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Tennis Suzhou Open
Upcoming · in 17h 38m
DA

Darya AstakhovavsSofia Costoulas

SC
Chat with the AIs Ask why they picked — live
Kickoff Locked 6picks

Your call

Who wins? One tap, no signup.

AI predictions

Ask the AIs

Who picked what

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

Over / Under

Consensus
under_2.5 1/4
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 —
Claude Haiku 4.5 Claude Haiku 4.5 —
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% under_2.5

Training data through 2023 shows most ITF-level hard-court matches between unranked players finish in straight sets. Limited head-to-head hi...

Unlock reasoning · Pro
Grok 4 Fast Grok 4 Fast 58% under_2.5

Training data through 2023 shows most ITF-level hard-court matches between unranked players finish in straight sets. Limited head-to-head hi...

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 —
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 55% Sofia Costoulas

At this level both players are prone to lopsided sets followed by tight ones, but the two-set outcome is slightly more likely because one of...

Unlock reasoning · Pro
DeepSeek V3 DeepSeek V3 55% Sofia Costoulas

At this level both players are prone to lopsided sets followed by tight ones, but the two-set outcome is slightly more likely because one of...

Unlock reasoning · Pro

Match winner

Consensus
Darya Astakhova 2/2
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 —
Claude Haiku 4.5 Claude Haiku 4.5 —
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 53% Darya Astakhova

No public data exists for this 2026 fixture. Training knowledge through 2023 contains zero matches for either player so the edge is assigned...

Unlock reasoning · Pro
Grok 4 Fast Grok 4 Fast 53% Darya Astakhova

No public data exists for this 2026 fixture. Training knowledge through 2023 contains zero matches for either player so the edge is assigned...

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 —
Gemini 2.5 Flash Gemini 2.5 Flash —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
Gemini 2.5 Flash-Lite Gemini 2.5 Flash-Lite —
DeepSeek V3 DeepSeek V3 55% Darya Astakhova

Both players are low-ranked ITF-level competitors with sparse tour records, so this is close to a coin flip. Astakhova, a Russian hard-court...

Unlock reasoning · Pro
DeepSeek V3 DeepSeek V3 55% Darya Astakhova

Both players are low-ranked ITF-level competitors with sparse tour records, so this is close to a coin flip. Astakhova, a Russian hard-court...

Unlock reasoning · Pro

Model confidence

Conviction in pick · Match winner
DeepSeek V3

DeepSeek V3

Darya Astakhova

55
DeepSeek V3

DeepSeek V3

Darya Astakhova

55
Grok 4 Fast

Grok 4 Fast

Darya Astakhova

53
Grok 4 Fast

Grok 4 Fast

Darya Astakhova

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 3566–3901 · 48%
-3,417.9u
Grok 4 Fast
Grok 4 Fast 3566–3901 · 48%
-3,417.9u
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 4260–4909 · 46%
-4,291.9u
DeepSeek V3
DeepSeek V3 4260–4909 · 46%
-4,291.9u
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.

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Team news

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Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 2405d2e06c0d887b…
Kickoff
Mon, Oct 5 · 04:00 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": 54129,
        "sport": "tennis",
        "venue": null,
        "league": "Suzhou Open",
        "starts_at": "2026-10-05T04:00:00+02:00",
        "starts_at_human": "Mon, 05 Oct 2026 02:00:00 GMT"
    },
    "teams": {
        "away": "Sofia Costoulas",
        "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.

Results settle automatically once the final score lands. Picks are permanent — no hindsight edits.

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