What Is AI Temperature and How Does It Affect Output?

When you ask an AI model the same question twice and get different answers, temperature is usually why. It’s a single parameter that controls how random or deterministic the model’s output is. Most users never touch it. But understanding what it does explains a lot of the behavior you’ve seen.

What Temperature Actually Is

Language models predict the next token by assigning a probability to every token in the vocabulary. Given “The capital of France is”, the model might assign 0.85 to “Paris”, 0.08 to “Lyon”, 0.03 to “the”, and tiny fractions to thousands of other tokens. Temperature controls how the model samples from this distribution.

At temperature 0, the model always picks the highest-probability token. Deterministic. Predictable. At temperature 1, it samples proportionally to the raw probabilities. At temperature 2, it flattens the distribution so that less probable tokens get picked more often. The model becomes more random, more creative, and more likely to produce surprising (or wrong) output.

If you’re new to how models generate text, what is an LLM covers the basics of token prediction and probability distributions.

How It Works Technically

The model outputs logits, raw scores before normalization. These get passed through a softmax function to convert them into probabilities that sum to 1. Temperature enters here: the logits are divided by the temperature before softmax.

At low temperature (e.g., 0.1), dividing by a small number makes the differences between logits larger. The highest-probability token dominates. The distribution becomes sharp, peaked. The model almost always picks the same token.

At high temperature (e.g., 2.0), dividing by a large number shrinks the differences. The distribution flattens. Tokens that had 1% probability might now have 5%. The model samples from a much wider range. Output becomes less predictable.

Think of it as a dial. Turn it down: the model narrows its choices. Turn it up: the model considers more options, including unlikely ones.

Related Parameters: Top-p and Top-k

Temperature isn’t the only knob. Two related parameters shape sampling in different ways.

Top-k limits the model to the k highest-probability tokens. If k is 50, the model ignores everything outside the top 50 and renormalizes the probabilities among those 50 before sampling. This cuts off the long tail of very unlikely tokens. Useful when you want some randomness but not total chaos.

Top-p (nucleus sampling) is similar but dynamic. You specify a probability threshold (e.g., 0.9). The model takes the smallest set of tokens whose cumulative probability exceeds that threshold, then renormalizes and samples from that set. If the top token has 0.7 probability, the nucleus might include just a few tokens. If the distribution is flatter, the nucleus expands. Top-p adapts to the shape of the distribution.

In practice, many APIs use temperature alone, or temperature combined with top-p. Top-k is less common in modern APIs. They all serve the same goal: controlling how much the model explores vs. exploits the probability distribution.

Practical Defaults: When to Use What

Low temperature (0 to 0.3): Factual tasks, code generation, data extraction, summarization, structured output. You want consistency and accuracy. The model should pick the most probable token, not wander into creative alternatives. For code, a wrong token can break the whole function. For facts, a wrong token can cause hallucination. Default to low.

High temperature (0.7 to 1.0): Brainstorming, creative writing, idea generation, varied responses. You want diversity. The model should consider less obvious options. A single “right” answer doesn’t exist. Exploration is the goal.

Very high temperature (1.5 to 2.0): Experimental, playful, or deliberately random output. Rarely useful in production. Can produce incoherent or nonsensical text. Use for exploration only.

Most production systems use 0 for deterministic tasks and 0.7 for creative ones. The defaults in ChatGPT, Claude, and other interfaces are usually in that range. They work for most use cases.

Temperature 0 vs 1 vs 2: What Changes

Ask the same model “Write a one-sentence tagline for a coffee shop” at different temperatures.

Temperature 0: You’ll get the same output every time. Probably something generic and safe: “Fresh coffee, made with care.” The model always picks the highest-probability next token. No variation.

Temperature 1: You’ll get different outputs on each run. Some might be creative: “Where every cup tells a story.” Some might be bland. The model samples proportionally, so common phrasings appear more often, but you’ll see variety.

Temperature 2: Output becomes unpredictable. You might get “Espresso dreams and croissant schemes” or something that barely makes sense. The model is pulling from the tail of the distribution. High creativity, high risk of nonsense.

For a factual question like “What is the capital of France?”, temperature 0 and 1 will both give “Paris” almost every time, because that token dominates the distribution. Temperature 2 might occasionally produce “Lyon” or something wrong. The flatter distribution gives unlikely tokens a real chance.

Why Most Users Never Need to Touch It

The default temperature in most interfaces is tuned for general use. It’s usually 0.7 or 1.0, which works for a mix of factual and creative tasks. If you’re chatting, writing, or brainstorming, the default is fine.

You should care about temperature when:

• You’re building an application with an API and need consistent, reproducible output (use 0).
• You’re doing fact-based extraction or code generation (use 0).
• You’re getting outputs that are too repetitive or too random (adjust accordingly).
• You’re debugging why the same prompt gives different results (temperature is the first thing to check).

Understanding temperature also explains behavior you’ve seen: why the model sometimes gives you the same answer every time, why it sometimes surprises you, and why hallucination increases when you crank it up. Higher temperature means more sampling from low-probability tokens, and those tokens are less grounded in the model’s training patterns.

The Big Picture

Temperature is a sampling parameter. It doesn’t change what the model “knows” or how it was trained. It only changes how it chooses the next token from the probability distribution it produces. Low temperature: pick the best. High temperature: explore more.

It interacts with other parts of the system too. A long context window gives the model more to work with, but temperature still controls how it uses that context. A well-crafted prompt narrows the distribution; temperature controls how strictly the model follows it.

For most users, the default works. For engineers building AI systems, temperature is one of the first parameters to set intentionally. Get it right and your outputs become predictable when they need to be, creative when they don’t.

Get Insanely Good at AI covers temperature, sampling strategies, and how to tune model parameters for production systems. See the full book for the complete treatment.