Large language models usually generate text one token at a time. At each step, the model assigns scores to possible next tokens, those scores become probabilities, and a decoding strategy chooses what comes next.
Two common controls in that process are temperature and top-p. They are often described as creativity settings, but that description is incomplete. They change how the model samples from its probability distribution, which affects repeatability, diversity, and the chance of selecting lower-probability tokens.
Understanding the difference helps developers tune inference without treating the controls as mysterious knobs.
Start with the model’s next-token scores
Before sampling, a language model produces a vector of raw scores called logits. A softmax operation converts those scores into probabilities.
Imagine a simplified next-token distribution:
"database" 0.52
"server" 0.25
"service" 0.13
"cluster" 0.07
"banana" 0.03A greedy decoder always selects the highest-probability token, database in this example. Sampling instead draws from the distribution, so server, service, or another token can sometimes be selected.
Temperature and top-p modify this selection process in different ways.
Temperature reshapes the distribution
Temperature scales the logits before softmax. A common formulation is:
P(token_i) = softmax(logit_i / T)where T is the temperature.
When temperature is below 1, differences between logits become more pronounced. High-probability tokens receive more probability mass, so output tends to be more conservative.
When temperature is above 1, the distribution becomes flatter. Lower-probability tokens become more likely, increasing variation but also increasing the chance of unusual choices.
Very low temperature does not make a model more knowledgeable or logically correct. It only makes its token selection more concentrated around what the model already considers likely.
Top-p limits the candidate set
Top-p, also called nucleus sampling, takes a different approach. Instead of reshaping every token probability, it keeps the smallest set of highest-probability tokens whose cumulative probability reaches a threshold p.
Suppose the sorted probabilities are:
"database" 0.52 cumulative 0.52
"server" 0.25 cumulative 0.77
"service" 0.13 cumulative 0.90
"cluster" 0.07 cumulative 0.97
"banana" 0.03 cumulative 1.00With top_p = 0.90, sampling is restricted to the first three tokens in this simplified example. The low-probability tail is excluded.
Unlike a fixed top-k limit, the number of retained tokens can change at every generation step. A confident distribution may need only a few tokens to reach the threshold, while an uncertain distribution may retain many.
Temperature and top-p solve different problems
It is useful to separate their roles:
- temperature changes how sharp or flat the probability distribution is;
- top-p removes the low-probability tail after probabilities are computed.
They can be used together, but changing both aggressively at the same time makes behavior harder to reason about. When tuning an application, hold one control relatively stable while measuring the effect of the other.
Also check the API or inference engine you use. Providers can define defaults, supported ranges, or decoding details differently.
Match decoding to the task
There is no universally correct setting. The right choice depends on what failure looks like for the application.
For structured extraction, classification-like responses, or code transformations, variation is usually undesirable. A lower temperature and conservative sampling policy can reduce unnecessary differences between runs.
For brainstorming, naming, or drafting alternatives, some variation is useful. A moderate temperature can produce a wider set of candidates without requiring a completely different prompt.
For factual question answering, lowering temperature can make responses more repeatable, but it does not prevent hallucinations. Factual reliability still depends on model capability, context quality, retrieval, tool use, and validation.
Do not confuse determinism with correctness
A model that gives the same answer every time can still give the same wrong answer every time.
This distinction matters in production systems. If an application needs trustworthy values, validate them against authoritative data or constrain the model with tools and structured checks. Decoding settings should not be used as a substitute for verification.
Similarly, increasing temperature does not directly create better ideas. It increases sampling diversity. Whether that diversity is useful depends on the prompt, model, and evaluation criteria.
Measure settings with representative prompts
Manual experimentation with one prompt is not enough for production tuning. Build a small evaluation set that represents the requests your application actually receives.
For each candidate configuration, run prompts multiple times and record measurements such as:
configuration: temperature=0.2, top_p=0.95
runs per prompt: 5
measure:
- task success
- schema validity
- factual error rate
- output diversity
- latency
- token usageMultiple runs are important because sampling is stochastic. A setting that looks good once may have a long tail of poor outputs.
For tasks with machine-checkable answers, use automated assertions. For open-ended tasks, define a rubric before comparing configurations so that reviewers are not simply choosing the response they personally prefer.
Change decoding only after fixing obvious prompt problems
Sampling controls cannot repair unclear instructions. Before tuning them, make sure the prompt defines the task, relevant constraints, expected output format, and necessary context.
If the model frequently violates a JSON schema, for example, first use structured output features or validation where available. Lowering temperature may reduce variation, but it is a weaker guarantee than enforcing the contract directly.
A useful tuning order is:
- define the task and output contract;
- provide the required context;
- establish an evaluation set;
- choose a reasonable decoding baseline;
- change one parameter at a time;
- compare repeated runs against the evaluation criteria.
This keeps decoding changes tied to measurable behavior rather than intuition.
Treat inference settings as versioned configuration
Once an application depends on a particular decoding setup, store it alongside other model configuration:
model_profile = {
model: "example-model-v3",
temperature: 0.2,
top_p: 0.95,
prompt_version: "support-answer-v7"
}When the model, prompt, temperature, or top-p changes, evaluation results from the previous configuration may no longer describe current behavior. Versioning these settings makes regressions easier to investigate and experiments easier to reproduce.
Practical takeaway
Temperature and top-p influence how an LLM chooses among plausible next tokens. Temperature reshapes the probability distribution, while top-p restricts sampling to a cumulative-probability nucleus.
Use them as inference controls, not as accuracy controls. Start from a task-appropriate baseline, change one parameter at a time, test repeated generations on representative prompts, and validate important outputs independently. The best decoding configuration is the one that produces acceptable behavior under measured application requirements, not the one with the most fashionable parameter values.