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

Writing the post body from only the stated facts—no invented versions, dates, or figures.Google Gemini’s latest models now treat temperature, top_p, and top_k as deprecated. Callers can still send those parameters, but the service ignores them. The change surfaced on the Hacker News front page, where discussion focused on the deprecation and on what “ignored” means for existing API clients.

Those three controls sit on the decoding path after the model scores the next-token distribution. Temperature rescales logits before sampling; top_p (nucleus sampling) keeps only the smallest set of tokens whose cumulative probability meets a threshold; top_k truncates to the k highest-probability tokens. When the API drops all three, output variation is no longer adjustable through those knobs. Whatever sampling policy the latest Gemini stack applies is fixed on the server side, not something the client can tune with the old fields.

For engineers this is a contract break, not a style note. Code that sets temperature for deterministic evals, low-creativity tool calls, or higher-entropy brainstorming will keep running and will look valid in request logs, yet the values will have no effect. Prompt templates, unit tests, and product A/B harnesses that assume “same prompt, different temperature” will silently stop separating conditions. Anyone wrapping Gemini behind an internal SDK needs to stop advertising those knobs for the latest models or map them to a no-op with an explicit warning.

Vendors differ on how much decoding control they leave in the public surface. Some keep temperature and nucleus settings as first-class levers; others lock sampling behind managed modes. Deprecating and ignoring the classic trio on the latest Gemini line pushes that stack toward a more closed, server-owned generation policy. Builders comparing providers for chat, agents, or batch generation now have a concrete compatibility axis: whether client-side sampling still works, or only prompts and higher-level product modes do.

Practical next steps: audit every Gemini call path for temperature, top_p, and top_k; treat them as dead on the latest models; and re-validate quality and diversity without those dials. Prefer prompts, system instructions, and structured output constraints where you previously leaned on sampling. Watch official model and API notes for what replaces the old controls—or confirmation that client sampling is gone for good on this line—and keep an eye on Hacker News and similar threads for reports of silent misconfiguration in production traffic.