Nikola BartunkovavsAntonia Ruzic
ARYour call
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AI predictions
2 markets · 5 models
Ask the AIsWho picked what
16 models × 2 markets · click a row to see reasoning
| Model |
Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
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Over / Under
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Over / Under
Over / Under total points. Will the combined score be above or below the line.
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| Consensus |
Nikola Bartunkova 5/5 models |
Over 2/10 models |
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Claude Opus 4.7 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Opus 4.6 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Opus 4.8 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Sonnet 4.6 FlagshipAnthropic |
Flagship picks across 2 markets — unlock with Pro
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Claude Haiku 4.5 Anthropic |
62%
Nikola Bartunkova |
58%
Over 1.5 |
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
62%
Nikola Bartunkova Nikola Bartunkova is a Czech professional who has competed on the WTA circuit with solid performances on hard courts, which is the typical s...
Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
58%
Over 1.5 A straight-sets victory (2–0) would require the favorite to dominate decisively. Absent live form data, competitive women's tennis matches a... |
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GPT-5 FlagshipOpenai |
Flagship picks across 2 markets — unlock with Pro
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GPT-5 Mini Openai |
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
No pick on this market.
Over / Under
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Over / Under
Over / Under total points. Will the combined score be above or below the line.
No pick on this market. |
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GPT-4o Mini Openai |
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
No pick on this market.
Over / Under
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Over / Under
Over / Under total points. Will the combined score be above or below the line.
No pick on this market. |
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o4-mini FlagshipOpenai |
Flagship picks across 2 markets — unlock with Pro
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Grok 4.3 FlagshipXai |
Flagship picks across 2 markets — unlock with Pro
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Grok 4 Fast Xai |
52%
Nikola Bartunkova |
58%
under_2.5 |
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
52%
Nikola Bartunkova Training data through 2025-09 contains no head-to-head or recent form for this future 2026 encounter. Hard-court surface in Mexico favors th...
Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
58%
under_2.5 Training data through 2025-09 shows lower-ranked WTA matches on hard often finish in straight sets. Limited player profiles suggest inconsis... |
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Gemini 3.1 Pro Flagship |
Flagship picks across 2 markets — unlock with Pro
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Gemini 2.5 Pro Flagship |
Flagship picks across 2 markets — unlock with Pro
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Gemini 2.5 Flash |
52%
Nikola Bartunkova |
60%
over_2.5_sets |
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Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
52%
Nikola Bartunkova Based on my training data up to my last update, both Nikola Bartunkova and Antonia Ruzic are young, developing players on the WTA circuit. B...
Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
60%
over_2.5_sets Given the lack of current data for this future match, and relying on general trends for developing players in my training data, a competitiv... |
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Gemini 2.5 Flash-Lite |
57%
Nikola Bartunkova |
55%
Over |
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Match winner
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Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
57%
Nikola Bartunkova Nikola Bartunkova is the slight favorite based on general training data. While specific recent form and head-to-head records are unavailable...
Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
55%
Over Given the uncertainty in predicting a clear winner and the potential for a competitive match between players of similar standing, it's proba... |
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DeepSeek V3 Deepseek |
60%
Nikola Bartunkova |
55%
over_2.5 |
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Match winner
?
Match winner
Match winner. Pick the team that wins in regular time (or who advances in cup formats).
60%
Nikola Bartunkova Training data through 2025-09: both players are young with limited WTA experience, but Bartunkova has shown stronger results on hard courts...
Over / Under
?
Over / Under
Over / Under total points. Will the combined score be above or below the line.
55%
over_2.5 Matches between players of similar lower-tier rankings often extend to three sets, especially in hard-court conditions where breaks are freq... |
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Match winner
ConsensusNikola Bartunkova 5/5
Nikola Bartunkova is a Czech professional who has competed on the WTA circuit with solid performances on hard courts, which is the typical s...
Training data through 2025-09 contains no head-to-head or recent form for this future 2026 encounter. Hard-court surface in Mexico favors th...
Based on my training data up to my last update, both Nikola Bartunkova and Antonia Ruzic are young, developing players on the WTA circuit. B...
Nikola Bartunkova is the slight favorite based on general training data. While specific recent form and head-to-head records are unavailable...
Training data through 2025-09: both players are young with limited WTA experience, but Bartunkova has shown stronger results on hard courts...
Over / Under
ConsensusOver 2/10
A straight-sets victory (2–0) would require the favorite to dominate decisively. Absent live form data, competitive women's tennis matches a...
Training data through 2025-09 shows lower-ranked WTA matches on hard often finish in straight sets. Limited player profiles suggest inconsis...
Given the lack of current data for this future match, and relying on general trends for developing players in my training data, a competitiv...
Given the uncertainty in predicting a clear winner and the potential for a competitive match between players of similar standing, it's proba...
Matches between players of similar lower-tier rankings often extend to three sets, especially in hard-court conditions where breaks are freq...
Model confidence
Conviction in pick · Match winnerClaude Haiku 4.5
Nikola Bartunkova
DeepSeek V3
Nikola Bartunkova
Gemini 2.5 Flash-Lite
Nikola Bartunkova
Grok 4 Fast
Nikola Bartunkova
Gemini 2.5 Flash
Nikola Bartunkova
Model track records
LifetimeEvery graded pick across all sports — auto-settled the moment results land, wins and losses both counted. Ranked by win rate.
Units = net profit at flat 1-unit stakes. The full sortable board lives on the leaderboard.
Refresh the read
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Confirmed XIs, late injuries and line moves land in the final hour. Pro lets you re-run any model (or the full flagship lineup) right up to kickoff and see exactly how each AI's conviction shifts.
Verifiable brief
Identical prompt sent to every AI · SHA-256 verified
hash:
2c1536cb0ae3ca4b…
- Kickoff
- Mon, Aug 24 · 01:00 GMT+0000
- 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": 30550,
"sport": "tennis",
"venue": null,
"league": "Abierto GNP Seguros",
"starts_at": "2026-08-24T01:00:00+00:00",
"starts_at_human": "Mon, 24 Aug 2026 01:00:00 GMT"
},
"teams": {
"away": "Antonia Ruzic",
"home": "Nikola Bartunkova"
},
"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.
Results settle automatically once the final score lands. Picks are permanent — no hindsight edits.
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