Knowledge Cutoff Bench

Every model answers the same questions about things that happened in one known month, from memory, with web search off and no date in the prompt. Run 2026-10-15.

Leaderboard

#ModelLabReal cutoffLab saysRecallCost of one run
1GPT-6 SolOpenAIApr 2026Apr 202625.0$0.069
2Claude Opus 5.5AnthropicMar 2026Jun 202622.2$0.17
3GPT-6 AstraOpenAIFeb 2026Apr 202616.7$0.26
4DeepSeek V4.1 FlashDeepSeekDec 2025Not stated8.3$0.11
5GPT-6 LunaOpenAIFeb 2026May 20268.3$0.006
6Claude Sonnet 5AnthropicDec 2025Jan 20262.8$0.040
7Kimi K3MoonshotNov 2025Not stated2.8$0.40
8Qwen3.8 MaxQwenSept 2025Not stated2.8$0.14
9Gemini 3.5 Flash-LiteGoogleFeb 2025Mar 20260.0$0.058
10Claude Fable 5.1AnthropicDec 2025Jun 20260.0$0.20
11GLM-5.3Z.aiNov 2025Not stated0.0$0.028
12GLM-5.3 FlashZ.aiDec 2025Not stated0.0$0.003
13DeepSeek V4 ProDeepSeekJun 2025Not stated0.0$0.15
14Gemini 3.1 ProGoogleJan 2025Jan 20250.0$0.45
15Grok 4.7xAIAug 2025May 20260.0$0.35
16Gemini 3.8 FlashGoogleMar 2025Mar 20260.0$0.068
17Qwen3.8 FlashQwenMar 2025Not stated0.0$0.048

Sample questions

Real questions from the bench, with the right answer and what every model replied: all 8 questions and answers.

How the bench works

Four questions a month, one each on world news, science and technology, sport and culture, and money and prices. Every answer and its date come from the organiser's or the authority's own record, re-derived by a second agent without seeing the first. The real cutoff is the last month whose three month average is at least half right. A model that says it does not know scores zero, the same as a wrong answer, and is counted apart.