What does temperature actually do?

Temperature scales the randomness of token sampling. Near 0 the model almost always picks the most likely next token, giving deterministic, focused output. Higher values flatten the distribution, producing more varied and creative — but less predictable — text.

Which temperature should I use?

Use low temperature (0 to 0.3) for factual, extraction, classification, and code tasks where consistency matters, and higher (0.7 to 1.0) for brainstorming, copywriting, and ideation where variety helps. This tool lets you see the trade-off on your actual prompt.

Is my API key stored?

No. The key stays in the page and is sent only in the direct HTTPS requests to OpenAI. It is never logged or sent to any Gera server.

Why does temperature 0 still sometimes vary?

Even at temperature 0, outputs are not always byte-identical because of backend non-determinism, but they are far more stable than at higher settings. For strict reproducibility, also set a seed where your provider supports it.

Temperature Effect Visualizer

Temperature effect visualizer

“Just set the temperature lower” is common advice, but the right value depends entirely on your task and your prompt. This tool removes the guesswork: it sends the same prompt at several temperatures and lays the outputs side by side, so you can see exactly how much the setting changes the result before you commit to one.

How it works

You enter a prompt and a list of temperatures (defaulting to 0, 0.5, and 1.0). For each temperature, the tool sends an identical request to OpenAI’s api.openai.com/v1/chat/completions endpoint from your browser using your own key, changing only the temperature parameter. The responses are collected and listed one below the other, each labelled with its temperature, so the differences are easy to compare.

Because the prompt is held constant and only the sampling temperature varies, any difference you see is attributable to the setting itself. Loading and error states are handled per request, and your key never leaves the browser except in the direct call to OpenAI.

Tips and notes

Run a prompt you actually care about, not a toy one — temperature’s effect is task-dependent. For extraction, classification, and code, you will usually find the low-temperature output is the safest choice and the high one introduces noise. For ideation and copy, the higher-temperature variants often surface phrasings the deterministic one would never produce. A useful habit: pick the lowest temperature that still gives you enough variety, since lower settings are cheaper to reason about and easier to test. If you need true reproducibility for a low-temperature task, pair it with a fixed seed where supported.