LLM Parameter Playground
Experiment with temperature, top-p, frequency penalty, and other LLM parameters with visual explanations
Presets
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Controls randomness in output. Higher values make output more creative/random, lower values make it more focused/deterministic.
Creative — more diverse and surprising outputs
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Limits token selection to a cumulative probability. Lower values restrict to higher-probability tokens. Alternative to temperature.
Unrestricted — full vocabulary considered
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Penalizes tokens based on how often they appear in the output so far. Positive values reduce repetition.
Neutral — natural repetition patterns
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Penalizes tokens that have appeared at all in the output. Encourages the model to talk about new topics.
Neutral — natural topic flow
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Maximum number of tokens to generate in the response. One token is roughly 4 characters or ¾ of a word.
~3072 words
Configuration JSON
{
"temperature": 1,
"top_p": 1,
"frequency_penalty": 0,
"presence_penalty": 0,
"max_tokens": 4096
}What is an LLM Parameter Playground?
An LLM parameter playground lets you experiment with the settings that control how large language models generate text — temperature, top-p (nucleus sampling), frequency penalty, presence penalty, max tokens, and more. These parameters dramatically affect output quality, creativity, and consistency, but their interactions are often misunderstood.
Most developers start with default parameter values and never adjust them. This works for basic use cases, but tuning parameters for your specific task can mean the difference between a chatbot that feels robotic and one that feels natural, or between code generation that is reliable and code that is full of creative but broken syntax.
Our free playground provides interactive visualizations of each parameter, showing how they affect the probability distribution of the model's next-token selection. Adjust sliders, see real-time visual feedback, experiment with presets for common use cases, and export your configuration as API-ready JSON. All processing happens in your browser with no data sent to any server.
How to Use This Playground
Exploring LLM parameters is intuitive:
- Start with a preset — Choose from presets optimized for common tasks: precise (code, factual Q&A), balanced (general conversation), creative (writing, brainstorming), or maximum diversity (idea generation). Each preset sets all parameters to recommended values.
- Adjust individual parameters — Use the sliders to modify each parameter. The visualization updates in real-time to show how the probability distribution changes. Hover over any parameter name for a detailed explanation.
- Observe the probability visualization — The interactive chart shows a simulated token probability distribution, illustrating how your parameter choices affect which tokens the model is likely to select. This makes abstract concepts like "nucleus sampling" visually concrete.
- Compare configurations — Save multiple configurations and compare them side by side to understand how parameter changes affect output characteristics.
- Export your configuration — Copy your parameters as a JSON object formatted for OpenAI, Anthropic, or Google APIs. The exported config is ready to paste directly into your code.
Understanding Each Parameter
Each parameter controls a different aspect of text generation. Understanding them individually and in combination is key to getting the outputs you want.
Temperature (0.0 - 2.0)
Temperature is the most important parameter for controlling randomness. It scales the logits (raw model predictions) before the softmax function converts them into probabilities. At temperature=0, the model always picks the single most likely token — outputs are deterministic and repetitive. At temperature=1.0 (default), the original probability distribution is used. At temperature=2.0, the distribution is nearly flat — almost any token could be selected, leading to highly creative but often incoherent output.
Top-p / Nucleus Sampling (0.0 - 1.0)
Top-p filters the token selection pool by cumulative probability. At top_p=0.1, only the tokens that together account for the top 10% of probability mass are considered. At top_p=1.0 (default), all tokens are eligible. Top-p is useful because it adapts dynamically — when the model is confident (one token has 90% probability), top_p=0.95 still selects deterministically. When the model is uncertain, the same setting allows more diversity.
Frequency Penalty (-2.0 to 2.0)
Frequency penalty reduces the probability of tokens that have already appeared in the output, proportional to how often they appeared. A value of 0.5 moderately discourages repetition. Higher values (1.0-2.0) strongly penalize repeated tokens, which can improve vocabulary diversity but may cause the model to avoid necessary repetition of technical terms or variable names.
Presence Penalty (-2.0 to 2.0)
Presence penalty applies a flat penalty to any token that has appeared even once, regardless of frequency. Unlike frequency penalty, it does not increase with repetitions. A value of 0.5 encourages the model to introduce new topics and vocabulary. This is useful for creative writing and brainstorming, where you want the model to explore widely rather than stay focused.
Max Tokens
Max tokens sets the hard upper limit on response length. This is not a target — the model may stop earlier if it reaches a natural conclusion. Setting max tokens prevents unexpectedly long (and expensive) responses. For most API calls, setting this explicitly is a best practice for cost control.
Recommended Presets by Use Case
These presets serve as starting points — fine-tune from here based on your specific needs:
- Code generation — temperature=0.1, top_p=0.95, frequency_penalty=0, presence_penalty=0. Deterministic output with consistent syntax.
- Factual Q&A — temperature=0.2, top_p=0.9, frequency_penalty=0, presence_penalty=0. Slightly more variation than pure deterministic, but still very focused.
- General conversation — temperature=0.7, top_p=0.95, frequency_penalty=0.3, presence_penalty=0.1. Natural-sounding responses with mild anti-repetition.
- Creative writing — temperature=1.0, top_p=0.95, frequency_penalty=0.5, presence_penalty=0.5. High variety with strong encouragement for diverse vocabulary.
- Brainstorming — temperature=1.3, top_p=0.98, frequency_penalty=0.8, presence_penalty=0.8. Maximum diversity for idea generation. Review output carefully.
Frequently Asked Questions
What is the difference between temperature and top_p?
Temperature and top_p both control randomness in model outputs, but they work differently. Temperature scales the probability distribution — higher values flatten it (more random), lower values sharpen it (more deterministic). Top_p (nucleus sampling) truncates the distribution by only considering tokens whose cumulative probability reaches p. At temperature=0, the model always picks the most likely token. At top_p=0.1, the model only considers the top 10% most likely tokens. Most providers recommend adjusting one or the other, not both simultaneously.
What is the difference between frequency penalty and presence penalty?
Frequency penalty reduces the likelihood of tokens proportionally to how many times they have already appeared — the more a word repeats, the stronger the penalty. Presence penalty applies a flat penalty to any token that has appeared at all, regardless of frequency. Use frequency penalty to reduce excessive repetition of specific words. Use presence penalty to encourage the model to explore new topics and vocabulary. A small frequency penalty (0.3-0.5) is often enough to reduce annoying repetitions without hurting quality.
What are the best parameter settings for code generation?
For code generation, low randomness produces the best results. Start with temperature=0.1-0.2 and top_p=0.95. Code needs to be syntactically correct and logically consistent, so deterministic outputs are preferred. Set frequency_penalty=0 because code naturally repeats keywords and variable names. For creative code (generating multiple solutions or brainstorming approaches), you can increase temperature to 0.5-0.7, but going above 0.8 tends to produce invalid syntax.
Can I share my parameter configurations with my team?
Yes. The playground includes a "Copy Config" button that exports your current parameter settings as a JSON object compatible with the OpenAI, Anthropic, and Google API formats. You can share this JSON with teammates, paste it into your codebase, or save it for later. The tool also provides a shareable URL that encodes your parameters, so you can bookmark specific configurations or send them to colleagues.
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