Temperature and Top-P: Control AI Creativity (Complete Guide)
Understand temperature and top-p settings in AI models. Learn what these parameters do, how they interact, and exactly what settings to use for different tasks.
Temperature and Top-P: Control AI Creativity (Complete Guide)
If you’ve ever wondered why the same prompt gives you wildly different responses, or why your AI sometimes goes off the rails with creative nonsense, the answer lies in two parameters: temperature and top-p.
These settings control the “creativity” vs. “consistency” tradeoff in AI outputs. Get them right for your use case, and you’ll see dramatically better results. Get them wrong, and you’ll be frustrated by outputs that are either robotically repetitive or chaotically random.
Here’s everything you need to know about these parameters—what they do, how they interact, and exactly what settings to use for different tasks.
What Is Temperature in AI?
Temperature is a setting that controls how “random” or “creative” an AI model’s responses are. It affects the probability distribution the model uses when selecting the next word (or token) in its output.
The Simple Explanation
Think of temperature as a “creativity dial”:
- Low temperature (0.0 - 0.3): Conservative, predictable, focused outputs. The model picks the most likely words.
- Medium temperature (0.4 - 0.7): Balanced between creativity and coherence. Good for most tasks.
- High temperature (0.8 - 1.0+): More creative, varied, sometimes unpredictable outputs. Higher chance of unusual word choices.
The Technical Explanation
Temperature modifies the probability distribution of the model’s next-token predictions. Before selecting a token, the model calculates a probability for every possible next token. Temperature scales these probabilities:
Lower temperature makes the probability distribution sharper—high-probability tokens become even more likely, low-probability tokens become even less likely. This makes the model more deterministic.
Higher temperature flattens the probability distribution—the difference between high and low probability tokens decreases. This makes less likely tokens more viable, introducing more variety (and more risk).
At temperature = 0, the model becomes fully deterministic (always picking the highest-probability token). At very high temperatures, outputs become increasingly random.
Temperature Examples
Same prompt, different temperatures:
Prompt: “Write the opening line of a story about a detective.”
Temperature 0.2:
Detective Sarah Chen stared at the crime scene photographs spread across her desk, searching for the detail that everyone else had missed.
Temperature 0.7:
The rain had washed away most of the evidence, but Detective Sarah Chen could still smell the copper tang of blood mixing with the petrichor.
Temperature 1.2:
Sarah’s coffee tasted like regret that morning—bitter, cold, and entirely too familiar for a Tuesday that had started with a body in the botanical gardens.
Notice how higher temperatures introduce more unusual word choices and unconventional structures.
What Is Top-P (Nucleus Sampling)?
Top-p, also called nucleus sampling, is an alternative approach to controlling output randomness. Instead of scaling probabilities like temperature, top-p filters which tokens are even considered.
The Simple Explanation
Top-p sets a threshold that includes only the most likely tokens whose cumulative probability adds up to the threshold:
- Top-p = 0.9: Consider tokens that together account for 90% of probability mass
- Top-p = 0.5: Consider only tokens that together account for 50% of probability mass
- Top-p = 1.0: Consider all tokens (no filtering)
The Technical Explanation
Here’s how top-p works step by step:
- Model calculates probability for every possible next token
- Tokens are sorted from highest to lowest probability
- Starting from the highest, tokens are accumulated until their combined probability reaches the top-p threshold
- Only tokens within this “nucleus” are considered; everything else is discarded
- The model samples from this reduced set
This approach dynamically adjusts how many options are considered based on the certainty of the prediction. When the model is confident, fewer tokens make the cut. When uncertain, more tokens are included.
Top-P Examples
Same prompt, different top-p settings (temperature fixed at 0.7):
Prompt: “Complete this sentence: ‘The cat sat on the…’”
Top-p = 0.1:
mat. (Very constrained—only the most likely completions considered)
Top-p = 0.5:
warm kitchen counter, watching the rain. (More options, but still common completions)
Top-p = 0.95:
antique bookshelf, tail wrapped around a dusty encyclopedia about ancient Egyptian mythology. (Wider variety, more creative options possible)
How Temperature and Top-P Interact
Here’s where things get interesting—and often confusing. Both parameters affect randomness, and they interact in complex ways.
The Interaction Problem
Using both temperature and top-p creates compounding effects:
- High temperature + low top-p: Temperature increases randomness, but top-p restricts options. This can create unpredictable behavior.
