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AI models on real semicha exam questions from Israel's Chief Rabbinate, in Hebrew. Provisional results; reviewers welcome: [email protected]

Joined October 2026
Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)Photo by HalachaBench (@HalachaBench)
Introducing HalachaBench: we gave AI programs from the makers of ChatGPT, Claude, Gemini and others real semicha exam questions from Israel's Chief Rabbinate, in Hebrew, with no books and no internet. The best scored 80 out of 100. Many scored under 11. ๐Ÿงต
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Which semicha paper is hardest for AI? Top 3 models: Chuppah v'Kiddushin. The next 5: Niddah. 7 of 8 did best on Mitzvot HaTeluyot BaAretz. Only 12 questions per paper, and some grading sheets may be stricter, so treat it as a hint. Purely an intellectual exercise.
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Two older OpenAI models, as @GregHBurnham of Epoch AI suggested. GPT-5.6 Sol: 77.3, 3rd of 24, ahead of GPT-6.1 Sol (72.8). GPT-6 Sol: 48.7. Same harness, same effort setting, same questions. The newer model scored 29 points lower. @OpenAI @EpochAIResearch
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Why? GPT-6 Sol didn't make more mistakes (it got 2% of checks wrong, 5.6 got 2.5%). It just said less: ~200 words vs ~310, less thinking, and it covered 48% of the sources and rulings a full answer needs vs 77%. 5.6 did better on 57 of 59 questions.
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It also gave a flat ruling where the answer depends on a fact the question leaves open 33 times, vs 9 for 5.6. Newer isn't always better. On a semicha exam, the model that writes out the conditions and the sources wins. github.com/halachabench/halaโ€ฆ
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Update: 5 models added. Gemini 4 Argon: 78.2, 2nd of 22, statistically tied with Claude Fable 5.1. Grok 4.7: 29.9, 10th. Grok 4.3: 10.8. Claude Opus 4.8: 48.0. Claude Sonnet 4.6: 29.6. The checks behind the Grok result: teapawt.org/HalachaBench/status/21โ€ฆ
New on HalachaBench: Gemini 4 Argon and Grok. Gemini 4 Argon: 78.2 out of 100, 2nd of 22, statistically tied with Claude Fable 5.1 (79.7). Grok 4.7: 29.9, 10th. Grok 4.3: 10.8. (Poll answer: under 65.) @GoogleDeepMind @xai
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New on HalachaBench: Gemini 4 Argon and Grok. Gemini 4 Argon: 78.2 out of 100, 2nd of 22, statistically tied with Claude Fable 5.1 (79.7). Grok 4.7: 29.9, 10th. Grok 4.3: 10.8. (Poll answer: under 65.) @GoogleDeepMind @xai
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Also added: two earlier Claude models. Claude Opus 4.8: 48.0 (7th). Claude Sonnet 4.6: 29.6 (11th), level with Claude Sonnet 5.5 (28.7). @AnthropicAI
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Quick guess: which semicha paper did AI find hardest? Answer tomorrow, with the chart.
12%Chuppah v'Kiddushin
39%Niddah
26%Mikvaot
22%Tefillah u'Brachot
76 votes โ€ข Final results
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Grok is next on HalachaBench. The top score so far is 80 out of 100 (Claude Fable 5.1). Where will Grok land?
7%New #1 (above 80)
40%65 to 80
29%Under 65
24%Just show me
161 votes โ€ข Final results
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AI takes rabbinical ordination examsโ€ฆ ๐Ÿ‘€
Introducing HalachaBench: we gave AI programs from the makers of ChatGPT, Claude, Gemini and others real semicha exam questions from Israel's Chief Rabbinate, in Hebrew, with no books and no internet. The best scored 80 out of 100. Many scored under 11. ๐Ÿงต
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Thank you to @SefariaProject: before the grading sheets were sealed, an AI checked their cited sources against Sefaria's library, about 2,000 lookups. Sources Sefaria doesn't carry are marked unverified.
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Ask an AI the same semicha question three times and you can get three different answers. Keep only each model's weakest try on every question and scores drop 7 to 11 points. The order doesn't change. You'd want the same answer every time you ask.
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ื”ื›ื™ืจื• ืืช HalachaBench: ื ืชื ื• ืœืžื•ื“ืœื™ื ืฉืœ ื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช (ืฉืœ ื™ื•ืฆืจื™ ChatGPT, Claude, Gemini ื•ืขื•ื“) ืฉืืœื•ืช ืืžื™ืชื™ื•ืช ืžืžื‘ื—ื ื™ ื”ื”ืกืžื›ื” ืฉืœ ื”ืจื‘ื ื•ืช ื”ืจืืฉื™ืช. ื”ื ืขื ื• ื‘ืขื‘ืจื™ืช, ื‘ืœื™ ืกืคืจื™ื ื•ื‘ืœื™ ืื™ื ื˜ืจื ื˜. ื”ืžื•ื“ืœ ื”ื˜ื•ื‘ ื‘ื™ื•ืชืจ ืงื™ื‘ืœ 80 ืžืชื•ืš 100. ืจื‘ื™ื ืงื™ื‘ืœื• ืคื—ื•ืช ืž-11. ๐Ÿงต
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ื—ืฉื•ื‘ ืœื”ื“ื’ื™ืฉ: ื–ื”ื• ืชืจื’ื™ืœ ืื™ื ื˜ืœืงื˜ื•ืืœื™ ื‘ืœื‘ื“. ืื™ืฉ ืื™ื ื• ื—ื•ืฉื‘ ืฉื‘ื™ื ื” ืžืœืื›ื•ืชื™ืช ื™ื›ื•ืœื” ืœืคืกื•ืง ื”ืœื›ื”. ื”ืชื•ืฆืื•ืช ืจืืฉื•ื ื™ื•ืช ื•ื–ืžื ื™ื•ืช: ืžื•ื“ืœ ืื—ื“ ื›ืชื‘ ืืช ื”ืžื—ื•ื•ื ื™ื ื•ืžื•ื“ืœ ืื—ืจ ื‘ื“ืง ืืช ื”ืชืฉื•ื‘ื•ืช, ื•ืฉื ื™ื”ื ื’ื ื ื‘ื—ื ื• ื‘ืขืฆืžื. ืืฃ ืจื‘ ืขื“ื™ื™ืŸ ืœื ืขื‘ืจ ืขืœ ื”ืžื—ื•ื•ื ื™ื ืื• ืขืœ ื”ืฆื™ื•ื ื™ื.
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ื›ืœ ืžื™ ืฉืจื•ืฆื” ืœืขื‘ื•ืจ ืขืœ ื”ืžื—ื•ื•ื ื™ื ืื• ืœื‘ื“ื•ืง ืชืฉื•ื‘ื•ืช: ื ืฉืžื— ืžืื•ื“ ืœืขื–ืจืชื›ื. ืจื•ืฆื™ื ืœืขื–ื•ืจ ื‘ื”ืจื—ื‘ืช ื”ืžื™ื–ื? ื›ืชื‘ื• ืœื ื•: [email protected] ื”ืชื•ืฆืื•ืช ื”ืžืœืื•ืช ื•ืคื™ืจื•ื˜ ื”ืฉื™ื˜ื” (ื‘ืื ื’ืœื™ืช, ืขื ืกื™ื›ื•ื ื‘ืขื‘ืจื™ืช): github.com/halachabench/halaโ€ฆ
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