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

**temperature**, **top_p**, and **top_k** are **deprecated and ignored** on **Gemini’s last models**. That report is circulating from the **Hacker News** front page, where discussion is centered on comments rather than a long formal changelog write-up.

Those three controls are the usual knobs for sampling: **temperature** scales randomness in the next-token distribution, **top_p** (nucleus sampling) keeps only the smallest set of tokens whose cumulative probability exceeds a cutoff, and **top_k** hard-limits the candidate set to the k most likely tokens. If the API still accepts the fields but the models ignore them, client code that still passes them will not change generation behavior. Diversity, determinism, and “creative vs strict” tuning no longer route through those parameters on the affected models.

For engineers and builders, that breaks a common pattern: same prompt, vary **temperature** or nucleus settings, A/B the outputs, then lock a setting in production. Caching strategies, eval harnesses, and product features that assume lower **temperature** means more stable answers will misread results if the backend discards the knobs. Teams that encode “use temperature 0 for extraction” or “raise top_p for ideation” in configs, SDKs wrappers, or agent frameworks need to treat those settings as no-ops on these models until they confirm a replacement path.

In market terms, providers have been moving generation quality control into model choice, system instructions, structured output, and tool/schema constraints rather than free-form sampling. Ignoring classic sampling params on “last” Gemini models fits that shift and puts Gemini closer to stacks where determinism is product policy, not a float in the request body. Competitors that still honor **temperature** / **top_p** / **top_k** keep an operational advantage for workflows that depend on fine-grained randomness control; builders comparing providers should test live, not rely on parameter names in the docs alone.

Practical takeaway: audit every Gemini call site and strip reliance on **temperature**, **top_p**, and **top_k** for behavior on the last models; re-validate prompts, judges, and evals without those levers. Watch for an official substitute (fixed decoding, different model IDs, or new request fields) and for SDK/docs updates that mark the params as ignored so silent misconfiguration is less likely. Until then, treat HN comment threads as a signal to verify against your own traffic and the current API surface, not as a complete migration guide.