What Are Temperature and Creativity in Language Models, Really?
If you have ever experimented with ChatGPT, Google Gemini, Claude, or Mistral, or created your own chatbot using tools such as Google AI Studio or OpenAI Playground, you may have noticed a slider with the mysterious name “temperature.”
For many beginners (and even intermediate users), the term “temperature” in the context of artificial intelligence sounds like black magic. Some explain it as the “model’s level of creativity.” Although there are indeed certain parallels between how temperature works and the resulting creativity, this oversimplification can easily lead to misunderstandings.
On the other hand, some people believe that we should not look for simple comparisons or metaphors—and that it is best to simply talk about temperature as a technical parameter, without more intuitive analogies. This approach makes sense, but mainly among people with a deeper interest in artificial intelligence.
In practice, however, most users (I suspect at least 90% of people using AI) will use temperature intuitively, without having any idea about concepts such as layers, transformers, or the attention mechanism. Therefore, completely avoiding simplifications (such as creativity) when explaining temperature, although elegant from a purist’s point of view, often fails to reflect reality.
Today I’ll show you how temperature and creativity work in language models—and why describing temperature as a creativity slider can be misleading.
Table of Contents
-
Greater artificial intelligence creativity requires more computing power and a better model.
-
A higher language model temperature may affect the creativity of the person using it.
-
Chatbot based on your own knowledge (e.g. documentation, instructions)
What exactly is temperature?
Let’s start with the basics. Temperature is a numeric parameter that controls how a language model selects subsequent words when generating a response.
When you ask a model to write a sentence, it does not actually “think” in the classical sense. It calculates which word is most likely to occur at a given stage and selects one of them.
Temperature determines whether the model should choose the most predictable word or allow itself a little freedom and reach for less obvious options.
Example:
Imagine that the model writes a sentence:
“Today I’ll have … for lunch”
Without any restrictions, the most likely ending is probably:
“…pasta.” or “…soup.”
But if you raise the temperature, the following may occur:
“…fried basil leaves in chocolate sauce.” or “…nothing, because the universe fell apart.”
See the difference? A high temperature doesn’t add meaning—but it allows for greater randomness. It’s like spinning a wheel of fortune with more non-obvious options.
Too complex? Simply put, ChatGPT can be compared to autocorrect on a phone. Only much more advanced.
Autocorrect evaluates what you meant based on probability—it analyzes the misspelled word you entered and matches it to the most probable correct form. For example: if you type “slon,” the system may assume you meant “słoń”; if you type “drewono,” the system will assume you meant “drewno.”
But that is not all. Modern smartphone keyboards often also try to predict the next word—and display it as a suggestion above the keyboard. They most often do this based on the last one, two, or three words. This context makes it possible not only to guess the content, but also to choose the appropriate grammatical form (e.g., case or number).
In this way, we learn the simple operating principle of language models—based on predicting the most probable sequences of words.
Of course, calling ChatGPT merely “autocorrect” is a considerable oversimplification that can lead to misconceptions. It is worth remembering that for a model to be able to analyze not only the last few words, but the entire paragraph, and then generate coherent text consisting of several hundred words, additional mechanisms are needed—such as transformer architecture, multilayer neural networks, and the attention mechanism.
Autocorrect most often predicts one next word, and does so within a very limited context. A large language model works differently: after generating the first word, it takes it into account (along with the entire prompt and the preceding context) to choose the next word—and then the entire sequence again, and the next one… until a complete utterance is produced.
Technical explanation: what happens “under the hood”?
Language models operate on the principle of so-called next-token prediction (that is, predicting the next word or part of a word).
For each possible token, the model assigns a probability. E.g.:
Token | Probability |
“pasta” | 0.40 |
“soup” | 0.35 |
“cookie” | 0.10 |
“crocodile” | 0.02 |
“rainwater” | 0.005 |
Without a temperature (or with a temperature
0
), the model will always choose “pasta” because it has the highest probability.
With temperature
1.0
lub
1.5
, this distribution gets “flattened,” and then even a “crocodile” has a real chance of appearing in the sentence.
The higher the temperature, the “flatter” the differences between words become, and the more room there is for unpredictability.
Where did this comparison to creativity come from?
In creative fields such as copywriting, short story writing, poetry, or advertising, people have noticed that responses can be more interesting at higher temperatures.
It was therefore said:
“Want AI to be more creative? Turn up the temperature.”
