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AI Temperature: What It Is and When It Matters
When working with generative AI models, one of the core settings you may encounter is temperature. It is a simple parameter that can significantly influence how predictable, creative, or surprising the model’s output becomes.
This article explains what temperature does, where it can be configured, and when it is relevant, especially for professionals using the OpenAI API or building their own applications.
How AI Temperature Influences Output
Temperature determines the level of randomness in the model’s word choices. You can think of it as a setting that influences how narrowly or broadly the model explores its next possible words.
- Low temperature (0.0 to 0.3) results in predictable, conservative output that sticks closely to the most likely phrasing.
- Mid-range values (0.4 to 0.7) allow for more variation in style, tone, and wording.
- High values (0.8 to 1.2 or more) lead to more creative or unpredictable responses, which can also be less consistent.
The model’s factual knowledge remains the same. Temperature only affects how that knowledge is expressed.
Where You Can Adjust the Temperature
1. OpenAI API
Temperature is a configurable parameter in all completions and chat endpoints. You can set it manually by including it in your API request. The default is usually 1.0 unless specified.
Example:
{
"model": "gpt-4",
"messages": [{"role": "user", "content": "Summarise this document"}],
"temperature": 0.3
}
Lower values are useful when you need repeatable results or clear structure. Higher values can help with brainstorming or generating multiple variations.
2. ChatGPT Web Interface
In the standard ChatGPT interface, temperature is not user-adjustable. The value is managed internally by OpenAI and may vary by model. You can still influence output style through prompt design.
3. Custom GPTs
When building a Custom GPT using OpenAI’s GPT Builder, temperature can be configured during setup. This lets you define a consistent style or tone for use cases like internal tools, writing assistants, or ideation bots.
One Prompt, Different Outcomes
Prompt:
“Write one sentence describing a rainy day.”
| Temperature | Output |
|---|---|
| 0.2 | Rain falls steadily on the pavement as the sky stays overcast. |
| 0.7 | The rain taps on rooftops while streets shimmer with reflections. |
| 1.0 | Silver curtains pour from the sky, draping the world in a hushed grey mist. |
All responses are valid. The variation lies in how likely or unusual the phrasing is.
Choosing the Right Setting for the Task
| Situation | Suggested temperature |
|---|---|
| Technical summaries | 0.0 to 0.3 |
| Formal writing | 0.4 to 0.6 |
| Brainstorming ideas | 0.6 to 0.8 |
| Creative generation | 0.8 to 1.2 |
Some applications require predictability. Others benefit from spontaneity. Tuning the temperature lets you steer the model’s behaviour accordingly.
Observed Differences Between ChatGPT Models (as inferred in August 2025)
The ChatGPT app allows switching between different models. The temperature for each model is not shown to the user and not officially published by OpenAI. However, based on observable behavior, these are reasonable estimates.
| Model Name | Approx. Temperature | Notes |
|---|---|---|
| GPT-4o | ~0.7 | Default model for Pro users. Balanced tone and clarity. |
| o3 | ~1.0 | Freer phrasing. Tends toward creative or chatty responses. |
| o3-pro | ~0.9 | Similar to o3, slightly more stable. |
| o4-mini | ~0.7 | Lighter version, tuned similarly to GPT-4o. |
| o4-mini-high | ~1.0 | Appears to generate more varied or unexpected output. |
| GPT-4.5 (preview) | ~0.7 | Preview model. Behavior appears stable and consistent. |
| GPT-4.1 | ~0.7 | Focused output, likely tuned for reliability. |
| GPT-4.1-mini | ~0.7 | Compact, consistent behavior. |
These values are not official and are included here for reference only. They are inferred based on usage patterns and model responses in the current ChatGPT app.
Summary: Why Understanding AI Temperature Helps You Get Better Results
Temperature is one of the few levers that lets you shape how a language model behaves without changing the underlying prompt. Whether you are building your own AI tools or simply experimenting with different outputs, understanding temperature helps you get more control over the results.
It is not a setting most users need to change, but for those working with prompts and APIs at a deeper level, it is worth knowing how it works and when to apply it.
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