What's the difference between temperature and top_p?

Temperature reshapes the probability distribution (low temp = sharper / more confident, high temp = flatter / more random). Top_p (nucleus sampling) truncates the distribution to the smallest set of tokens whose cumulative probability is p (e.g., 0.9 = top tokens that together cover 90% of probability mass). Best practice: pick one to control. Temperature is more intuitive; top_p is more precise.

What does temperature 0 actually do?

Greedy sampling — the model always picks the highest-probability next token. This is deterministic given the same prompt and same model version (sometimes called 'greedy decoding'). NOT necessarily 100% reproducible across model updates or even minor floating-point differences in some implementations. For maximum reproducibility, also set seed if your provider supports it.

How do penalties work?

Frequency_penalty subtracts a value from the probability of each token in proportion to how often it's appeared in the output so far. Presence_penalty subtracts a fixed value once a token has appeared, regardless of frequency. Both range -2.0 to 2.0 in OpenAI's API. Positive values discourage repetition; negative encourage it. Use 0.3-0.6 for creative writing to add variety; leave at 0 for technical content.

Should I change settings for different prompts?

Often yes. A single API endpoint serving multiple feature areas (code, creative, JSON) should adjust temperature per request type. Most production apps that get this right have a config map: {code: 0.0, creative: 0.8, json: 0.0, summary: 0.3} and pick based on the operation being performed. Hardcoding one temperature for all uses is a common mistake.

Top_k restricts sampling to the K most probable next tokens. Less commonly used than top_p but works similarly — both prune the distribution. Anthropic's Claude API supports top_k; OpenAI doesn't expose it. Useful for constraining wild sampling at high temperatures: temperature 1.0 + top_k 40 gives variety with a safety floor on coherence.

Are these settings the same across providers?

Mostly. Temperature, top_p, presence_penalty, frequency_penalty work similarly across Anthropic, OpenAI, Google, DeepSeek with subtly different default ranges. Claude defaults to temperature 1.0; GPT defaults to 1.0; Gemini defaults to 0.7-ish. Always check provider docs — values you set should match across providers but defaults often differ. Reasoning models (o1, o3, Claude with extended thinking) override these with internal logic.

AI & Prompt Tools · Free tool

AI Sampling Settings Helper

Find the right temperature, top_p, top_k, and penalties for code, creative, factual, or reasoning prompts. Free, instant — no sign-up needed.

Updated June 2026

Pick a use case and get a recommended sampling configuration for OpenAI-style APIs. Values are starting points—tune from here.

Use case

Temperature

0.90

top_p

0.95

top_k

presence

0.6

frequency

0.3

Rationale

High temperature and top_p encourage novel word choices; presence penalty pushes the model to explore new ideas.

JSON snippet

{
  "temperature": 0.9,
  "top_p": 0.95,
  "top_k": 80,
  "presence_penalty": 0.6,
  "frequency_penalty": 0.3
}

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What it does

Sampling settings — temperature, top_p, top_k, frequency_penalty, presence_penalty — control how an LLM picks each next token from its predicted probability distribution. They're the difference between a model that produces deterministic, focused output (low temperature, restrictive top_p) and one that produces creative, varied output (high temperature, looser top_p). Wrong settings for your use case is one of the most common and easily-fixed quality issues in LLM deployments. A creative-writing app at temperature 0 will produce flat, formulaic output. A code-generation tool at temperature 1.2 will produce hallucinated, syntactically broken code.

The helper takes your use case (code generation, creative writing, factual Q&A, summarization, classification, role-play, translation, JSON output, data extraction) and returns recommended settings with brief rationale. Code generation: temperature 0.0-0.2, top_p 1.0 (you want the most probable tokens — deviation breaks syntax). Factual Q&A: temperature 0.0-0.3 (low variance, accurate retrieval). Creative writing: temperature 0.7-1.0, top_p 0.9 (variety without total chaos). Brainstorming: temperature 1.0-1.5 (maximum exploration). JSON / structured output: temperature 0.0 (deterministic format adherence). Role-play / character: temperature 0.7-0.9, presence penalty 0.3-0.6 (variety without repetition).

