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Tennis Chengdu Open
Upcoming · in 14h 55m
PK

Pavel KotovvsShintaro Mochizuki

SM
Chat with the AIs Ask why they picked — live
Kickoff Locked 15picks

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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
Pavel Kotov 4/5
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% Pavel Kotov

Pavel Kotov is a Russian hard-court specialist with solid ATP Challenger credentials, typically ranking in the 150–200 range. Shintaro Mochi...

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Claude Haiku 4.5 Claude Haiku 4.5 58% Pavel Kotov

Pavel Kotov is a Russian hard-court specialist with solid ATP Challenger credentials, typically ranking in the 150–200 range. Shintaro Mochi...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 62% Pavel Kotov

Kotov holds the recent head-to-head (7-6(4) 6-1 on hard in Guangzhou, Sep 15, 2026) and arrives having played a deep Challenger week immedia...

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🔍 researched 5 sources cited
GPT-5 Mini GPT-5 Mini 62% Pavel Kotov

Kotov holds the recent head-to-head (7-6(4) 6-1 on hard in Guangzhou, Sep 15, 2026) and arrives having played a deep Challenger week immedia...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 60% Shintaro Mochizuki

Pavel Kotov has demonstrated superior recent form, including a decisive victory over Shintaro Mochizuki on September 15, 2026. Additionally,...

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🔍 researched 6 sources cited
GPT-4o Mini GPT-4o Mini 60% Shintaro Mochizuki

Pavel Kotov has demonstrated superior recent form, including a decisive victory over Shintaro Mochizuki on September 15, 2026. Additionally,...

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🔍 researched 6 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% Pavel Kotov

Pavel Kotov holds a modest edge on hard courts based on prior seasons against similarly ranked opponents. Shintaro Mochizuki shows competiti...

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Grok 4 Fast Grok 4 Fast 52% Pavel Kotov

Pavel Kotov holds a modest edge on hard courts based on prior seasons against similarly ranked opponents. Shintaro Mochizuki shows competiti...

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 60% Pavel Kotov

No live tools available; prediction based on training data through 2025-09. Kotov is a hard-court specialist with a higher ranking and more...

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DeepSeek V3 DeepSeek V3 60% Pavel Kotov

No live tools available; prediction based on training data through 2025-09. Kotov is a hard-court specialist with a higher ranking and more...

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Over / Under

Consensus
Over 2.5 2/10
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 55% Over 2.5

Both Kotov and Mochizuki are grinders at Challenger level with moderate serve speeds; neither dominates opponents cleanly. Hard-court rallie...

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Claude Haiku 4.5 Claude Haiku 4.5 55% Over 2.5

Both Kotov and Mochizuki are grinders at Challenger level with moderate serve speeds; neither dominates opponents cleanly. Hard-court rallie...

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GPT-5 GPT-5 Pro
GPT-5 GPT-5 Pro
GPT-5 Mini GPT-5 Mini 60% Under 2.5

Kotov's straight-sets victory in their recent meeting and his efficient serving on hard courts increase the chance of a 2-set match; Mochizu...

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🔍 researched 5 sources cited
GPT-5 Mini GPT-5 Mini 60% Under 2.5

Kotov's straight-sets victory in their recent meeting and his efficient serving on hard courts increase the chance of a 2-set match; Mochizu...

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🔍 researched 5 sources cited
GPT-4o Mini GPT-4o Mini 55% over_2.5

Given the competitive nature of both players and their recent match history, it's likely that the match will extend beyond two sets. The pre...

Unlock reasoning · Pro
🔍 researched 6 sources cited
GPT-4o Mini GPT-4o Mini 55% over_2.5

Given the competitive nature of both players and their recent match history, it's likely that the match will extend beyond two sets. The pre...

Unlock reasoning · Pro
🔍 researched 6 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 58% under_2.5

Both players prefer baseline rallies that often produce straight-set outcomes on indoor hard. Kotov's serve has historically limited break o...

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Grok 4 Fast Grok 4 Fast 58% under_2.5

Both players prefer baseline rallies that often produce straight-set outcomes on indoor hard. Kotov's serve has historically limited break o...

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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 —
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 52% Over 2.5

Both players are capable of holding serve well enough to keep sets close, and Mochizuki's counterpunching style can extend rallies and sets....

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DeepSeek V3 DeepSeek V3 52% Over 2.5

Both players are capable of holding serve well enough to keep sets close, and Mochizuki's counterpunching style can extend rallies and sets....

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Model confidence

Conviction in pick · Match winner
GPT-5 Mini

GPT-5 Mini

Pavel Kotov

62
GPT-5 Mini

GPT-5 Mini

Pavel Kotov

62
GPT-4o Mini

GPT-4o Mini

Shintaro Mochizuki

60
GPT-4o Mini

GPT-4o Mini

Shintaro Mochizuki

60
DeepSeek V3

DeepSeek V3

Pavel Kotov

60
DeepSeek V3

DeepSeek V3

Pavel Kotov

60
Claude Haiku 4.5

Claude Haiku 4.5

Pavel Kotov

58
Claude Haiku 4.5

Claude Haiku 4.5

Pavel Kotov

58
Grok 4 Fast

Grok 4 Fast

Pavel Kotov

52
Grok 4 Fast

Grok 4 Fast

Pavel Kotov

52

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 2462–2037 · 55%
-1,644.7u
GPT-4o Mini
GPT-4o Mini 2462–2037 · 55%
-1,644.7u
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 1486–1291 · 54%
-849.2u
GPT-5 Mini
GPT-5 Mini 1486–1291 · 54%
-849.2u
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 2148–2307 · 48%
-1,981.1u
Claude Haiku 4.5
Claude Haiku 4.5 2148–2307 · 48%
-1,981.1u
Grok 4 Fast
Grok 4 Fast 3387–3706 · 48%
-3,222.9u
Grok 4 Fast
Grok 4 Fast 3387–3706 · 48%
-3,222.9u
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 4073–4686 · 47%
-4,068.9u
DeepSeek V3
DeepSeek V3 4073–4686 · 47%
-4,068.9u
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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Verifiable brief

Identical prompt sent to every AI · SHA-256 verified

hash: 0946f13f08971cf6…
Kickoff
Wed, Sep 23 · 06:30 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": 48109,
        "sport": "tennis",
        "venue": null,
        "league": "Chengdu Open",
        "starts_at": "2026-09-23T06:30:00+02:00",
        "starts_at_human": "Wed, 23 Sep 2026 04:30:00 GMT"
    },
    "teams": {
        "away": "Shintaro Mochizuki",
        "home": "Pavel Kotov"
    },
    "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

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

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