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ChatGPT temperature is the setting that controls how randomly the model picks each word. That is why ChatGPT gives different answers when you repeat a question. In the app, however, you can’t change ChatGPT temperature, because that setting lives in the API. Also, memory, search, plan and updates change the answer.
In short
- Temperature controls how each word gets picked. In the OpenAI API, it ranges from 0 to 2, but current models only accept it with reasoning turned off.
- ChatGPT settings offer no way to change the temperature, and OpenAI does not say which value it uses. Writing “temperature 0” in the prompt only changes the text of the request.
- Memory, location, plan and search change ChatGPT answers from person to person. In a Profound study, answers to prompts that described different profiles shared only about 1 in 4 brands.
- So a screenshot proves nothing. What holds steady is how often each brand appears, and only repeated questions show that number.
Why does ChatGPT give different answers to the same question?
Because the model does not always pick the most likely word. Instead, it draws from the plausible next words, and ChatGPT temperature controls that draw. In addition, web search, memory, location, plan and updates change the path of each answer. So ChatGPT gives different answers even when the question is identical.
OpenAI itself describes the mechanism in its Help Center. In the company’s example, the sentence “Instead of turning left, she turned ___” can end in several plausible ways. That is where the variation comes from:
“Because there are multiple plausible ways to complete a sentence […] there is an inherent element of randomness in how the model responds. As a result, the same question may yield different answers across different queries.”
OpenAI, Help Center (2026)
In fact, you can see this in the app. In the refresh control of an answer, the Try again option creates another version for the same request. However, each cause changes a different part of ChatGPT answers:
| Cause | What changes | Who controls it |
|---|---|---|
| Word sampling | Words, order and brands | OpenAI |
| Web search | Sources of the answer | The model or the user |
| Memory and instructions | Context and search query | The user |
| Location | Local results | The IP of the person asking |
| Plan | The model that answers | The account |
| Updates | Model behavior | OpenAI |
Each piece, from training to memory, is covered in the guide on how ChatGPT works. The decision to search, which swaps the sources of the answer, is in the guide on when ChatGPT searches the internet.
What is ChatGPT temperature, and can you change it?
Temperature is a sampling setting: it defines how much risk the model takes when it picks the next word. The OpenAI API accepts values from 0 to 2, and low values make the output more focused. In the app, however, no one can change ChatGPT temperature, and OpenAI does not publish the value it uses.
In the API reference, OpenAI explains that values like 0.8 make the output more random. On the other hand, values like 0.2 make it more focused and deterministic. The same page lists top_p, another control over the draw. With 0.1, for example, the model only considers the top words that add up to 10% of the probability. But the company recommends changing only one of the two.
In the app, users control the style, not the draw. In Personalization settings, they pick a style and tone and adjust traits like enthusiasm and emoji use. Among these controls, listed in OpenAI’s release notes, there is no ChatGPT temperature setting.
Even in the API, the setting does not work for every model. The guide to current models says to remove temperature from the request whenever the model reasons. Two of them, including the most advanced, never accept the setting. Likewise, the newest Anthropic models reject non-default values, and Google models ignore the parameter. In other words, temperature explains the draw, but it is not a knob a brand can turn.
Does ChatGPT temperature 0 make answers identical?
No, not even in the API. Anthropic warns that even at temperature 0, results are not fully deterministic. Google says something similar: at 0, the model always picks the most likely word, but some small variation can remain.
The Thinking Machines lab measured the effect on an open model, Qwen3-235B. It ran the same request 1,000 times at temperature 0 and got 80 different answers. According to the lab, the main cause is server load, because the number of requests processed alongside yours changes the math.
In an academic study of five models, including GPT-4o, accuracy varied by up to 15% across identical runs, with temperature 0 and a fixed seed. Therefore, not even the most cautious setting guarantees the same answer.
Can you set ChatGPT temperature in the prompt?
Not as a real setting. In the API, temperature is a field in the request, separate from the text of the question. So writing “use temperature 0” in ChatGPT becomes just one more instruction. The model may even answer in a more sober way because you asked. But OpenAI does not document any command to change ChatGPT temperature through the chat.
Are ChatGPT answers the same for everyone?
No. With memory on, ChatGPT can use past chats, saved memories and custom instructions, even to build the search query. The location estimated from the IP changes local results, and the plan sets the model. So the brand owner and their client can get different lists for the same question.
According to OpenAI, depending on plan and region, memory can also include files in the Library and connected apps. In addition, ChatGPT itself decides what is relevant for each answer. So anyone who has already chatted with ChatGPT about their own company may carry that context into the test.
Location matters for local questions, and a VPN or the network you use can change the region ChatGPT estimates from your IP. The plan also counts: according to the release notes, Free and Go use a different model from Plus and Pro. On Plus and Pro, Instant can also switch on its own to a higher reasoning level if the user leaves that option on.
When the prompt describes the person asking, the list changes even more. Profound, which sells AI monitoring, analyzed 71,147 answers from ChatGPT, Claude and Gemini about clothing, furniture and credit cards. Each prompt got a profile, with gender, age, income or occupation. As a result, answers for different profiles shared only about 1 in 4 brands.
