AI API Temperature & Top-p Guide 2026 | Creativity Control & Best Practices

What Is Temperature?

Temperature controls how "random" or "creative" the model's output is. It ranges from 0 to 2 (most APIs default to 0.7 or 1.0).

0.0

2.0

Deterministic Balanced Creative Chaotic Random

Key rule: Lower temperature = more predictable, repeatable output. Higher temperature = more diverse, surprising output. For production systems, always test at temperature=0 first.

Recommended Temperature by Use Case

Code Generation

0.0 - 0.2

Deterministic output. Same prompt always produces same code. Essential for unit tests and CI/CD.

Data Extraction

0.0 - 0.3

Structured, consistent results. You want the same entities extracted from similar inputs every time.

Technical Writing

0.2 - 0.5

Factual and precise with minor variation in phrasing. Good for documentation and reports.

Chat & Conversations

0.7 - 1.0

Natural, varied responses. The default range for most chat applications. Balanced and engaging.

Creative Writing

1.0 - 1.4

Stories, poems, marketing copy. More surprising word choices and narrative directions.

Brainstorming

1.2 - 1.8

Wild, unconventional ideas. High diversity for ideation sessions. Filter manually afterward.

What Is Top-p (Nucleus Sampling)?

Top-p is an alternative to temperature. Instead of scaling all token probabilities, it only considers tokens whose cumulative probability reaches a threshold p.

top_p = 1.0 — Consider all possible tokens (no filtering)
top_p = 0.9 — Only consider tokens in the top 90% cumulative probability mass
top_p = 0.1 — Only the most likely tokens, very conservative

Recommendation: Use temperature OR top-p, not both. The OpenAI docs suggest setting the one you're not using to 1.0. In practice, most developers just use temperature and leave top_p at 1.0.

Temperature vs Top-p: When to Use Which

Scenario	Use	Why
Need exact same output every time	Temperature = 0	Greedy decoding, no randomness
Need some variety but bounded	Top-p (0.7-0.95)	Filters out truly bizarre tokens
Need full creative range	Temperature (1.0+)	Scales all probabilities uniformly
Testing prompt consistency	Temperature = 0	Same prompt = identical output

Code Example: Temperature in Practice

from openai import OpenAI

client = OpenAI(api_key="YOUR_KEY", base_url="https://tokenease.io/v1")

# Deterministic code generation
code = client.chat.completions.create(
    model="deepseek-tc",
    messages=[{"role": "user", "content": "Write a Python function to reverse a string"}],
    temperature=0,
    max_tokens=200
)

# Creative marketing copy
copy = client.chat.completions.create(
    model="k3",
    messages=[{"role": "user", "content": "Write a catchy headline for a running shoe"}],
    temperature=1.2,
    max_tokens=50
)

Common Mistakes

Mistake #1: Using temperature=0.7 for everything. Code generation at 0.7 produces inconsistent, buggy output. Always match temperature to the task.

Mistake #2: Setting both temperature and top-p to non-default values. They interact in complex ways. Pick one, set the other to 1.0.

Mistake #3: Expecting temperature=0 to be 100% deterministic across model versions. A model update can change output even at T=0.

Mistake #4: Not testing edge cases. A prompt that works at T=0.5 might hallucinate at T=1.2. Test your full temperature range.

Other Sampling Parameters

max_tokens

Hard limit on output length. Always set this to prevent runaway generation. For chat, 1000-2000 is usually sufficient. For long-form writing, you may need 4000+.

presence_penalty & frequency_penalty

Discourage repetition. Presence penalty penalizes tokens that have appeared at all. Frequency penalty penalizes tokens proportionally to how often they've appeared. Range: -2.0 to 2.0. Default: 0. Use 0.1-0.3 to reduce repetition without breaking coherence.

seed

For reproducibility. Setting a seed with temperature=0 (or low) gives more consistent results across calls. Not all providers support this.

What Is Temperature?

Recommended Temperature by Use Case

Code Generation

Data Extraction

Technical Writing

Chat & Conversations

Creative Writing

Brainstorming

What Is Top-p (Nucleus Sampling)?

Temperature vs Top-p: When to Use Which

Code Example: Temperature in Practice

Common Mistakes

Other Sampling Parameters

max_tokens

presence_penalty & frequency_penalty

seed

Experiment with All Models