And that is true—but only half the truth. Because…
Temperature ≠ creativity
This is one of the most common misconceptions. Saying that temperature is creativity is an oversimplification that leads to false assumptions. Here’s why:
Temperature Does Not Create Anything New
The model already knows all the answers it can generate—temperature does not add ideas, it only shifts the point from which it samples them.
Higher temperature = a greater tendency to choose a less obvious word. But it is still a choice from previously learned options.
Creativity in People Implies Intent
A human creates “creatively” because they have a goal, a message, emotions, and an awareness of context. An AI model has none of that. It operates solely on statistics.
Temperature introduces randomness, but randomness is not the same as creativity.
Creativity is choosing something new, but meaningful. Temperature does not recognize meaning—only opportunity.
High temperature = chaos, not always value
When you set the temperature to
1.5
or
2.0
, you may receive a reply:
“Elephants look at the cup that tells the truth about lemon soup.”
It may sound like surrealism, but most often it is simply incoherent gibberish. Creativity needs structure. Temperature does not provide it.
How to Approach the Creativity of Language Models?
The very use of the concept of creativity in relation to artificial intelligence is quite useful. There are language models that seem to be more creative (e.g. ChatGPT 4o) and less creative (older versions of models, e.g. ChatGPT 3.5). We therefore have two issues here:
- Artificial intelligence creativity differs from human creativity
- Temperature does not function as a simple “slider” for setting artificial intelligence creativity.
Greater artificial intelligence creativity requires greater computing power and a better model.
For both people and language models, creativity is not something that can simply be adjusted. Certainly, sometimes saying, “be more creative when carrying out this task,” may lead to more creative results; however, it cannot make someone who is not very creative become highly innovative. Creativity is a skill acquired over many years.
The same applies to language models—the more advanced the model, the more creatively it can operate. Much also depends on the environment we prepare for it (e.g., by using better prompts and providing better data, context files, etc.).
How AI Model Temperature Relates to Creativity.
A higher temperature of AI models can stimulate human creativity. Regardless of the temperature, AI can discover something new—it happens when the probability of connections between topics, resulting from calculations, differs from the associations existing in society. Sometimes hallucination is involved here—when we ask ChatGPT to provide specific answers, the model may make them up, but sometimes such invented texts are also interesting. Often, the results of this make no sense, but it happens that the connections AI uses are useful.
Examples: Bolek and Lolek with blurred faces.
I was wondering how ChatGPT came up with Bolek and Lolek with their faces painted over. I didn’t suggest anything like that in the prompt. I’m guessing that ChatGPT inferred the atmosphere of “B-movie horror” from keywords such as the 1990s, a neighborhood vibe, fairytale elements, and surrealism, and then, based on the combination of the Polish People’s Republic and fairytale elements, came up with Bolek and Lolek and merged it all into one.
The idea of “Bolek and Lolek” with their faces painted over is, in my opinion, brilliant and original. I suspect that a hand-drawn illustration in this style would be perceived by many people as something highly creative. Although in this case, it is the result of chance.
I have therefore described here a rudimentary mechanism somewhat similar to actual creativity (but still far from human creativity). To achieve greater “creativity” in a language model, it is crucial to use appropriate prompts and a knowledge base; only when these requirements are met can the temperature slider help somewhat.
A higher language model temperature may affect the creativity of the person using it.
Random, nonsensical texts can stimulate human creativity. Sometimes a model may accidentally come across something good, and sometimes even nonsensical texts can inspire a person to do something. An example could be texts created by cutting up a newspaper and then arranging sentences from randomly selected words. Such an activity is certainly creative, but we would not call a bag of random words creative. The creativity here lies in coming up with a way of writing that differs from the standard way of writing texts. This effect is interesting when encountered rarely—mass-producing texts by randomly selecting words would simply be boring.
A language model set to a high temperature can provide a person with useful context that helps find interesting solutions.
What Is Creativity?
Creativity is the ability to create new things—not necessarily groundbreaking or great, but things that did not previously exist in a given context. It is a way of thinking that allows us to see more possibilities than just the obvious ones, question established patterns, and propose a different path. It appears in art, science, business, education, and everyday life—in fact, wherever something needs to be invented, improved, interpreted, or combined in a new way.
You don’t have to be an artist or a visionary to be creative. Creativity is a skill that manifests itself every day—in how we solve problems, how we cook, how we hold conversations, how we raise children, or how we organize our work. When someone comes up with a game out of nothing or resolves a conflict through an unconventional approach, they are being creative.
