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Tennis Abierto GNP Seguros
Final
MT

Maria TimofeevavsLanlana Tararudee

LT
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Kickoff Locked 30picks

AI predictions

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Who picked what

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

Match winner

Consensus
Maria Timofeeva 10/10
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% Maria Timofeeva

Maria Timofeeva is a higher-ranked Russian player with more consistent WTA-level experience and a stronger baseline game. Lanlana Tararudee...

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Claude Haiku 4.5 Claude Haiku 4.5 62% Maria Timofeeva

Maria Timofeeva is a higher-ranked Russian player with more consistent WTA-level experience and a stronger baseline game. Lanlana Tararudee...

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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 —
GPT-4o Mini GPT-4o Mini —
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% Maria Timofeeva

Maria Timofeeva holds a higher ranking and better overall results on hard courts based on pre-2025 data. Lanlana Tararudee has shown inconsi...

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Grok 4 Fast Grok 4 Fast 62% Maria Timofeeva

Maria Timofeeva holds a higher ranking and better overall results on hard courts based on pre-2025 data. Lanlana Tararudee has shown inconsi...

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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 75% Maria Timofeeva

Maria Timofeeva, based on her career trajectory and higher ranking prior to my knowledge cut-off, is the stronger and more experienced playe...

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1 source cited
Gemini 2.5 Flash Gemini 2.5 Flash 75% Maria Timofeeva

Maria Timofeeva, based on her career trajectory and higher ranking prior to my knowledge cut-off, is the stronger and more experienced playe...

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1 source cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Maria Timofeeva

Maria Timofeeva is a more established player with a higher ranking and a better record on hard courts, suggesting she has the edge in this m...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Maria Timofeeva

Maria Timofeeva is a more established player with a higher ranking and a better record on hard courts, suggesting she has the edge in this m...

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DeepSeek V3 DeepSeek V3 58% Maria Timofeeva

Training data through mid-2025 indicates Timofeeva holds a higher peak level on hard courts and has showed greater resilience in tight match...

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DeepSeek V3 DeepSeek V3 58% Maria Timofeeva

Training data through mid-2025 indicates Timofeeva holds a higher peak level on hard courts and has showed greater resilience in tight match...

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Over / Under

Consensus
Over 1.5 2/20
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 1.5

The match is best-of-three sets (standard WTA format). A straight-sets win (2–0) requires one player to dominate significantly; given no maj...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Over 1.5

The match is best-of-three sets (standard WTA format). A straight-sets win (2–0) requires one player to dominate significantly; given no maj...

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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 —
GPT-4o Mini GPT-4o Mini —
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 55% over_2.5

Both players tend to drop sets in early rounds against similar opposition. Hard court rallies favor longer matches. Training data through 20...

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Grok 4 Fast Grok 4 Fast 55% over_2.5

Both players tend to drop sets in early rounds against similar opposition. Hard court rallies favor longer matches. Training data through 20...

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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 65% Under 2.5 sets

Given Maria Timofeeva's expected dominance, a straight-sets victory (2-0) is the most probable outcome for this match. Her superior experien...

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1 source cited
Gemini 2.5 Flash Gemini 2.5 Flash 65% Under 2.5 sets

Given Maria Timofeeva's expected dominance, a straight-sets victory (2-0) is the most probable outcome for this match. Her superior experien...

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1 source cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Lanlana Tararudee

Given that Timofeeva is favored, a two-set victory for her is the most probable outcome. While Tararudee might win a set on a good day, Timo...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Lanlana Tararudee

Given that Timofeeva is favored, a two-set victory for her is the most probable outcome. While Tararudee might win a set on a good day, Timo...

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DeepSeek V3 DeepSeek V3 55% Over 2.5

Both players are of similar competitive level and tend to split sets in matches without a clear favorite, making a three-set contest plausib...

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DeepSeek V3 DeepSeek V3 55% Over 2.5

Both players are of similar competitive level and tend to split sets in matches without a clear favorite, making a three-set contest plausib...

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Model confidence

Conviction in pick · Match winner
Gemini 2.5 Flash

Gemini 2.5 Flash

Maria Timofeeva

75
Gemini 2.5 Flash

Gemini 2.5 Flash

Maria Timofeeva

75
Claude Haiku 4.5

Claude Haiku 4.5

Maria Timofeeva

62
Claude Haiku 4.5

Claude Haiku 4.5

Maria Timofeeva

62
Grok 4 Fast

Grok 4 Fast

Maria Timofeeva

62
Grok 4 Fast

Grok 4 Fast

Maria Timofeeva

62
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Maria Timofeeva

60
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Maria Timofeeva

60
DeepSeek V3

DeepSeek V3

Maria Timofeeva

58
DeepSeek V3

DeepSeek V3

Maria Timofeeva

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 2202–2352 · 48%
-2,026.1u
Claude Haiku 4.5
Claude Haiku 4.5 2202–2352 · 48%
-2,026.1u
Grok 4 Fast
Grok 4 Fast 3583–3914 · 48%
-3,430.9u
Grok 4 Fast
Grok 4 Fast 3583–3914 · 48%
-3,430.9u
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 4278–4926 · 46%
-4,308.9u
DeepSeek V3
DeepSeek V3 4278–4926 · 46%
-4,308.9u
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.

Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: fb8c55a73b7ee306…
Kickoff
Sat, Aug 22 · 23:35 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": 29774,
        "sport": "tennis",
        "venue": null,
        "league": "Abierto GNP Seguros",
        "starts_at": "2026-08-22T20:30:00+00:00",
        "starts_at_human": "Sat, 22 Aug 2026 20:30:00 GMT"
    },
    "teams": {
        "away": "Lanlana Tararudee",
        "home": "Maria Timofeeva"
    },
    "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
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