Two parameter relationships worth understanding: (1) Temperature and top_p interact — if both are restrictive, output is very narrow; if both are loose, output is chaotic. Most practitioners pick one to control and leave the other at default (temperature is more intuitive). (2) Frequency_penalty (penalizes tokens by how often they've appeared) and presence_penalty (penalizes tokens that appeared at all) help with repetition in longer outputs — useful for poetry, storytelling, brainstorming where you want variety; harmful for technical writing where repetition of correct terms is desired. Defaults of 0 are usually right unless you're seeing repetitive output.

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How to use it

Pick your use case from the menu (code, creative writing, Q&A, JSON, etc.).
Read the recommended temperature, top_p, and penalty settings.
Read the brief rationale for why those values fit your case.
Apply the values in your API call (Anthropic, OpenAI, Google all use similar parameter names).
Iterate: if output is too varied, lower temperature; too repetitive, raise temperature or add presence_penalty.

When to use this tool

Setting up a new LLM-powered feature and choosing initial sampling parameters.
Debugging quality issues — wrong temperature is often the cause when output feels off.
Comparing config across providers (Anthropic, OpenAI, Google all use these parameters with subtle differences).
Onboarding new engineers to LLM API usage — one quick reference for typical settings.

When not to use it

Some endpoints don't support all parameters (e.g., Claude doesn't expose top_k by default in API).
Reasoning models (o1, o3, Sonnet extended-thinking) handle their own internal sampling — most parameters have limited or no effect.
Fine-tuned models often need different settings than their base — don't blindly apply defaults.
When the underlying issue is prompt quality, not sampling — fixing temperature can't compensate for a bad prompt.

Common use cases

Verifying a number or output before passing it on
Quick use during a typical workday
Pre-decision sanity-check on inputs and outputs
Educational use — demonstrating the underlying concept

Frequently asked questions

What's the difference between temperature and top_p?: Temperature reshapes the probability distribution (low temp = sharper / more confident, high temp = flatter / more random). Top_p (nucleus sampling) truncates the distribution to the smallest set of tokens whose cumulative probability is p (e.g., 0.9 = top tokens that together cover 90% of probability mass). Best practice: pick one to control. Temperature is more intuitive; top_p is more precise.
What does temperature 0 actually do?: Greedy sampling — the model always picks the highest-probability next token. This is deterministic given the same prompt and same model version (sometimes called 'greedy decoding'). NOT necessarily 100% reproducible across model updates or even minor floating-point differences in some implementations. For maximum reproducibility, also set seed if your provider supports it.
How do penalties work?: Frequency_penalty subtracts a value from the probability of each token in proportion to how often it's appeared in the output so far. Presence_penalty subtracts a fixed value once a token has appeared, regardless of frequency. Both range -2.0 to 2.0 in OpenAI's API. Positive values discourage repetition; negative encourage it. Use 0.3-0.6 for creative writing to add variety; leave at 0 for technical content.
Should I change settings for different prompts?: Often yes. A single API endpoint serving multiple feature areas (code, creative, JSON) should adjust temperature per request type. Most production apps that get this right have a config map: {code: 0.0, creative: 0.8, json: 0.0, summary: 0.3} and pick based on the operation being performed. Hardcoding one temperature for all uses is a common mistake.
What about top_k?: Top_k restricts sampling to the K most probable next tokens. Less commonly used than top_p but works similarly — both prune the distribution. Anthropic's Claude API supports top_k; OpenAI doesn't expose it. Useful for constraining wild sampling at high temperatures: temperature 1.0 + top_k 40 gives variety with a safety floor on coherence.
Are these settings the same across providers?: Mostly. Temperature, top_p, presence_penalty, frequency_penalty work similarly across Anthropic, OpenAI, Google, DeepSeek with subtly different default ranges. Claude defaults to temperature 1.0; GPT defaults to 1.0; Gemini defaults to 0.7-ish. Always check provider docs — values you set should match across providers but defaults often differ. Reasoning models (o1, o3, Claude with extended thinking) override these with internal logic.

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