What Is Temperature and Top-p in AI? The Creativity Setting Explained

What Is Temperature and Top-p in AI?

Temperature and top-p are two sampling settings that control how random a language model is when it picks the next word (token). Temperature sharpens or flattens the probability distribution. Top-p, on the other hand, limits the choice to the most likely candidates whose probabilities add up to a chosen threshold.

Together, these settings decide whether an answer feels consistent or creative. In other words, you do not change the model itself. Instead, you change how it draws from the probabilities it already produced. Anyone who works with a large language model should know both dials.

In this guide, we answer "what is temperature and top-p" in plain language. Then we cover low and high values, the difference from top-k, the gap between chat apps and APIs, and a practical testing plan. We avoid fixed numeric ranges on purpose, because they vary by provider and model.

What is temperature and top-p, explained with an everyday example?

Imagine ordering food at a restaurant. The waiter holds a list that shows how likely you are to pick each dish. For example, if the waiter always brings the most likely dish, the result never changes. If the waiter sometimes picks a less likely dish, the table gets more variety.

First, temperature sets how bold the waiter is. At a low value, the waiter sticks to the favorite. At a high value, even unlikely dishes start to appear. Top-p works differently: the waiter adds up the likelihoods from the top and cuts the list once a certain share is reached.

The analogy is not perfect. Still, it shows that both settings solve the same problem in two ways. One reshapes the probabilities, and the other limits which candidates stay in the game.

How does a language model choose the next word?

A language model writes text in small pieces called tokens, not whole words. At every step, the model produces a score for each possible token. Then it turns those scores into probabilities that add up to one. In short, we call this a probability distribution.

For example, after the phrase "The weather today is very," the word "nice" may get a high probability. "Cold" may get a medium one, and "blue" a very low one. The model picks one token, adds it to the sentence, and repeats the process for the next step. For more detail, read our guide on what a token is.

Also, this sampling step happens only while the model writes an answer. So temperature and top-p do not change the training or the knowledge of the model. For example, the same model can sound very different under different settings. That is why testing settings is cheap and fast compared with retraining.

The key point is simple: the model does not have to choose the most likely token. Instead, how it chooses depends on the sampling settings. In practice, temperature and top-p are the two main ones.

• At every step, the model builds a probability distribution.
• Sampling settings decide how to draw from that distribution.
• The same prompt can produce different text under different settings.
• Settings do not add knowledge. Instead, they only change the choice behavior.

How does temperature work?

Temperature rescales the probability distribution before the model chooses. At a low value, the distribution gets sharper. Probabilities that were already high rise further, and the rest drop close to zero. As a result, the model almost always picks the top token, and answers feel predictable.

At a high value, the distribution flattens. As a result, unlikely tokens get a realistic chance. The text also becomes more varied, more surprising, and sometimes more scattered. Technically, the model divides its raw scores by a number before it converts them to probabilities. For most readers, "sharpness dial" is a good enough mental model.

When temperature approaches zero, the model picks the most likely token at nearly every step. In practice, people call this greedy selection. However, this does not always mean perfect repeatability. We cover that in a separate section below.

What do low, medium, and high temperature change?

Providers use different ranges. So instead of memorizing a fixed number, it is safer to think in low, medium, and high. Check the valid range and the default value for your model in the provider's current documentation.

• Low: Answers stay consistent, short, and cautious. For example, the same question gets similar replies. This suits classification, data extraction, and rule-following tasks.
• Medium: This setting balances both sides. The writing flows naturally, but the model stays on topic. Many teams choose it for general chat and summaries.
• High: Answers get more varied, and unexpected phrases appear. This also helps with brainstorming and creative writing. However, the risk of mistakes and inconsistency also grows.

A high value does not mean "smarter." The model has the same knowledge, but it takes riskier choices. For tasks that need facts, a high temperature can therefore increase the risk of made-up information.

What is top-p (nucleus sampling) and how does it work?

Top-p is also called nucleus sampling. The model sorts tokens by probability from high to low. Then it adds the probabilities from the top until the total reaches the top-p value. This group is the "nucleus," and the model chooses only from it.

For example, here is a small calculation. Suppose five candidate tokens have probabilities of 50, 30, 10, 5, and 5 percent. If you set top-p to a share of 80 percent, the first two candidates reach 80 percent together. As a result, the nucleus holds those two, and the other three drop out. These numbers are for illustration only, not from a real model.

A strong advantage of this method is that the number of candidates is not fixed. For instance, when the model is confident, the nucleus shrinks by itself. When the model is unsure, the nucleus grows.

The idea comes from academic research. The nucleus sampling paper (arXiv) argues that sampling from the "nucleus" of likely words produces more natural text. If you like detail, we recommend reading the primary source.

What is top-k, and how is it different from top-p?