- Low temperature + high top-p: Temperature makes high-probability tokens dominant anyway, so top-p has minimal effect.
- High temperature + high top-p: Maximum randomness—often too chaotic for useful outputs.
Best Practice: Choose One
My recommendation: use one parameter and leave the other at its default.
- If you want to control creativity, adjust temperature and set top-p to 1.0
- If you prefer nucleus sampling, adjust top-p and set temperature to 1.0
Most practitioners prefer temperature because it’s more intuitive and widely supported. OpenAI, Anthropic, and Google all use temperature as their primary creativity control.
Exception: Advanced Use Cases
Some advanced users combine both parameters with careful tuning for specific effects:
- Temperature = 0.8 + Top-p = 0.9: Creative but bounded (avoids extremely rare tokens)
- Temperature = 0.5 + Top-p = 0.8: Moderate creativity with hard cutoff on unlikely options
These combinations require experimentation and aren’t recommended until you understand each parameter independently.
Recommended Settings by Use Case
Here’s a practical reference for common tasks. These settings assume you’re adjusting temperature and leaving top-p at 1.0 (or the API default).
Creative Writing
| Task | Temperature | Reasoning |
|---|---|---|
| Poetry | 0.8 - 1.0 | Unusual combinations, metaphors |
| Fiction brainstorming | 0.7 - 0.9 | Creative variety, unexpected twists |
| Marketing copy | 0.5 - 0.7 | Creative but coherent |
| Blog writing | 0.4 - 0.6 | Readable, natural flow |
Technical and Factual
| Task | Temperature | Reasoning |
|---|---|---|
| Code generation | 0.0 - 0.2 | Correctness matters most |
| Technical documentation | 0.1 - 0.3 | Precision, consistency |
| Data extraction | 0.0 | Deterministic needed |
| Translations | 0.0 - 0.2 | Accuracy over creativity |
| Summarization | 0.2 - 0.4 | Faithful to source |
Business Applications
| Task | Temperature | Reasoning |
|---|---|---|
| Customer support responses | 0.3 - 0.5 | Professional, varied |
| Email drafting | 0.4 - 0.6 | Natural, appropriate |
| Report generation | 0.2 - 0.4 | Consistent, professional |
| Brainstorming ideas | 0.7 - 0.9 | Variety wanted |
Conversational
| Task | Temperature | Reasoning |
|---|---|---|
| Chatbots (general) | 0.5 - 0.7 | Natural conversation |
| Virtual assistants | 0.3 - 0.5 | Helpful, consistent |
| Role-play/character | 0.7 - 0.9 | Personality, expressiveness |
Settings by API
Different AI providers offer temperature and top-p with slightly different ranges and defaults.
OpenAI (GPT-5)
response = client.chat.completions.create(
model="gpt-5",
messages=[{"role": "user", "content": "Your prompt"}],
temperature=0.7, # Range: 0.0 - 2.0
top_p=1.0, # Range: 0.0 - 1.0
)- Temperature range: 0.0 to 2.0 (default: 1.0)
- Top-p range: 0.0 to 1.0 (default: 1.0)
- Recommendation: Use temperature 0.0-1.0 for most tasks; above 1.0 for experimental creativity
Anthropic (Claude 4)
message = client.messages.create(
model="claude-4-sonnet",
max_tokens=1024,
temperature=0.7, # Range: 0.0 - 1.0
top_p=1.0, # Range: 0.0 - 1.0 (called "top_p")
messages=[{"role": "user", "content": "Your prompt"}]
)- Temperature range: 0.0 to 1.0 (default: 1.0)
- Note: Claude’s temperature caps at 1.0 (no higher)
- Recommendation: Same scale as other providers; 0.7 is a good default
Google Gemini (Gemini 3)
response = model.generate_content(
"Your prompt",
generation_config={
"temperature": 0.7, # Range: 0.0 - 2.0
"top_p": 0.95, # Range: 0.0 - 1.0
"top_k": 40, # Additional parameter
}
)- Temperature range: 0.0 to 2.0
- Also offers top_k (limits number of options regardless of probability)
- Recommendation: Start with temperature 0.7, top_p 0.95 (Google’s defaults)
Meta Llama 4 (via Ollama)
curl http://localhost:11434/api/generate -d '{
"model": "llama4",
"prompt": "Your prompt",
"options": {
"temperature": 0.7,
"top_p": 0.9
}
}'- Standard temperature and top_p ranges
- Also supports top_k and repeat_penalty
Common Mistakes and Troubleshooting
Problem: Outputs Are Too Repetitive
Symptoms: Same responses to similar prompts, boring or “safe” outputs
Solution: Increase temperature from 0.3 to 0.5-0.7. If still repetitive, check if you’re caching responses or if your prompt is too constrained.
