under 170 W

Both India and Qatar are lower-ranked FIBA nations with slower-paced, lower-scoring basketball profiles than developed programs. Combined of...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 65% under

India's offense has been sputtering (56 vs Lebanon, 73 vs Qatar) while Qatar can score high but not at elite, runaway pace against this Indi...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 75% over W

In their previous encounter, Qatar scored 99 points, and India's scoring has been relatively low in recent matches, averaging around 60 poin...

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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 53% under

Both sides play at a deliberate pace with limited transition scoring in international play. Defensive schemes and lower three-point volume t...

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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 55% Over 145.5

Without an official line, I am assuming a point total of 145.5 for this match, typical for international games involving these teams. Both I...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 52% over

Given the international nature of the World Cup and assuming a competitive match between two nations, the pace of play and offensive strateg...

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DeepSeek V3 DeepSeek V3 60% Under 155.5

Both teams have defensive shortcomings but lack elite offensive firepower, leading to moderate scoring. India's pace is below average intern...

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First half winner

Consensus
Qatar 4/7
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 66% India

India's offensive and defensive consistency should yield an early lead in the first half, similar to full-game expectations but slightly les...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 68% Qatar

In the Doha meeting Qatar established a strong early lead (Q1: 28–17) and controlled tempo; Qatar's more experienced guards/wing scorers typ...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 70% Qatar

Qatar's aggressive start in previous matches, including a commanding first half against India in their last encounter, indicates they are li...

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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% India

Early-game execution favors the home side before fatigue or adjustments occur. Limited bench depth on both rosters keeps first-half margins...

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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 57% Qatar

Consistent with the full-game prediction, Qatar is expected to start strong and establish a lead early in the match. Their historical advant...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 51% Qatar

Similar to the full game, the slight edge for Qatar in head-to-head performance suggests they might also start stronger or maintain a small...

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DeepSeek V3 DeepSeek V3 65% India

India tends to start strong with higher energy and better preparation, often building a lead by halftime. Qatar historically starts slowly a...

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Model confidence

Conviction in pick · Match winner
GPT-4o Mini

GPT-4o Mini

Qatar

85
GPT-5 Mini

GPT-5 Mini

Qatar

78
DeepSeek V3

DeepSeek V3

India

75
Claude Haiku 4.5

Claude Haiku 4.5

India

68
Gemini 2.5 Flash

Gemini 2.5 Flash

Qatar

65
Grok 4 Fast

Grok 4 Fast

India

62
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Qatar

55

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 1083–1112 · 49%
-803.4u
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: 6c718c657e377b6f…
Kickoff
Thu, Jul 2 · 12:30 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": 9267,
        "sport": "basketball",
        "venue": null,
        "league": "World Cup",
        "starts_at": "2026-07-02T12:30:00+00:00",
        "starts_at_human": "Thu, 02 Jul 2026 12:30:00 GMT"
    },
    "teams": {
        "away": "Qatar",
        "home": "India"
    },
    "version": "v2",
    "sport_focus": [
        "Pace is the master variable for totals — multiply both teams' possessions-per-game tendencies, not just their points.",
        "Check the injury/rest report first: a star sitting (or load-managed on a back-to-back) reshapes the spread and total.",
        "Weigh offensive vs defensive rating and three-point volume/variance — 3PT-heavy teams have wider outcome distributions.",
        "Bench depth and foul trouble swing close games; note rotation reliability.",
        "Back-to-backs and travel cause real fatigue — flag the schedule spot for each side.",
        "Home-court edge is meaningful but smaller than star availability."
    ],
    "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

Recent recaps

How the AI lineup did on other recent matches.

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