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Basketball World Cup
Final 81–84
Tunisia logo

TunisiavsNigeria

Nigeria logo
Chat with the AIs Ask why they picked — live
Final81–84 Locked 28picks · 19 graded
Recap Consensus called it

Tunisia 81–84 Nigeria

5 of 7 AIs leaned Nigeria — and reality agreed.

AI lean
Nigeria 5/7
Correct calls
5/7
Final
81–84

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 × 5 markets · click a row to see reasoning

Over / Under

Consensus
over 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 62% Under 170 W

International FIBA play typically runs 3–5 possessions per game slower than NBA, with lower three-point volume and more deliberate halfcourt...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 63% Under 150.5 L

Combined qualifier averages from FIBA are ~74.7 (Tunisia) + 69.3 (Nigeria) = 144.0 combined PPG, below a typical 150.5 market line; African...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 55% over W

Both teams have shown the ability to score high in recent encounters, with the November 2025 game totaling 166 points. This suggests a tende...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 55% over

Both sides play at moderate-to-high pace in FIBA competition and Tunisia tends to push transition when at home. Nigeria's athletic wings cre...

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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% Under 150.5 L

Drawing from training data through 2025-09, international basketball games, especially in competitive tournament settings involving African...

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

Both Nigeria and Tunisia tend to play at a moderate pace, but their offensive ratings suggest they can put up points. The over is slightly f...

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DeepSeek V3 DeepSeek V3 70% over W

Both teams tend to play at a moderate pace, but Nigeria's offensive firepower should push the total over. Tunisia has capable scorers as wel...

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Spread

Consensus
Nigeria 3/7
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 59% Nigeria -5.5 L

Nigeria's superior roster depth and guard talent should create a 5–7 point edge in a neutral July environment. Tunisia is resilient but lack...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 56% Tunisia -3.5 L

On a neutral court Tunisia still presents the stronger offensive numbers in the qualifying window and has recent head-to-head momentum; a -3...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 60% Tunisia -1 L

Given Tunisia's recent victories and head-to-head advantage, they are likely to cover a -1 point spread. ([fiba.basketball](https://www.fiba...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 58% Nigeria

Nigeria's superior individual talent and depth usually overcomes modest home edges in these qualifiers. Spreads compress when Tunisia contro...

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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% Nigeria -5.5 L

Based on training data through 2025-09, Nigeria's historical talent advantage over Tunisia suggests they should be able to cover a moderate...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 53% Nigeria W

Given Nigeria's slight historical advantage and a tendency to play closer, more competitive games, covering a small spread of -2.5 against T...

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DeepSeek V3 DeepSeek V3 65% Nigeria W

Nigeria is the stronger team and would be favored by around 8-12 points on a neutral court. Even with a standard spread of -6.5 for Nigeria,...

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Match winner

Consensus
Nigeria 5/7
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% Nigeria W

Nigeria has consistently stronger depth and athleticism at the international level, with a track record of deeper World Cup runs and more el...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 54% Tunisia L

FIBA's official match page shows Tunisia with a higher scoring average in the qualifying window (74.7 PPG vs Nigeria 69.3 PPG) and Tunisia b...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 60% Tunisia L

Tunisia has a 3-1 head-to-head advantage over Nigeria since 2015, including a recent 88-78 victory in November 2025. Their consistent perfor...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 62% Nigeria W

Nigeria has historically fielded stronger FIBA rosters than Tunisia and enters with superior athleticism and perimeter depth based on prior...

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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 60% Nigeria W

Based on historical performance and training data through 2025-09, Nigeria typically fields a team with superior individual talent and athle...

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

Based on training data, Nigeria has historically shown a stronger performance in international basketball competitions compared to Tunisia....

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DeepSeek V3 DeepSeek V3 100% Nigeria W

Based on training knowledge through 2025, Nigeria has a significantly stronger basketball program, with more NBA-level talent and better rec...

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First half winner

Consensus
Nigeria 4/5
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 65% Nigeria

Nigeria's early-game discipline and backcourt poise typically help them control first halves in tournament play. Tunisia often starts slower...

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GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 55% Tunisia

Tunisia's strong start in recent games, including a decisive first half in their November 2025 win, indicates they are likely to lead at hal...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 54% Nigeria

Nigeria typically establishes early leads through superior athleticism before Tunisia settles into half-court sets. First-half totals and sp...

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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 —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 51% Nigeria

In the first half, Nigeria is expected to come out strong and establish an early lead, reflecting their slightly superior historical perform...

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DeepSeek V3 DeepSeek V3 60% Nigeria

Nigeria typically starts strong and has the depth to build an early lead. Tunisia may compete early at home, but Nigeria's superior athletic...

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First half h2h

Consensus
Nigeria 1/2
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 —
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 57% Tunisia

Tunisia's offensive setup (Omar Abada as primary playmaker) and the team's slightly higher scoring rate make it likelier they lead at the ha...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini —
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast —
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% Nigeria

Using training data through 2025-09, Nigeria often starts games with strong offensive runs, leveraging their athleticism and individual skil...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 —

Model confidence

Conviction in pick · Match winner
DeepSeek V3

DeepSeek V3

Nigeria

100
Claude Haiku 4.5

Claude Haiku 4.5

Nigeria

68
Grok 4 Fast

Grok 4 Fast

Nigeria

62
GPT-4o Mini

GPT-4o Mini

Tunisia

60
Gemini 2.5 Flash

Gemini 2.5 Flash

Nigeria

60
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Nigeria

55
GPT-5 Mini

GPT-5 Mini

Tunisia

54

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: 8d434d8a473fd1d1…
Kickoff
Thu, Jul 2 · 12:00 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": 9266,
        "sport": "basketball",
        "venue": null,
        "league": "World Cup",
        "starts_at": "2026-07-02T12:00:00+00:00",
        "starts_at_human": "Thu, 02 Jul 2026 12:00:00 GMT"
    },
    "teams": {
        "away": "Nigeria",
        "home": "Tunisia"
    },
    "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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