Top-k keeps only the k most likely tokens at each step. Everything else then drops out. In short, top-k limits candidates by count, while top-p limits them by probability share. The gap looks small, yet it changes behavior.

Even when the model is very sure, top-k keeps a fixed number of candidates, and many may be nonsense. When the model is very unsure, top-k may cut good options. Top-p adapts to the situation instead. For that reason, many developers find top-p more flexible.

Also, not every provider offers top-k. The Google Gemini API documentation describes temperature, topP, and topK together. Some other APIs do not offer top-k at all, or offer it only for certain models. So always confirm which parameters your API supports.

Should you change temperature and top-p together?

Technically, you can set both. In most cases, though, we advise against it. We say this because both settings control the same behavior, which is variety. If you move both at once, you cannot tell which one changed the result, and your experiments get messy.

Provider documentation often carries a note on this point: change one and leave the other at its default. The OpenAI API reference and the Anthropic Messages API documentation include advice in this direction. Pages change between versions, so check the page for your own model.

However, Google gives a different kind of warning. The Google Gemini API documentation strongly recommends keeping these parameters at their default values for some newer Gemini versions. It also says that changes can cause unexpected behavior, such as looping or degraded performance. So "more tuning" does not always mean "better results."

1. First, leave both settings at their defaults and measure the baseline quality.
2. Next, change only one setting.
3. Then compare the results using the same sample inputs.
4. Finally, write down the outcome, and move to the second setting only if you need to.

Does temperature zero always give the same answer?

No, not always. At a very low temperature, the model almost always picks the top token, so answers become mostly consistent. However, most providers do not guarantee exact repeatability. For example, infrastructure, model version, and computation details can create small differences.

So "temperature zero means a certain result" is a wrong assumption. Also, in long answers, a tiny difference can grow step by step. Some APIs also offer extra settings such as a seed. These try to improve consistency, but they still do not promise it. Read the exact wording in the provider's documentation.

So for critical work, do not rely on a setting alone. Format rules, output validation, and human review give a much sturdier result.

Which temperature and top-p approach fits which task?

The table below is a starting point based on field experience. In other words, it is a suggestion for testing, not a guarantee. So test it on your own model and your own data.

The top-p column says "default" for most rows on purpose. As we explained earlier, it is usually healthier to move one setting and keep the other fixed.

Which terms do people mix up when they ask what is temperature and top-p?

In practice, sampling parameters often get confused with other settings. The table below puts the differences side by side. They sit close together in API panels, so the confusion is normal.

The last row matters most. Put simply, you cannot rescue a weak prompt with temperature. First apply the basics of prompt engineering, then treat sampling parameters as fine-tuning.

How do max tokens, repetition penalties, and stop sequences fit in?

Other parameters also shape the answer next to sampling settings. They are easy to confuse with temperature and top-p, so a short separation helps. Not every provider uses the same names or offers the same options.

• Max tokens: This limits how long the answer can grow. Instead, it affects length, not creativity. If you set it too low, the answer may stop halfway.
• Repetition penalty: This tries to reduce repeated words and phrases. Some APIs offer it, and others do not.
• Stop sequence: This ends the output when a certain phrase appears. It helps when you need a fixed format.

These settings work together, yet each has its own job. For example, if your answer repeats itself, first review your prompt and the repetition penalty. Raising temperature may fix the repetition, but it may also add inconsistency.

What is the difference between a chat app and an API?

In chat apps such as ChatGPT, Gemini, or Claude, you usually cannot change these settings yourself. Instead, the provider picks values in the background to suit the product. That is why you may get slightly different answers to the same question in each chat.

With an API, however, the situation changes. You can also pass these parameters in the request body. If you skip them, the provider's default values apply. Some platforms also offer a playground, where you can test settings with a slider.

Chat apps also add system instructions, memory, and safety layers that shape the answer. So two products can use the same model and still give different results. With an API, you build most of those layers yourself. That freedom also brings responsibility.

• Chat app: The setting is usually hidden, and users cannot change it.
• API: You define the parameters for each request.
• Playground: It is ideal for quick tests, but it is not a production setting.
• Production code: Store your chosen value in configuration and note why you chose it.

For a first step on the API side, our guide to using the OpenAI API is a useful starting point.

Does every model support temperature and top-p the same way?

No. Supported parameters, valid ranges, and defaults differ by provider. They can also differ between models from the same provider. Instead of memorizing this, check the current documentation for the model you use.

In some model families, the provider may restrict, fix, or change how these parameters work. For example, reasoning models often produce intermediate steps before the final answer. For such models, read the provider's documentation to see whether sampling settings are offered and how they behave.

When you move to a new model version, do not copy old settings blindly. For example, the same value can behave differently on a new model. A short regression test, meaning a rerun of the same sample inputs, closes that risk cheaply.

Does lowering temperature stop hallucinations?

It can reduce them, but it does not stop them. A low temperature pushes the model toward the most likely token. However, the most likely token is not always correct. A model can produce a false statement with high probability if the pattern is strong in its training data.