What makes us creative?
At the heart of creativity lies curiosity—the inner need to discover, understand, and explore the unknown. Creative people ask questions that would not occur to others, are not afraid to make mistakes, and treat failure as an opportunity to learn. Their thinking is not linear; they can change perspectives, recognize connections between seemingly distant phenomena, and find meaning in unexpected places.
Contrary to appearances, creativity is not just a flash of insight or inspiration. It also involves patience and the ability to refine ideas. An original idea is only the beginning—it is equally important to develop it, adapt it to the situation, and give it a tangible form. This is where four traits that psychologists attribute to creative thinking come into play:
- fluency – the ease of generating many different ideas;
- flexibility – the ability to switch between different ways of thinking and approaches;
- originality – the ability to come up with unique, surprising solutions;
- elaboration – developing, detailing, and refining concepts.
Some people have a natural aptitude for it, but creativity is not something confined to our genes. It can be exercised, developed, and strengthened—just like muscles.
How to Develop Creativity?
Creativity training does not require special conditions, but it does need one thing: openness. The more stimuli, the more inspiration. The more attempts, the greater the chance that something will click. This does not mean that you have to live chaotically—quite the opposite. Creativity often arises within constraints, under pressure, and with limited resources. Limitations can be a trigger for ingenuity because they force you to think outside the box.
If you want to support your creativity, you can:
- write down even the strangest ideas every day, without judging them;
- talk to people with different views, because new perspectives open up new paths;
- expose yourself to art, science, technology, and anything that differs from your everyday routine;
- ask yourself questions like “what if…” and test the answers in practice;
- play with convention, parody, and juggle meanings.
Over time, creativity ceases to be something external—it becomes a habit, a way of responding to reality.
Creativity at Different Scales
Not every creative work changes the world—and it doesn’t have to. Psychology distinguishes several levels of creativity. The first is everyday, personal ingenuity—something that has value for you but does not necessarily matter to the world. The second level is professional creativity: specialized creativity developed over years in a particular field. The third is groundbreaking creativity—creativity that creates new styles, discoveries, and directions. But each of these levels is important, because each responds to a real need.
Why Is It Worth Developing Creativity?
Creativity helps us cope with change more effectively. It helps us see opportunities where others see only obstacles. It facilitates communication by opening us up to metaphors, analogies, and other ways of expressing ourselves. It enables us to resolve conflicts, make decisions, and find meaning. And perhaps most importantly, it makes life more interesting.
There is no single definition of creativity, because it itself eludes definitions. But if you look at something familiar and ask yourself, “Could it be done differently?”—you’re on the right track.
Creativity Is Not Just Chaos and Joyful Creation.
When we hear the word “creativity,” the first things that come to mind are art classes, painting, play, puzzles, designing crazy devices from everyday objects, and so on. Some people may associate creativity with MacGyver, Bear Grylls, or survival in general and makeshift repairs.
Creativity, however, also has applications in the most exact fields of life, such as physics and mathematics. A mathematician solving complex problems adheres strictly to established frameworks and does not randomly decide that 2+2=5 or that the sum of the angles of a triangle is not 180 degrees. Nevertheless, it is possible to create a consistent system in which 2+2 = 5 (non-classical algebras), or in which the sum of the angles of a triangle is not 180 degrees (non-Euclidean geometry, e.g. a triangle drawn on a sphere). Both cases have practical applications. c
Thanks to experience, people know when they can create freely (e.g., during brainstorming) and when they must adhere strictly to the rules.
Temperature and Creativity – Practical Applications
Well then—if it’s not about creativity, when and how should temperature be used in practice?
Chatbot based on your own knowledge (e.g. documentation, instructions)
You have a company bot that is supposed to answer customers’ questions about your product based solely on internal documentation.
Temperature: 0.0
Do you want the bot to:
- adhered to the source content,
- did not guess,
- did not “create” their own versions of the answers.
This is precisely the temperature
0
makes the answers deterministic—that is, always the same given the same data. No fabrication.
Creating multiple versions of the same text
You need several alternative versions of a welcome email, advertising slogan, or introduction to an article.
Temperature: 0.7–1.2
Here, you care about diversity, but still within reasonable limits. The temperature allows the model to “let loose” a little and explore unusual phrasing.
