Developer analysis explains why setting temperature to 0 does not guarantee absolute determinism in LLMs

On November 7, 2026, DEV Community's AI section published a developer-focused article titled "Why temperature 0 doesn't mean absolute determinism: four sampling myths." The author opens with a personal failure: a test in his CI that had been passing suddenly went red. Same prompt, same temperature of 0, yet a different output. That prompted what he calls the boring question: is temperature 0 a promise, or just a request? His answer is that it is a request, and the endpoint decides how literally to honor it, so outputs can still vary.

The piece is structured as a myth-busting FAQ, rounding up four claims the author kept hearing about sampling behavior. For each claim, he lays out the evidence he gathered that breaks it, and includes a small probe readers can run in ten minutes to check how their own models actually behave when temperature is set to 0. The article closes with practical troubleshooting advice for developers trying to pin down the sampling behavior behind their deployments. As of now, the discussion rests with this article: temperature 0 is not being treated as a guarantee of identical outputs, though how different endpoints interpret and honor such a request remains unsettled.