For the complete documentation index, see [llms.txt](https://docs.parasail.io/parasail-docs/llms.txt). This page is also available as [Markdown](https://docs.parasail.io/parasail-docs/api-reference/parameters.md).

Parasail supports all parameters that vLLM supports. These parameters control the randomness, diversity, and length of model outputs.

## Sampling parameters

### `temperature` (default: 1.0)

Controls randomness in token selection.

- Higher values (>1.0) increase randomness.
- Lower values (<1.0) make outputs more deterministic.
- A value of 0 forces greedy decoding.

### `top_p` (Nucleus Sampling) (default: 1.0)

Controls the probability mass of token selection.

- Only considers tokens that sum up to `top_p` probability.
- Lower values (for example, 0.9) limit token choices to more likely options.
- Higher values (close to 1.0) allow a broader range of tokens.

### `top_k` (default: -1, disabled)

Limits token selection to the top `k` most probable tokens.

- Lower values (for example, `top_k=50`) make output more deterministic.
- If `-1`, this setting is ignored.
- Since `top_k` is not in the OpenAI spec, pass it in the `extra_body` field.

### `max_tokens` (default: None)

Sets the maximum number of tokens to generate. Helps prevent excessively long responses.

### `repetition_penalty` (default: 1.0)

Penalizes repeated tokens to avoid looping responses. Common values: 1.1 to 1.2.

### `presence_penalty` (default: 0.0)

Increases the likelihood of introducing new tokens. Useful for making outputs more diverse.

### `frequency_penalty` (default: 0.0)

Penalizes tokens that have appeared frequently. Helps prevent excessive repetition of common words.

### `seed` (default: None)

Sets a fixed seed for reproducible results. Useful for debugging or deterministic sampling.

## How parameters work together

- Setting `temperature=0` forces deterministic output (greedy decoding).
- Using `top_p` and `top_k` together balances diversity and coherence.
- `repetition_penalty` and `presence_penalty` help avoid repetitive loops.

## Example

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

response = client.chat.completions.create(
    model="parasail-deepseek-r1",
    messages=[{"role": "user", "content": "Write a creative story about a robot."}],
    max_completion_tokens=1000,
    temperature=0.7,
    top_p=0.1,
    repetition_penalty=1.1,
    extra_body={"top_k": 50}
)

print(response.choices[0].message.content)
```

## Next steps

- [Chat completions API](https://docs.parasail.io/parasail-docs/api-reference/chat-completions)—full chat completions API spec and endpoint compatibility matrix
- [Structured output guide](https://docs.parasail.io/parasail-docs/guides/structured-output)—constrain outputs to JSON
- [Responses API](https://docs.parasail.io/parasail-docs/api-reference/responses-api)—parameters for agentic, multi-turn calls