Temperature is probably the most misunderstood setting in ChatGPT, Claude, and every other AI chat app. Most people either ignore it completely or crank it up expecting "more creative" results. Neither approach is quite right.
If you've noticed that ChatGPT gives slightly different answers to the same question, or wondered why your AI-generated code sometimes invents functions that don't exist—temperature is usually the culprit.
What Temperature Actually Does
LLMs don't "think." They predict the next word based on probabilities. Given "The capital of France is", the model assigns probabilities to every possible next word: "Paris" might get 85%, "the" might get 5%, "located" 3%, and so on down to near-zero for "banana."
Temperature controls how the model picks from these probabilities.
Low temperature (0.1–0.3) means the model almost always picks the highest-probability word. Safe, predictable, consistent.
High temperature (0.7–1.5) means the model is willing to pick lower-probability words. More varied, sometimes surprising, occasionally nonsensical.
Temperature 1.0 is the default—the model's natural probability distribution, unmodified.
Think of it as a dial between "by the book" and "wildcard."
Does Higher Temperature = More Creative?
Sort of, but not how most people think.
When you raise the temperature, the model becomes more willing to pick less-probable words. This surfaces unusual phrasings, unexpected combinations, patterns from the long tail of its training data. Sometimes that's genuinely creative. Sometimes it's gibberish.
The thing is: temperature doesn't make the model think harder or become more inventive. It just widens the net. A wider net catches more interesting fish—and more junk.
I've found that if you want reliably better creative output, improving your prompt matters more than cranking temperature. But for brainstorming or generating variations? Higher temperatures genuinely help.
The Ranges Are Different Across Providers
This trips people up:
- OpenAI (GPT-4o, GPT-4.1): 0.0 – 2.0, default 1.0
- Anthropic (Claude): 0.0 – 1.0, default 1.0
- Google (Gemini): 0.0 – 2.0, default 1.0
So temperature 0.7 on Claude is proportionally higher than 0.7 on GPT-4o. If you're comparing models, this matters—or just use something like PromptHQ to test the same prompt across models side-by-side.
One more thing: OpenAI's reasoning models (o1, o3) don't support temperature at all. The parameter just gets ignored.
What Temperature to Use
I'm not going to give you a giant table. Here's the short version:
Go low (0.0–0.3) for code, factual questions, classification, translation, or anything where you need the model to follow instructions precisely. Creative bugs are not a feature.
Stay in the middle (0.4–0.7) for summaries, emails, explanations—stuff where you want natural flow but not wild swings.
Go higher (0.8–1.2) for brainstorming, marketing copy, creative writing, or when you explicitly want variety. "Give me 5 different ways to say this" is a good use case.
Avoid 1.5+ unless you're experimenting. Output gets incoherent—made-up words, sentences that trail off, grammatical breakdowns. It's rarely useful.
Temperature 0 Isn't Actually Deterministic
This surprised me when I first learned it. You'd think temperature 0 means "always give me the exact same response." It doesn't.
Even at temperature 0, you'll see slight variations. The reasons are technical—floating-point math on GPUs isn't perfectly consistent, your request gets batched with others, some models use routing architectures that introduce small variations.
One researcher ran 1,000 identical prompts at temperature 0 and got 80 unique responses. They were identical for the first 102 tokens, then diverged.
If you really need reproducibility, look for a seed parameter in addition to temperature 0. But even then, no provider actually guarantees identical outputs.
Provider Quirks Worth Knowing
OpenAI: Temperatures above 1.0 get weird fast—you start seeing underscores, made-up words, nonsense. Their docs suggest 0.9 for creative tasks, 0 for analytical ones.
Claude: The 0.0–1.0 range means small changes have bigger effects than on other models. Even at 0.0, outputs aren't fully deterministic (Anthropic documents this explicitly).
Gemini: For the newer Gemini 3 models, Google actually recommends keeping temperature at 1.0. Changing it can cause unexpected behavior like output looping. Gemini 2.5 and earlier handle temperature tuning normally.
Finding What Works
Start with the default (1.0). Only adjust if you have a reason.
Small moves matter. Going from 1.0 to 0.7 is a meaningful change—you don't need to swing from 0.2 to 0.9.
Run the same prompt multiple times at your chosen temperature. This shows you the actual variance you should expect. (PromptHQ makes this easy.)
If you're chaining LLM calls—where one output feeds into the next—use lower temperatures (0.2–0.5). Variance compounds across steps.
Match temperature to stakes. Medical, legal, financial? Stay low. Low-stakes content where "close enough" is fine? You can go higher.
When you find something that works, write it down. Different tasks genuinely benefit from different settings, and you'll forget what worked.
When It Doesn't Matter
Sometimes temperature is basically irrelevant:
- Very short outputs: Asking for a single word or number? Temperature won't change much.
- Highly constrained prompts: If only one reasonable answer exists, temperature barely matters.
- Reasoning models: As mentioned, some don't support it at all.
- Bad prompts: No amount of temperature tuning fixes a vague or confusing prompt. Fix the prompt first—here's how.
Temperature is a useful dial, not a magic wand. The default works for most things. For specialized tasks, deliberate tuning helps. But if you're spending more time tweaking temperature than improving your prompts, you're probably optimizing the wrong thing.