Gemini API Drops Temperature, Top_p, Top_k

Google deprecated temperature, top_p, and top_k in its latest Gemini models, per the official docs and a Hacker News thread that reached 99 points and 37 comments.

The parameters are now ignored at inference time. Requests containing them still succeed, but the values have no effect on output distribution.

Model: Gemini 1.5 / 2.0 latest | Deprecated params: temperature, top_p, top_k | Status: Ignored | Docs: ai.google.dev

What Changed Technically

Gemini now uses a single internal sampling policy per model version. The previous three knobs that let developers adjust randomness, nucleus size, and token filtering no longer reach the decoder.

This removes a common source of non-determinism across runs. The same prompt and seed now produce more consistent token sequences than before.

How Sampling Works Now

The model exposes only a fixed temperature-like behavior baked into each checkpoint. Developers control output style through prompt phrasing, few-shot examples, and system instructions instead of numeric parameters.

Early HN comments noted that creative tasks previously tuned with temperature 0.9 now require explicit instructions such as "vary sentence length and vocabulary."

Impact on Reproducibility

Teams running A/B tests or evaluation suites gain stability. The same prompt yields identical token probabilities across sessions when other variables stay constant.

The tradeoff appears in creative applications. Writers and game developers who relied on temperature sweeps for variation must redesign their pipelines around prompt variation or multiple model calls.

Comparison with Other Providers

Gemini is the first major API to remove all three classic sampling parameters.

Who Should Use This

Teams prioritizing evaluation consistency and reproducible benchmarks benefit immediately. Applications that need fine-grained randomness control should test prompt-based alternatives or stay on older Gemini versions while they remain available.

Practical Next Steps

Update any code that passes temperature, top_p, or top_k. Remove the parameters or set them to documented defaults to avoid future deprecation warnings.

Test output diversity by varying system prompts and example sets rather than numeric values. Measure variance across 50+ runs to confirm the new behavior matches your requirements.

Bottom line: Gemini has removed the three most common sampling controls, forcing developers to manage output variation through language instead of numbers.

The change aligns Gemini with a more opinionated inference stack. Teams that adapt their prompting discipline will see more stable results; those needing wide randomness ranges will need to evaluate other providers.