In other words, temperature is a variety setting, not an accuracy setting. To cut down made-up information, read our article on AI hallucination. For example, the methods there include citing sources, grounding in a knowledge base, and verifying output.

If you build a system that works with company documents, a RAG setup helps accuracy far more. In practice, low temperature plus RAG makes answers consistent and tied to sources. Even so, no result is guaranteed, so always verify with sample tests.

How do these settings behave in agents and tool-calling systems?

An AI agent takes many steps instead of giving a single answer. It calls a tool, reads the result, and picks the next step. In that chain, the randomness of each step affects the next one. A small deviation can grow into a large difference a few steps later.

For this reason, most teams keep variety low in systems that call tools and produce structured output. For example, if the output format breaks, the next step fails. The text-writing step, on the other hand, can be managed as a separate part of the chain.

In short, you do not have to use the same setting at every step. You can pick a consistent setting for extraction and decision steps and a looser one for writing steps. Still, confirm in the documentation whether your provider allows per-step settings.

Example scenario: how do we choose settings for three jobs in an online store?

The example below is entirely fictional and is not a client result. Suppose an e-commerce team uses AI for three jobs.

• Product attribute extraction: Move color, size, and material from supplier text into a table. Consistency is a must, so you pick a low temperature.
• Answering customer questions: Relay clear facts such as the return policy. So a low to medium value with a linked document works well.
• Campaign slogan ideas: Ask for twenty draft slogans. Here, instead, a medium to high value brings more varied ideas.

Even in the third job, the model does not produce publish-ready output. Still, the team picks the best few ideas and edits them by hand. So a high temperature opens the door to creative output, but it does not remove the need for editorial review.

What if the team used one setting for every job? Then a low setting would make the slogans dull, and a high setting would add errors to product attributes. That is why choosing settings per job matters.

Which mistakes do teams make most with temperature and top-p?

These are the mistakes we see most often. In many cases, the problem is not the setting itself but the expectation placed on it.

• Changing both parameters at once and not knowing which one had an effect.
• Looking for accuracy through a high temperature.
• Expecting the exact same result every time at a low temperature.
• Deciding from a single test result.
• Skipping a retest when the model version changes.
• Trying to fix a weak prompt with a setting.

The fix for all of them is similar: run small, controlled, recorded experiments. Run the same input several times, place the outputs side by side, and write your criteria first.

How do you test temperature and top-p settings?

A good test plan does not need to be complicated. You need a small sample set of real inputs and a clear success criterion. The steps below work for most projects.

1. Collect ten to twenty sample inputs from your real use case.
2. Write your success criteria, such as accuracy, tone, length, and format fit.
3. Run every input with default settings and record the outputs.
4. Change only temperature and run the same inputs again.
5. Score the outputs against your criteria. A blind review, where you do not know which setting produced which output, is fairer.
6. Write the winning setting into configuration and note your reasons.

In short, this method relies on process more than on numbers. If needed, you can strengthen your criteria with few-shot prompting examples or chain-of-thought prompting prompts.

What is the practical checklist for businesses and developers?

The list below gathers the points to check before you ship an AI feature. We kept it short on purpose, so you can use it quickly.

• Read the current documentation for the model and version you use.
• Confirm the supported parameters and default values.
• Change only one sampling setting at a time.
• Choose a low, medium, or high approach by task type.
• Add format rules and a validation step for critical outputs.
• Store the chosen value in configuration and record the reason.
• Retest the settings when the model version changes.
• Plan human editorial review for creative outputs.

As a decision maker, you do not need to handle the technical values yourself. However, you should expect a clear answer from your team to the question "why did you choose this setting, and how did you test it?"

How do we handle temperature and top-p in client projects?

At Talha Aslan and team, we treat sampling settings as a design decision that we must test, not as a magic number. For every workflow, we define the success criterion first and then choose the setting.

In AI chatbot development and similar work, a common approach is to keep customer-facing answers consistent and to leave internal idea generation looser. For the wider picture, you can look at our AI automation services page.

In short, this section describes how we work. It is not a promise of results. Every project is different, so the best setting only emerges from tests on your own data.

So when and how should you use temperature and top-p?

In short, when people ask "what is temperature and top-p," the answer is simple: temperature adjusts the sharpness of the probability distribution, and top-p adjusts the width of the candidate pool. Low values favor consistency, and high values favor variety. The two are alternatives to each other, and changing just one is usually enough.

Above all, temperature is not an accuracy or intelligence dial. For critical work, you need validation, source grounding, and human review next to the setting. Finally, remember that numeric ranges differ between providers, so always check the current value in the official documentation.

If you want to keep learning AI terms, our articles on reasoning models and AI hallucination are the natural next steps. They also help you see the link between parameters, model behavior, and accuracy as one picture.