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Tennis US Open
Final
FM

Felipe Meligeni AlvesvsDalibor Svrcina

DS
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
Felipe Meligeni Alves 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 58% Felipe Meligeni Alves

Both players are ranked outside the top 100 and have limited recent ATP-level exposure; this appears to be a qualifying or early-round US Op...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Felipe Meligeni Alves

Both players are ranked outside the top 100 and have limited recent ATP-level exposure; this appears to be a qualifying or early-round US Op...

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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 56% Felipe Meligeni Alves

Meligeni Alves holds a slight edge on hard courts from prior seasons and benefits from home-nation support at the US Open. Svrcina has shown...

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Grok 4 Fast Grok 4 Fast 56% Felipe Meligeni Alves

Meligeni Alves holds a slight edge on hard courts from prior seasons and benefits from home-nation support at the US Open. Svrcina has shown...

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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 53% Felipe Meligeni Alves

Based on my training data up to my last update, both players are primarily clay-court specialists who also compete on hard courts. Without l...

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Gemini 2.5 Flash Gemini 2.5 Flash 53% Felipe Meligeni Alves

Based on my training data up to my last update, both players are primarily clay-court specialists who also compete on hard courts. Without l...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Felipe Meligeni Alves

Felipe Meligeni Alves has a slightly better recent performance and a higher career-high ranking than Dalibor Svrcina. While both players are...

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3 sources cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 60% Felipe Meligeni Alves

Felipe Meligeni Alves has a slightly better recent performance and a higher career-high ranking than Dalibor Svrcina. While both players are...

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3 sources cited
DeepSeek V3 DeepSeek V3 55% Felipe Meligeni Alves

Training data through 2025-09. Both players are lower-ranked qualifiers; Meligeni Alves has more experience on hard courts and a slightly hi...

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DeepSeek V3 DeepSeek V3 55% Felipe Meligeni Alves

Training data through 2025-09. Both players are lower-ranked qualifiers; Meligeni Alves has more experience on hard courts and a slightly hi...

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Over / Under

Consensus
over 6/20
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 62% Over 2.5

Both players are relatively evenly matched in serve stability and break-point conversion at this level, reducing the likelihood of a dominan...

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Claude Haiku 4.5 Claude Haiku 4.5 62% Over 2.5

Both players are relatively evenly matched in serve stability and break-point conversion at this level, reducing the likelihood of a dominan...

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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 61% under

Both players lack elite serve dominance so expect service breaks but still a competitive three-set match rather than a blowout. Qualifying-l...

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Grok 4 Fast Grok 4 Fast 61% under

Both players lack elite serve dominance so expect service breaks but still a competitive three-set match rather than a blowout. Qualifying-l...

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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 3.5

With both players being closely matched based on available historical data and neither having a significant hard-court advantage, a competit...

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Gemini 2.5 Flash Gemini 2.5 Flash 60% Over 3.5

With both players being closely matched based on available historical data and neither having a significant hard-court advantage, a competit...

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Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 55% over

This is projected to be a closely contested match between two players of similar standing. Given their relatively even head-to-head potentia...

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3 sources cited
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite 55% over

This is projected to be a closely contested match between two players of similar standing. Given their relatively even head-to-head potentia...

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3 sources cited
DeepSeek V3 DeepSeek V3 55% Over 3.5

This is a first-round match between two qualifiers with similar skill levels, likely to be competitive and extended. Both players have shown...

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DeepSeek V3 DeepSeek V3 55% Over 3.5

This is a first-round match between two qualifiers with similar skill levels, likely to be competitive and extended. Both players have shown...

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Model confidence

Conviction in pick · Match winner
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Felipe Meligeni Alves

60
Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite

Felipe Meligeni Alves

60
Claude Haiku 4.5

Claude Haiku 4.5

Felipe Meligeni Alves

58
Claude Haiku 4.5

Claude Haiku 4.5

Felipe Meligeni Alves

58
Grok 4 Fast

Grok 4 Fast

Felipe Meligeni Alves

56
Grok 4 Fast

Grok 4 Fast

Felipe Meligeni Alves

56
DeepSeek V3

DeepSeek V3

Felipe Meligeni Alves

55
DeepSeek V3

DeepSeek V3

Felipe Meligeni Alves

55
Gemini 2.5 Flash

Gemini 2.5 Flash

Felipe Meligeni Alves

53
Gemini 2.5 Flash

Gemini 2.5 Flash

Felipe Meligeni Alves

53

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 3586–3931 · 48%
-3,447.9u
Grok 4 Fast
Grok 4 Fast 3586–3931 · 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 4283–4944 · 46%
-4,326.9u
DeepSeek V3
DeepSeek V3 4283–4944 · 46%
-4,326.9u
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: ed2700ce0fb6dc8b…
Kickoff
Mon, Aug 24 · 21:55 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": 30766,
        "sport": "tennis",
        "venue": null,
        "league": "US Open",
        "starts_at": "2026-08-24T19:30:00+00:00",
        "starts_at_human": "Mon, 24 Aug 2026 19:30:00 GMT"
    },
    "teams": {
        "away": "Dalibor Svrcina",
        "home": "Felipe Meligeni Alves"
    },
    "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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