That's the thing with benchmarks, without evals and actual hands-on experience they can give you false confidence. Claude now sounds almost clinical, and is unable to speak in different styles as easily. Claude 4+ uses a lot more constructions borrowed from English than Claude 3, especially in Slavic languages where they sound unnatural. And most modern models eventually glitch out in longer texts, spitting a few garbage tokens in a random language (Telugu, Georgian, Ukrainian, totally unrelated), then continuing in the main language like nothing happened. It's rare but it happens. Samplers do not help with this, you need a second run to spellcheck it. This wasn't a problem in older models, it's a widespread issue that roughly correlates with the introduction of reasoning. Another new failure mode is self-correction in complicated texts that need reading comprehension: if the model hallucinates an incorrect fact and spots it, it tries to justify or explain it immediately. Which is much more awkward than leaving it incorrect, and also those hallucinations are more common now (maybe because the model learns to make those mistakes together with the correction? I don't know.)