How often does the brand list repeat in ChatGPT answers?
The full list almost never repeats, and the order changes even more. Still, the brands the model remembers best come back in most ChatGPT answers. That is why you can safely measure how often each brand appears, but not its position on the list.
In the SparkToro study, 600 volunteers made 2,961 runs of 12 prompts in ChatGPT, Claude and Google’s AI. The chance of ChatGPT repeating the same list of brands was below 1 in 100, and the chance of repeating the order was close to 1 in 1,000. The study involved Gumshoe.ai, which sells AI tracking.
Still, frequency held up. In ChatGPT, City of Hope hospital in Los Angeles showed up in 69 of 71 answers, but it led the list in only 25. In another round, volunteers wrote 142 prompts about headphones, almost all different from each other. Even so, Bose, Sony, Sennheiser and Apple appeared in 55% to 77% of the 994 answers.
Profound saw the same pattern with fixed profiles. Repeated answers for the same profile shared about 2 in 5 brands and 34% of cited domains. For a brand, this has two effects. First, a single screenshot can show the brand by luck or hide it by bad luck. Second, the position on the list says nothing. How many answers to collect, and with what margin, is in the guide on how to measure brand mentions and citations.
What changes in ChatGPT answers when the model is updated?
The model’s behavior changes for every user on that plan. In April 2025, for example, OpenAI rolled back a GPT-4o update in ChatGPT because answers had become too agreeable. Then, in June 2026, another update changed answers about shopping and local businesses. According to the release notes, the model got better at using location to suggest nearby options.
Model swaps are frequent, too. According to OpenAI, a model usually stays in ChatGPT for 90 days after its successor arrives. In addition, when it leaves, even old chats move to the new model. In the API, on the other hand, the company recommends pinning a specific version, because even versions from the same family can answer differently. So write down the date of each round and compare month to month.
How do you test brand presence without relying on one screenshot?
Swap the screenshot for frequency: run the same questions several times, under fixed conditions, and count how many ChatGPT answers mention the brand. Since you can’t change ChatGPT temperature, only repetition separates luck from real presence. You can control the other causes or, at least, write them down.
| Cause of variation | How to control it in the test |
|---|---|
| Word sampling | Repeat each question several times |
| Memory and instructions | Use an unpersonalized temporary chat |
| Location | Fix the city and don’t use a VPN |
| Plan | Test on the plan the client uses |
| Web search | Note whether the answer showed sources |
| Updates | Record the date and compare month to month |
In ChatGPT, a temporary chat only stops using memory and custom instructions if you choose Unpersonalized before the first message. Once the chat starts, however, you can’t change that choice. The quick test, logged out or in a temporary chat, is in the guide on how to check your AI visibility.
When a client shows a screenshot with a competitor, don’t argue about the screenshot. Instead, run the same question several times and show how many answers included each brand. For a first diagnosis, the free GEO audit includes the questions, the engines and the spreadsheet.
What we found analyzing 12 Portuguese pages on ChatGPT temperature
We analyzed the 12 Portuguese-language pages on AI temperature that showed up in 18 searches such as “chatgpt temperatura” (ChatGPT temperature) and “ajustar temperatura chatgpt” (adjust ChatGPT temperature). Only 4 say the ChatGPT app does not let you change the temperature. Two teach a /temperature command that OpenAI does not document. Three cite a default value for ChatGPT, 0.5 or 0.7 to 0.8, with no official source.
Only 2 warn that temperature 0 does not guarantee the same answer, and one says the opposite. Just 1 notes that reasoning models limit the setting. Finally, none cite memory, search or model updates as a cause of the variation, and none discuss brand presence in ChatGPT answers.
Frequently asked questions
OpenAI does not disclose the temperature the ChatGPT app uses, and users can’t change ChatGPT temperature themselves. The documentation treats temperature as an API parameter, from 0 to 2, set by whoever makes the request. So values like 0.5 or 0.7, which circulate in tutorials, have no official source.
Not entirely. On OpenAI’s current models, the API only accepts temperature with reasoning turned off. In addition, Anthropic and Google warn that not even temperature 0 guarantees the same answer. So before you hire a tool, ask how many times it runs each question.
No. Without memory, word sampling, location, plan, search and model updates still matter. An unpersonalized temporary chat removes only one of these causes. So two people without memory can still get different answers from ChatGPT, and the test still needs repetition.
Want to know how your brand shows up in AI answers today?
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Main sources
- OpenAI, How ChatGPT and our foundation models are developed (2026).
- OpenAI, Responses API reference, with the temperature and top_p parameters (2026).
- OpenAI, guide to using GPT-6 models in the API (2026).
- OpenAI, ChatGPT release notes (2026).
- OpenAI, Memory in ChatGPT (2026).
- Thinking Machines Lab, Defeating Nondeterminism in LLM Inference (2025).
- SparkToro, study on how inconsistent AI brand recommendations are (2026).
- Profound, study on AI answers for different personas (2026).