Problem: Outputs Are Nonsensical or Chaotic
Symptoms: Random tangents, made-up words, incoherent sentences
Solution: Decrease temperature to 0.3-0.5. If using both temperature and top-p, set one to its default. Very high temperatures (>1.0) cause this frequently.
Problem: Code Has Syntax Errors
Symptoms: Generated code doesn’t compile, has typos, mixing languages
Solution: Use temperature 0.0-0.2 for code. Add clear examples of the desired output format. Consider using a code-specialized model if available. Learn more in our guide to using ChatGPT for coding.
Problem: Responses Don’t Follow Instructions
Symptoms: Model ignores format requirements, adds unwanted content
Solution: This is usually a prompting problem, not temperature. Lower temperature slightly (0.3-0.5) and improve prompt clarity. Add explicit constraints.
Problem: Different Results Every Time (Unwanted)
Symptoms: Need consistent outputs but getting variation
Solution: Set temperature to 0.0 for fully deterministic outputs. Note that some APIs also offer a “seed” parameter for reproducibility.
Advanced: Other Sampling Parameters
Beyond temperature and top-p, several other parameters affect output generation:
Top-K
Limits sampling to the k most likely tokens. Unlike top-p, it uses a fixed number rather than probability threshold.
- Low top-k (10-20): More constrained
- High top-k (100+): Nearly equivalent to top-p = 1.0
Useful when you want hard limits on vocabulary diversity.
Frequency Penalty
Reduces likelihood of tokens based on how often they’ve appeared in the output. Helps prevent repetition.
- Range: Usually -2.0 to 2.0
- Positive values: Reduce repetition
- Negative values: Encourage repetition (rare use case)
Presence Penalty
Similar to frequency penalty but applies a one-time penalty regardless of how often the token appeared.
- Good for encouraging topic diversity
- Use when you want the model to explore new subjects
Max Tokens
Controls output length, not creativity, but worth mentioning. Set this based on your expected output length plus buffer.
Practical Experimentation Approach
When tuning settings for a new use case:
- Start with defaults: Temperature 0.7, top-p 1.0
- Generate 5-10 samples with the same prompt
- Evaluate pattern: Too similar? Increase temp. Too chaotic? Decrease.
- Adjust in small increments: +/- 0.1 temperature
- Test edge cases: Try prompts that might break your settings
- Document what works: Keep a reference for your use cases
Frequently Asked Questions
Should I use temperature or top-p?
Use temperature. It’s more intuitive, more widely supported, and easier to reason about. Set top-p to 1.0 (or leave at default) and adjust only temperature.
What’s the best temperature for general use?
0.7 is a solid default for most conversational and writing tasks. Adjust lower for factual/code tasks, higher for creative tasks.
Can I set temperature to exactly 0?
Yes, and it makes the model deterministic (always selecting the highest-probability token). Useful for tasks requiring consistency, like code generation or data extraction.
Why do I get different results with temperature 0?
Some APIs have slight randomness even at temperature 0, or may add small noise for performance reasons. Use the “seed” parameter if available for true reproducibility.
How do temperature settings differ between models?
Models from different providers behave slightly differently at the same temperature. A Claude temperature of 0.7 may feel different from GPT-5’s 0.7. Test and adjust for each model.
Summary: Quick Reference
| Use Case | Temperature | Top-P |
|---|---|---|
| Code generation | 0.0 - 0.2 | 1.0 |
| Data extraction | 0.0 | 1.0 |
| Technical writing | 0.2 - 0.4 | 1.0 |
| Business writing | 0.4 - 0.6 | 1.0 |
| Conversational | 0.5 - 0.7 | 1.0 |
| Creative writing | 0.7 - 0.9 | 1.0 |
| Brainstorming | 0.8 - 1.0 | 1.0 |
| Experimental | 1.0+ | 0.8-0.95 |
Master these parameters and you’ll have much more control over AI outputs. The difference between a good AI integration and a great one often comes down to getting these settings right.
Want to understand more about how AI models work? Check out What Is an LLM? for the fundamentals, dive into Tokens in AI to understand the building blocks of AI language, and learn prompt engineering techniques to get better results.