two frontier labs killed the temperature knob in the last few weeks and I noticed this migrating stuff off older models and it’s a bigger deal than the changelogs make it sound both gemini 4 argon and claude sonnet 5.5 have effectively removed control over sampling and on sonnet 5.5, sending any non-default temperature or top_p returns a 400, it refuses the request outright while gemini 4 argon, you can still send temperature, top_p and top_k, but they’re silently ignored, no error, the values do nothing. so one lab fails loud and the other fails quiet, and the quiet one is scarier cause your code looks like it’s still pinning temperature at 0 when it isn’t the official line is that these are thinking models and the reasoning process handles variability, so the old knobs don’t apply. fine. but for two years the standard advice for anything that had to come out the same every time, extraction, classification, structured output, was turn the temperature down. a huge amount of production glue is built on exactly that assumption, and it’s now a no-op or a hard error depending on which api you hit this is the thing i keep coming back to with local models. on my own llama or qwen setup i control temperature, top_k, the seed, the whole sampler, and nobody ships an update that changes what those do without telling you. a closed api can change the meaning of your parameters between one week and the next and the only way you find out is your outputs drift and your logs don’t explain why, because the request you sent never changed the counterargument is real too cause if a reasoning model gives more consistent structured output at its default than an old model did at temp 0, then the knob mattering less is fine, honestly even an upgrade. i haven’t seen anyone actually measure that rather than assume it though. so i’m testing it. same 200 extraction inputs, run each 20 times, measure how often the output is byte-identical. on my local qwen at temp 0 it’s near perfect. argon and 5.5 at their defaults i’m still collecting. if you route through a gateway you can point the same eval set at several models and diff the outputs without rewriting anything, which is how i’m doing the closed ones (Orq AI’s experiments for that, litellm if you want the routing without the eval harness) and whoever’s run this on argon already, os it true abt determinism actually hold without the knob, or did you lose the lever??   submitted by   /u/SmartEnoughToFail [link]   [comments]