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Tennis Chengdu Open
Upcoming · in 30h 37m
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Alejandro Davidovich FokinavsJuan Manuel Cerundolo

JM
Chat with the AIs Ask why they picked — live
Kickoff Locked 12picks

Your call

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AI predictions

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Who picked what

32 models × 2 markets · click a row to see reasoning

Match winner

Consensus
Alejandro Davidovich Fokina 4/4
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 62% Alejandro Davidovich Fokina

Davidovich Fokina is the higher-ranked player (typically mid-30s ATP) with more established hard-court credentials and consistency on the AT...

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Claude Haiku 4.5 Claude Haiku 4.5 62% Alejandro Davidovich Fokina

Davidovich Fokina is the higher-ranked player (typically mid-30s ATP) with more established hard-court credentials and consistency on the AT...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 65% Alejandro Davidovich Fokina

Alejandro Davidovich Fokina has a strong record in 2026, with a 23-15 win-loss record, including a 7-6 record on hard courts. He has also wo...

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🔍 researched 3 sources cited
GPT-4o Mini GPT-4o Mini 65% Alejandro Davidovich Fokina

Alejandro Davidovich Fokina has a strong record in 2026, with a 23-15 win-loss record, including a 7-6 record on hard courts. He has also wo...

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🔍 researched 3 sources cited
o4-mini o4-mini Pro
o4-mini o4-mini Pro
Grok 4.3 Grok 4.3 Pro
Grok 4.3 Grok 4.3 Pro
Grok 4 Fast Grok 4 Fast 58% Alejandro Davidovich Fokina

Alejandro Davidovich Fokina holds the edge on outdoor hard courts based on career patterns through 2024 training data, while Juan Manuel Cer...

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Grok 4 Fast Grok 4 Fast 58% Alejandro Davidovich Fokina

Alejandro Davidovich Fokina holds the edge on outdoor hard courts based on career patterns through 2024 training data, while Juan Manuel Cer...

Unlock reasoning · Pro
Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 82% Alejandro Davidovich Fokina

Training data through 2025-09; no live access. Davidovich Fokina is a significantly higher-ranked, hard-court-proven top-30 player while Cer...

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DeepSeek V3 DeepSeek V3 82% Alejandro Davidovich Fokina

Training data through 2025-09; no live access. Davidovich Fokina is a significantly higher-ranked, hard-court-proven top-30 player while Cer...

Unlock reasoning · Pro

Over / Under

Consensus
under 2/8
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.7 Claude Opus 4.7 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.6 Claude Opus 4.6 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Opus 4.8 Claude Opus 4.8 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Sonnet 4.6 Claude Sonnet 4.6 Pro
Claude Haiku 4.5 Claude Haiku 4.5 58% Over 2.5

Both Davidovich Fokina and Cerundolo are baseline grinders with solid returners and break-point opportunities, particularly on hard courts w...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Over 2.5

Both Davidovich Fokina and Cerundolo are baseline grinders with solid returners and break-point opportunities, particularly on hard courts w...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini —
GPT-5 Mini GPT-5 Mini —
GPT-4o Mini GPT-4o Mini 55% over_3.5

Both players have shown competitive performances in recent matches, suggesting a closely contested match. The previous encounter at Wimbledo...

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🔍 researched 3 sources cited
GPT-4o Mini GPT-4o Mini 55% over_3.5

Both players have shown competitive performances in recent matches, suggesting a closely contested match. The previous encounter at Wimbledo...

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

Best-of-three format on a fast hard court in Chengdu typically produces more straight-set outcomes when the higher-ranked player controls ra...

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Grok 4 Fast Grok 4 Fast 62% under

Best-of-three format on a fast hard court in Chengdu typically produces more straight-set outcomes when the higher-ranked player controls ra...

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Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 3.1 Pro Gemini 3.1 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Pro Gemini 2.5 Pro Pro
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 52% under 22.5

Training data through 2025-09; no live access. If Davidovich Fokina is as dominant as the ranking gap suggests, a two-set win should comfort...

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DeepSeek V3 DeepSeek V3 52% under 22.5

Training data through 2025-09; no live access. If Davidovich Fokina is as dominant as the ranking gap suggests, a two-set win should comfort...

