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Datetime-bench: which datetime formats LLMs get right (and wrong)

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Datetime-bench: which datetime formats LLMs get right (and wrong)

tl;dr * If you need an LLM to parse OR emit a timestamp, use: RFC 3339 ( e.g. 2024-03-26 10:30:00-05:00 ) * python date format also works well * Do NOT use unix epoch or javascript date formats. * Smaller models and non-reasoning models still make a LOT of mistakes in time parsing / formatting. --- There are lots of temporal reasoning benchmarks (like TimeBench, TRAM, etc.) but they test whether models understand time concepts. Nothing on which datetime output format models get right most often. So we just built the benchmark ourselves. We tested 22 models across Google, Anthropic, OpenAI, Qwen, and GLM on 235 scenarios and 7 different formats. The two that surprised us the most were JavaScript Date and unix epoch. JavaScript Date is probably the most commonly used format and it's wrong ~1 in 4 times on parsing. Unix epoch drops to 40% on arithmetic tasks. If you need epoch, just have the model output a string and convert it yourself in code.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, models · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: mistakes, ios · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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