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Tennis US Open
Final
MY

Ma YeXinvsClara Burel

CB
Chat with the AIs Ask why they picked — live
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
Clara Burel 6/10
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% Ma YeXin

Ma YeXin is a Chinese player with a strong hard-court game and solid ranking trajectory as of my training knowledge (through September 2025)...

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Claude Haiku 4.5 Claude Haiku 4.5 62% Ma YeXin

Ma YeXin is a Chinese player with a strong hard-court game and solid ranking trajectory as of my training knowledge (through September 2025)...

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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 58% Clara Burel

Training data through 2025-09 shows Clara Burel with stronger hard-court results and recent form against similar opposition compared to Ma Y...

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Grok 4 Fast Grok 4 Fast 58% Clara Burel

Training data through 2025-09 shows Clara Burel with stronger hard-court results and recent form against similar opposition compared to Ma Y...

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 75% Clara Burel

Based on training data through September 2025, Clara Burel is a significantly more experienced and higher-ranked player compared to Ma YeXin...

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Gemini 2.5 Flash Gemini 2.5 Flash 75% Clara Burel

Based on training data through September 2025, Clara Burel is a significantly more experienced and higher-ranked player compared to Ma YeXin...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Clara Burel

Based on general knowledge of tennis players, Clara Burel is a more established player with a higher career ranking and more significant win...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 65% Clara Burel

Based on general knowledge of tennis players, Clara Burel is a more established player with a higher career ranking and more significant win...

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DeepSeek V3 DeepSeek V3 55% Ma YeXin

Training data through 2025-09: Ma YeXin has shown strong form on hard courts, with a solid serve and aggressive baseline play. Clara Burel i...

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DeepSeek V3 DeepSeek V3 55% Ma YeXin

Training data through 2025-09: Ma YeXin has shown strong form on hard courts, with a solid serve and aggressive baseline play. Clara Burel i...

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Over / Under

Consensus
over_2.5 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 2.5

Both players are mid-to-upper tier professionals capable of extended rallies and break-point contests on hard courts. Ma YeXin's consistent...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Over 2.5

Both players are mid-to-upper tier professionals capable of extended rallies and break-point contests on hard courts. Ma YeXin's consistent...

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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 52% over_2.5

Training data through 2025-09 indicates both players often push matches to three sets on hard courts when evenly matched. Burel's experience...

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Grok 4 Fast Grok 4 Fast 52% over_2.5

Training data through 2025-09 indicates both players often push matches to three sets on hard courts when evenly matched. Burel's experience...

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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 Clara Burel's significant edge in experience and ranking on hard courts, a dominant performance leading to a straight-sets victory (2-...

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Gemini 2.5 Flash Gemini 2.5 Flash 65% Under 2.5 Sets

Given Clara Burel's significant edge in experience and ranking on hard courts, a dominant performance leading to a straight-sets victory (2-...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Clara Burel

Given Clara Burel is the favored player and this is likely a best-of-three sets match at the US Open, Burel is expected to win in two sets....

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Clara Burel

Given Clara Burel is the favored player and this is likely a best-of-three sets match at the US Open, Burel is expected to win in two sets....

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DeepSeek V3 DeepSeek V3 55% Under 2.5

Both players have shown tendency to win matches in straight sets in recent tournaments, with Ma YeXin's aggressive play and Burel's occasion...

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DeepSeek V3 DeepSeek V3 55% Under 2.5

Both players have shown tendency to win matches in straight sets in recent tournaments, with Ma YeXin's aggressive play and Burel's occasion...

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Model confidence

Conviction in pick · Match winner
Gemini 2.5 Flash

Gemini 2.5 Flash

Clara Burel

75
Gemini 2.5 Flash

Gemini 2.5 Flash

Clara Burel

75
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Clara Burel

65
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Clara Burel

65
Claude Haiku 4.5

Claude Haiku 4.5

Ma YeXin

62
Claude Haiku 4.5

Claude Haiku 4.5

Ma YeXin

62
Grok 4 Fast

Grok 4 Fast

Clara Burel

58
Grok 4 Fast

Grok 4 Fast

Clara Burel

58
DeepSeek V3

DeepSeek V3

Ma YeXin

55
DeepSeek V3

DeepSeek V3

Ma YeXin

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 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 3592–3931 · 48%
-3,447.9u
Grok 4 Fast
Grok 4 Fast 3592–3931 · 48%
-3,447.9u
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 4283–4952 · 46%
-4,334.9u
DeepSeek V3
DeepSeek V3 4283–4952 · 46%
-4,334.9u
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: d2968b546ba6b257…
Kickoff
Mon, Aug 24 · 17:05 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": 30807,
        "sport": "tennis",
        "venue": null,
        "league": "US Open",
        "starts_at": "2026-08-24T15:00:00+00:00",
        "starts_at_human": "Mon, 24 Aug 2026 15:00:00 GMT"
    },
    "teams": {
        "away": "Clara Burel",
        "home": "Ma YeXin"
    },
    "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.

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