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Tennis Korea Open
Upcoming · in 9h 49m
DA

Darya AstakhovavsAlexandra Shubladze

AS
Chat with the AIs Ask why they picked — live
Kickoff Locked 20picks

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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
Darya Astakhova 6/6
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% Darya Astakhova

Both players are lower-ranked professionals competing in a WTA 250 event. Astakhova, playing at home (Korea Open is typically held in Seoul)...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Darya Astakhova

Both players are lower-ranked professionals competing in a WTA 250 event. Astakhova, playing at home (Korea Open is typically held in Seoul)...

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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 75% Darya Astakhova

Darya Astakhova has a higher career-high singles ranking of No. 181, achieved on 20 March 2023, compared to Alexandra Shubladze's No. 1056 o...

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🔍 researched 3 sources cited
GPT-4o Mini GPT-4o Mini 75% Darya Astakhova

Darya Astakhova has a higher career-high singles ranking of No. 181, achieved on 20 March 2023, compared to Alexandra Shubladze's No. 1056 o...

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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 55% Darya Astakhova

Both players are low-ranked journeymen with limited public profiles; home designation gives Astakhova the slight edge in a neutral-surface K...

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Grok 4 Fast Grok 4 Fast 55% Darya Astakhova

Both players are low-ranked journeymen with limited public profiles; home designation gives Astakhova the slight edge in a neutral-surface K...

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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 68% Darya Astakhova

Based on historical performance data available through my training, Darya Astakhova holds a higher career-best ranking and possesses more ex...

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Gemini 2.5 Flash Gemini 2.5 Flash 68% Darya Astakhova

Based on historical performance data available through my training, Darya Astakhova holds a higher career-best ranking and possesses more ex...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Darya Astakhova

Based on training data, Darya Astakhova is generally a stronger player than Alexandra Shubladze. Astakhova's consistent performance and high...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Darya Astakhova

Based on training data, Darya Astakhova is generally a stronger player than Alexandra Shubladze. Astakhova's consistent performance and high...

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DeepSeek V3 DeepSeek V3 58% Darya Astakhova

No live access; predicted from training data through 2025-09. Astakhova is the more established WTA-level hard-court player with a heavier f...

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DeepSeek V3 DeepSeek V3 58% Darya Astakhova

No live access; predicted from training data through 2025-09. Astakhova is the more established WTA-level hard-court player with a heavier f...

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Over / Under

Consensus
Over 2.5 2/14
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 55% Over 2.5

Both Astakhova and Shubladze are baseline-oriented players without dominant serves, historically making their matches competitive and extend...

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Claude Haiku 4.5 Claude Haiku 4.5 55% Over 2.5

Both Astakhova and Shubladze are baseline-oriented players without dominant serves, historically making their matches competitive and extend...

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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% over_2.5

Given the competitive nature of the match, it's likely to extend beyond two sets. Both players have shown resilience in their recent matches...

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🔍 researched 3 sources cited
GPT-4o Mini GPT-4o Mini 65% over_2.5

Given the competitive nature of the match, it's likely to extend beyond two sets. Both players have shown resilience in their recent matches...

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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 52% under_2.5

Lower-level ITF/WTA 125 matches on hard courts frequently finish in straight sets when one player is slightly more consistent. Limited stami...

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Grok 4 Fast Grok 4 Fast 52% under_2.5

Lower-level ITF/WTA 125 matches on hard courts frequently finish in straight sets when one player is slightly more consistent. Limited stami...

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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 60% Over 2.5

Given the perceived similar level of Darya Astakhova and Alexandra Shubladze from available training data, a close match extending to three...

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Gemini 2.5 Flash Gemini 2.5 Flash 60% Over 2.5

Given the perceived similar level of Darya Astakhova and Alexandra Shubladze from available training data, a close match extending to three...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 70% Alexandra Shubladze

Given Astakhova's favored status and Shubladze's potential to struggle, it's likely Astakhova will win in straight sets. While Shubladze cou...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 70% Alexandra Shubladze

Given Astakhova's favored status and Shubladze's potential to struggle, it's likely Astakhova will win in straight sets. While Shubladze cou...

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DeepSeek V3 DeepSeek V3 62% Under 2.5

Training-data-based view: Astakhova's serve and first-strike advantage should let her control most service games, reducing the likelihood of...

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DeepSeek V3 DeepSeek V3 62% Under 2.5

Training-data-based view: Astakhova's serve and first-strike advantage should let her control most service games, reducing the likelihood of...

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Model confidence

Conviction in pick · Match winner
GPT-4o Mini

GPT-4o Mini

Darya Astakhova

75
GPT-4o Mini

GPT-4o Mini

Darya Astakhova

75
Gemini 2.5 Flash

Gemini 2.5 Flash

Darya Astakhova

68
Gemini 2.5 Flash

Gemini 2.5 Flash

Darya Astakhova

68
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Darya Astakhova

65
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Darya Astakhova

65
Claude Haiku 4.5

Claude Haiku 4.5

Darya Astakhova

58
Claude Haiku 4.5

Claude Haiku 4.5

Darya Astakhova

58
DeepSeek V3

DeepSeek V3

Darya Astakhova

58
DeepSeek V3

DeepSeek V3

Darya Astakhova

58
Grok 4 Fast

Grok 4 Fast

Darya Astakhova

55
Grok 4 Fast

Grok 4 Fast

Darya Astakhova

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-5 290–182 · 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 2446–1994 · 55%
-1,601.7u
GPT-4o Mini
GPT-4o Mini 2446–1994 · 55%
-1,601.7u
GPT-5 Mini
GPT-5 Mini 1474–1258 · 54%
-816.2u
GPT-5 Mini
GPT-5 Mini 1474–1258 · 54%
-816.2u
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
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 2142–2300 · 48%
-1,974.1u
Claude Haiku 4.5
Claude Haiku 4.5 2142–2300 · 48%
-1,974.1u
Grok 4 Fast
Grok 4 Fast 3332–3618 · 48%
-3,134.9u
Grok 4 Fast
Grok 4 Fast 3332–3618 · 48%
-3,134.9u
Gemini 2.5 Flash
Gemini 2.5 Flash 4052–4448 · 48%
-4,077.3u
Gemini 2.5 Flash
Gemini 2.5 Flash 4052–4448 · 48%
-4,077.3u
DeepSeek V3
DeepSeek V3 4014–4588 · 47%
-3,970.9u
DeepSeek V3
DeepSeek V3 4014–4588 · 47%
-3,970.9u
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 3376–4008 · 46%
-3,605.6u
Gemini 2.5 Flash-Lite
Gemini 2.5 Flash-Lite 3376–4008 · 46%
-3,605.6u

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: ca3994d215286261…
Kickoff
Sun, Sep 20 · 02:00 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": 46439,
        "sport": "tennis",
        "venue": null,
        "league": "Korea Open",
        "starts_at": "2026-09-20T02:00:00+02:00",
        "starts_at_human": "Sun, 20 Sep 2026 00:00:00 GMT"
    },
    "teams": {
        "away": "Alexandra Shubladze",
        "home": "Darya Astakhova"
    },
    "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
  • 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.

  • 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
    3 tool calls · 3 sources

    3 citations captured — unlock with Pro

  • Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite
    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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