Literary Experiments, RPGs, Narrative
You’re writing a story, leading a narrative in a role-playing game, or looking for unconventional ideas.
Temperature: 1.3–1.8
Here, a high temperature can produce interesting results—provided you accept that not every response will make sense. This requires selection, testing, and sometimes manual correction.
Testing: what does it look like in practice?
If you want to test how temperature works yourself, I recommend:
- Google AI Studio (Makersuite) You can upload your own data, set a prompt, and change the temperature in real time.
- OpenAI Playground A more advanced, paid tool that allows precise control over generation parameters.
- Hugging Face Transformers For more technical users—you can see how models behave with different settings.
Gemini flash 2.0, prompt: “Write 10 blog article ideas for a bicycle rental company—headings only.”
On the left, the temperature is set to zero; on the right, to two.
In this case, there is no significant difference. Therefore, there is no greater creativity apparent. And if this task were assigned to two people, one not very creative and the other highly creative, the difference should be visible at first glance.
Things look different when we ask the model to be more creative.
The difference is also apparent when we ask the model to perform a “creative” task, such as writing a story.
On the left, the model with the lower temperature creates a generic story about Mr. Ambroży, who is picking mushrooms. On the right, the model with the higher temperature delivers a much more interesting narrative.
A model with a lower temperature uses words such as “story” and “narrative” in the text because they are close to the word “tale.” A model with a higher temperature correctly determined that there was no need to use such words. Paradoxically, the more creative and chaotic model was more factual and accurate in this case. From its tale, we learn that the main part of the fungus is underground and that fungi communicate with one another; there are no major errors either. The tale about Mr. Ambrose mentions communication among fungi, but in the context of a fairy-tale conversation between the fungi and Ambrose.
Some sentences are simply stupid as well; for example: “His brown velvet hat shone proudly, while the mighty stem seemed to support the entire forest floor” makes no sense. It is merely a mechanical calque of common sentences in stories about forests, such as “the mighty trunk of an oak tree, which seems to support the entire forest undergrowth.”
Aside: what else influences the model’s behavior?
It is worth remembering that temperature is only one of many factors that influence how the model generates responses.
Others include:
- Prompts used, knowledge provided, and context
- Model selection, multi-step reasoning.
- Top-p (nucleus sampling) – limits the selection to tokens that together account for, for example, 90% probability,
- max tokens – maximum response length,
- stop sequences – points at which the model should stop,
- presence penalty / frequency penalty – influence on phrase repetition.
In practice, a good result comes from a combination of parameters, not just temperature alone.
Temperature and response quality: don’t be misled
One of the biggest misconceptions:
“If I raise the temperature, the answers will be better.”
No. They will be different. Maybe more interesting, maybe stranger, maybe funnier—but not necessarily better.
Quality depends mainly on:
- the prompt (i.e., the instructions),
- source data,
- context (i.e., previous interactions),
- the model (GPT-3.5, 4, Claude, Mistral, etc.),
- and only finally—the temperature.
Is It Worth Experimenting with Temperature?
Definitely yes—but thoughtfully.
If you are creating a solution for customers (e.g., a helpdesk or company chatbot), first test at very low values (
0.0
do
0.3
).
If you’re building something creative, you can gradually increase it and observe at what point the model begins to lose coherence.
Pro tip: Create several versions with different temperatures and compare the results. This is the best way to learn how this parameter actually works.
So what is the best term for temperature?
In practice, we cannot avoid comparisons to creativity. After all, even Google suggests to users that a higher temperature means greater creativity. However, where we have more space (e.g. in a LinkedIn post or in a video explaining this concept), it is useful to point out that a higher temperature means greater randomness and chaos, while a lower temperature means more repeatable results. We use a lower temperature when, for example, we enter our instruction into a language model and want ChatGPT to perform the task mechanically.
Summary
Temperature in language models is a randomness regulator, not a “creativity slider.”
The higher the temperature:
- the greater the chance of unusual words,
- the less predictable (and potentially less coherent) the response,
- the greater the risk of nonsense.
In practice:
- For precise chatbots: set up
0.0
- To create different versions: use
0.7 – 1.0
- For literary experiments: test
1.2+
Don't be afraid to experiment—but remember that temperature is just a tool. It won't replace a well-thought-out prompt or make AI “smarter.”
And now… try it yourself. Change the temperature, observe, and note the differences. The best way to understand AI is to have a conversation with it.