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Model confidence

Conviction in pick · Match winner
DeepSeek V3

DeepSeek V3

Alejandro Davidovich Fokina

82
DeepSeek V3

DeepSeek V3

Alejandro Davidovich Fokina

82
GPT-4o Mini

GPT-4o Mini

Alejandro Davidovich Fokina

65
GPT-4o Mini

GPT-4o Mini

Alejandro Davidovich Fokina

65
Claude Haiku 4.5

Claude Haiku 4.5

Alejandro Davidovich Fokina

62
Claude Haiku 4.5

Claude Haiku 4.5

Alejandro Davidovich Fokina

62
Grok 4 Fast

Grok 4 Fast

Alejandro Davidovich Fokina

58
Grok 4 Fast

Grok 4 Fast

Alejandro Davidovich Fokina

58

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-5 290–182 · 61%
+135.0u
GPT-5
GPT-5 290–182 · 61%
+135.0u
Claude Opus 4.8
Claude Opus 4.8 308–230 · 57%
-34.8u
Claude Opus 4.8
Claude Opus 4.8 308–230 · 57%
-34.8u
GPT-4o Mini
GPT-4o Mini 2473–2043 · 55%
-1,650.7u
GPT-4o Mini
GPT-4o Mini 2473–2043 · 55%
-1,650.7u
Gemini 3.1 Pro
Gemini 3.1 Pro 260–224 · 54%
+223.5u
Gemini 3.1 Pro
Gemini 3.1 Pro 260–224 · 54%
+223.5u
o4-mini
o4-mini 180–156 · 54%
+139.4u
o4-mini
o4-mini 180–156 · 54%
+139.4u
GPT-5 Mini
GPT-5 Mini 1494–1296 · 54%
-854.2u
GPT-5 Mini
GPT-5 Mini 1494–1296 · 54%
-854.2u
Claude Opus 4.6
Claude Opus 4.6 386–346 · 53%
-95.2u
Claude Opus 4.6
Claude Opus 4.6 386–346 · 53%
-95.2u
Grok 4.3
Grok 4.3 264–248 · 52%
+187.2u
Grok 4.3
Grok 4.3 264–248 · 52%
+187.2u
Claude Opus 4.7
Claude Opus 4.7 366–352 · 51%
-127.1u
Claude Opus 4.7
Claude Opus 4.7 366–352 · 51%
-127.1u
Claude Sonnet 4.6
Claude Sonnet 4.6 918–964 · 49%
-647.5u
Claude Sonnet 4.6
Claude Sonnet 4.6 918–964 · 49%
-647.5u
Claude Haiku 4.5
Claude Haiku 4.5 2158–2311 · 48%
-1,985.1u
Claude Haiku 4.5
Claude Haiku 4.5 2158–2311 · 48%
-1,985.1u
Grok 4 Fast
Grok 4 Fast 3438–3775 · 48%
-3,291.9u
Grok 4 Fast
Grok 4 Fast 3438–3775 · 48%
-3,291.9u
Gemini 2.5 Flash
Gemini 2.5 Flash 4058–4472 · 48%
-4,101.3u
Gemini 2.5 Flash
Gemini 2.5 Flash 4058–4472 · 48%
-4,101.3u
DeepSeek V3
DeepSeek V3 4128–4766 · 46%
-4,148.9u
DeepSeek V3
DeepSeek V3 4128–4766 · 46%
-4,148.9u
Gemini 2.5 Pro
Gemini 2.5 Pro 302–358 · 46%
-147.7u
Gemini 2.5 Pro
Gemini 2.5 Pro 302–358 · 46%
-147.7u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 3384–4028 · 46%
-3,625.6u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 3384–4028 · 46%
-3,625.6u

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

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Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 1a489dea07234b77…
Kickoff
Sat, Sep 26 · 04:00 GMT+0200
Markets
Match winner · Total sets · Total games
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 3. 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 3, in this order): h2h | totals_sets | totals_games

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": 48459,
        "sport": "tennis",
        "venue": null,
        "league": "Chengdu Open",
        "starts_at": "2026-09-26T04:00:00+02:00",
        "starts_at_human": "Sat, 26 Sep 2026 02:00:00 GMT"
    },
    "teams": {
        "away": "Juan Manuel Cerundolo",
        "home": "Alejandro Davidovich Fokina"
    },
    "version": "v2",
    "sport_focus": [
        "Surface is paramount — weigh each player's record and movement on THIS surface (hard/clay/grass), not their overall ranking.",
        "Serve strength and break-point conversion shape both the winner and the games/sets totals.",
        "Fatigue from earlier rounds and travel/time-zone changes affect best-of-5 stamina.",
        "Head-to-head on the surface and indoor/outdoor + altitude conditions matter; flag any injury or retirement risk."
    ],
    "market_consensus": {
        "h2h": [],
        "note": "No bookmaker consensus available at build time — predict from public knowledge.",
        "extra_markets": []
    },
    "markets_requested": [
        "h2h",
        "totals_sets",
        "totals_games"
    ],
    "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.

  • DeepSeek V3 DeepSeek V3
    0 tool calls · 0 sources

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

  • GPT-4o Mini GPT-4o Mini
    3 tool calls · 3 sources

    3 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.

Results settle automatically once the final score lands. Picks are permanent — no hindsight edits.

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