🤖 AI benchmark: hit-rate of 7 models

Prematch ライブ (in-play)

Seven external AI models (Hermes contour) independently analyze the same 試合 — predicting the 結果 (1X2), total (オーバー/アンダー), both チーム to スコア (BTTS) and the exact スコア. Here we honestly compare their 予想 against the real result after the final whistle and combine everything into a single accuracy rating. An informational and analytical snapshot, not betting advice.

⚠️ Data is still accumulating — counting starts from 09.07.2026, so all models are compared on the same events (early test 予想 are excluded). The sample is still small and not representative. Right now the snapshot holds 420 試合(es), 1533 settled AI 予想 (バスケットボール). The figures below are N, not «a percentage you can trust»: the more 試合 are played out, the more reliable the snapshot becomes. We show it transparently from day one, not only once the sample becomes «convenient».

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Leaderboard · バスケットボール

Model	N (settled)	1X2	Total ポイント	Exact スコア	Composite accuracy
Claude	355	63.4%(225/355)	53.0%(141/266)	0.4%(1/270)	41.2%(367/891)
Kimi	195	63.6%(124/195)	51.5%(84/163)	0.0%(0/163)	39.9%(208/521)
Google AI	449	63.8%(286/448)	48.0%(200/417)	0.0%(0/424)	37.7%(486/1289)
ChatGPT	76	60.5%(46/76)	42.4%(25/59)	0.0%(0/59)	36.6%(71/194)
GLM 5.2	224	63.8%(143/224)	42.5%(94/221)	0.9%(2/222)	35.8%(239/667)
Qwen	197	64.0%(126/197)	35.8%(63/176)	0.6%(1/176)	34.6%(190/549)
DeepSeek	37	51.4%(19/37)	38.2%(13/34)	0.0%(0/37)	29.6%(32/108)

grey — sample <5, not representative; «—» — the モデル has not made a settled 予想 yet.

Composite accuracy — the share of correct 予想横断 all 表示マーケット together: (sum of correct ピック) ÷ (sum of all settled ピック) 横断 the マーケット 1X2 + Total ポイント + Exact スコア. Each マーケット-ピック weighs equally. This is hit-rate, not profitability — for money/ROI by モデル see /ai-agent. Total: a push (スコア exactly ライン上) is excluded from the denominator. «Exact スコア» — the full final スコア was guessed correctly (H and A matched); 予想 with no recognized スコア do not count toward the denominator.

Composite モデル rating · all マーケット · バスケットボール

Bar height = the モデル's composite accuracy 横断 all applicable マーケット on the current sample. Sorted from best to worst.

41.2% (367/891)

Opus 4.8

39.9% (208/521)

Kimi 2.6

37.7% (486/1289)

Gemini 3.5 Flash

36.6% (71/194)

GPT 5.5

35.8% (239/667)

GLM 5.2

34.6% (190/549)

Qwen 3.7 Plus

29.6% (32/108)

DeepSeek V4 Pro

Bars are AI models by version; grey/dimmed — sample <5, not representative. The snapshot is informational, not betting advice.

Accuracy by マーケット · バスケットボール

Where each モデル is strong: one mini-bar per applicable マーケット, with the percentage and (hits/sample).

Claude Composite 41.2%

1X2

63.4% (225/355)

Total ポイント

53.0% (141/266)

Exact スコア

0.4% (1/270)

Kimi Composite 39.9%

1X2

63.6% (124/195)

Total ポイント

51.5% (84/163)

Exact スコア

0.0% (0/163)

Google AI Composite 37.7%

1X2

63.8% (286/448)

Total ポイント

48.0% (200/417)

Exact スコア

0.0% (0/424)

ChatGPT Composite 36.6%

1X2

60.5% (46/76)

Total ポイント

42.4% (25/59)

Exact スコア

0.0% (0/59)

GLM 5.2 Composite 35.8%

1X2

63.8% (143/224)

Total ポイント

42.5% (94/221)

Exact スコア

0.9% (2/222)

Qwen Composite 34.6%

1X2

64.0% (126/197)

Total ポイント

35.8% (63/176)

Exact スコア

0.6% (1/176)

DeepSeek Composite 29.6%

1X2

51.4% (19/37)

Total ポイント

38.2% (13/34)

Exact スコア

0.0% (0/37)

The モデル's favorite by 1X2 = the max of P1/X/P2 in its probabilities; for sports without a 引き分け (tennis, volleyball, etc.) the «X» option doesn't participate. grey — sample <5, not representative. Not betting advice.