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

Drafting five analytical paragraphs from only the stated facts—no invented versions, dates, or figures.Gemini’s latest models now treat **temperature**, **top_p**, and **top_k** as deprecated. Those sampling controls are ignored at runtime. The change surfaced on the **Hacker News** front page, where the discussion centers on comments rather than a long primary write-up: builders are reacting to the loss of knobs they still pass in client code.

Technically, those three parameters are the usual levers for decoding randomness. **Temperature** scales how sharp or flat the next-token distribution is. **Top_p** (nucleus sampling) keeps only the smallest set of tokens whose cumulative probability crosses a cutoff. **Top_k** keeps only the k highest-probability tokens before sampling. On these Gemini models, sending them no longer changes generation: the API or runtime accepts the request path but does not apply the values, so output variance is fixed by the model’s own default decoding path.

For engineers, the practical break is silent. Existing SDKs, wrappers, and prompt pipelines often still set temperature for “creative” vs “deterministic” modes, or tune top_p / top_k for tool-calling and structured output. If those fields are ignored, A/B tests that only change sampling params will look like no-ops. Retry loops that lower temperature to reduce flakiness will not stabilize answers. Eval harnesses that assume controllable entropy will mis-attribute score moves to the model or the prompt when the real issue is that sampling never changed.

In market terms, this pushes Gemini’s latest line toward a more locked decoding surface while many competing APIs still expose the same trio as first-class controls. Teams that abstract “provider + sampling config” behind one interface will need Gemini-specific branches: either drop those fields, stop documenting them as effective, or route workloads that depend on tight sampling control elsewhere. Comment threads on HN are already the discovery channel for this class of API behavior change—often before docs or client libraries fully catch up.

What to do next: audit every call site that sets **temperature**, **top_p**, or **top_k** against Gemini’s latest models and treat them as no-ops until the platform documents a replacement. Prefer prompt structure, system instructions, and post-processing for style and determinism instead of sampling flags. Watch for official guidance on whether any new decoding controls replace the deprecated set, and whether older Gemini models still honor the old parameters so multi-model routers do not apply one config to both.Gemini’s latest models now treat **temperature**, **top_p**, and **top_k** as deprecated. Those sampling controls are ignored at runtime. The change surfaced on the **Hacker News** front page, where the discussion centers on comments rather than a long primary write-up: builders are reacting to the loss of knobs they still pass in client code.

Technically, those three parameters are the usual levers for decoding randomness. **Temperature** scales how sharp or flat the next-token distribution is. **Top_p** (nucleus sampling) keeps only the smallest set of tokens whose cumulative probability crosses a cutoff. **Top_k** keeps only the k highest-probability tokens before sampling. On these Gemini models, sending them no longer changes generation: the API or runtime accepts the request path but does not apply the values, so output variance is fixed by the model’s own default decoding path.

For engineers, the practical break is silent. Existing SDKs, wrappers, and prompt pipelines often still set temperature for “creative” vs “deterministic” modes, or tune top_p / top_k for tool-calling and structured output. If those fields are ignored, A/B tests that only change sampling params will look like no-ops. Retry loops that lower temperature to reduce flakiness will not stabilize answers. Eval harnesses that assume controllable entropy will mis-attribute score moves to the model or the prompt when the real issue is that sampling never changed.

In market terms, this pushes Gemini’s latest line toward a more locked decoding surface while many competing APIs still expose the same trio as first-class controls. Teams that abstract “provider + sampling config” behind one interface will need Gemini-specific branches: either drop those fields, stop documenting them as effective, or route workloads that depend on tight sampling control elsewhere. Comment threads on HN are already the discovery channel for this class of API behavior change—often before docs or client libraries fully catch up.

What to do next: audit every call site that sets **temperature**, **top_p**, or **top_k** against Gemini’s latest models and treat them as no-ops until the platform documents a replacement. Prefer prompt structure, system instructions, and post-processing for style and determinism instead of sampling flags. Watch for official guidance on whether any new decoding controls replace the deprecated set, and whether older Gemini models still honor the old parameters so multi-model routers do not apply one config to both.