Gemini last models: temperature, top_p, and top_k are deprecated and ignored

Google's latest Gemini models deprecate and ignore the temperature, top_p, and top_k API parameters, removing manual control over output randomness. Developers relying on these parameters for deterministic or creative outputs must shift to runtime schema enforcement, structured output constraints, and defensive prompting.

Hacker News https://ai.google.dev/gemini-api/docs/latest-model Gemini last models: temperature, top p, and top k are deprecated and ignored Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. Google's latest Gemini models completely ignore and deprecate the temperature , top p , and top k API parameters, stripping production pipelines of manual control over output randomness. This immediately breaks any hardcoded configurations designed to force determinism—such as setting temperature to zero for reliably parsing structured schemas or tool-calling. You must immediately shift your reliability strategies from API-level sampling tweaks to runtime schema enforcement and rigorous defensive prompting to prevent unpredictable output variance in production. Google's latest Gemini models now silently ignore temperature, top p, and top k — these sampling params are deprecated and have no effect on output. If your production code relies on low temperature for deterministic/structured extraction or high temperature for creative diversity, that lever is gone; you'll need to shift control to prompting, structured output constraints, or thinking-budget settings, and you should audit any pipeline that assumed those knobs still worked.