POST /v1/completions

The model to use for completion. The model value may include supported model suffixes, including web search (':online', ':online/<provider>'), memory (':memory', ':memory-<days>'), reasoning visibility (':reasoning-exclude'), thinking variants where listed by the model catalog (':thinking'), and provider routing preferences for eligible models (':fast', ':cheap', ':caching', etc.).

Billing override to force pay-as-you-go without an explicit provider, or to apply saved provider preferences to subscription-included traffic. Accepted values (case-insensitive): paygo, pay-as-you-go, pay_as_you_go, paid, payg.

Alias for billing_mode.

The text prompt to complete

Upper bound on generated tokens. If omitted, NanoGPT does not enforce an explicit default and the routed provider/model default applies

Classic randomness control. Accepts any decimal between 0-2. If omitted, NanoGPT does not force a value and the routed provider/model default applies

Nucleus sampling. When set below 1.0, trims candidate tokens to the smallest set whose cumulative probability exceeds top_p. Works well as an alternative to tweaking temperature

Whether to stream the response

Penalizes tokens proportionally to how often they appeared previously. Negative values encourage repetition; positive values discourage it

Penalizes tokens based on whether they appeared at all. Good for keeping the model on topic without outright banning words

Penalizes tokens that already appear in the conversation, on most providers including the prompt. 1 turns it off and values above 1 discourage repetition. Values below 1 encourage repetition and can cause misspelled words and endless loops, especially with long prompts. Unlike frequency_penalty and presence_penalty, the neutral value is 1, not 0

Caps sampling to the top-k highest probability tokens per step

Combines top-p and temperature behavior; leave unset unless a model description explicitly calls for it

Ensures each candidate token probability exceeds a floor (0-1). Helpful for stopping models from collapsing into low-entropy loops

Tail free sampling. Values between 0-1 let you shave the long tail of the distribution; 1.0 disables the feature

Cut probabilities as soon as they fall below the specified tail threshold

Cut probabilities as soon as they fall below the specified tail threshold

Typical sampling (aka entropy-based nucleus). Works like top_p but preserves tokens whose surprise matches the expected entropy

Enables Mirostat sampling for models that support it. Set to 1 or 2 to activate

Mirostat target entropy parameter. Used when mirostat_mode is enabled

Mirostat learning rate parameter. Used when mirostat_mode is enabled

For providers that support it, enforces a minimum completion length before stop conditions fire

When true, keeps the stop sequence in the final text. Not supported by many providers

Allows completions to continue even if the model predicts EOS internally. Useful for long creative writing runs

Extension that forbids repeating n-grams of the given size. Not supported by many providers

Requests logprobs on the prompt itself when the upstream API allows it

Optional integer forwarded on model/provider routes that support seeded sampling. This may improve reproducibility but does not guarantee identical output. Results can change if NanoGPT selects a different automatic or fallback route, or if the provider changes its backend.

Unique identifier for the completion

Object type, always 'text_completion'

Unix timestamp of when the completion was created

Model used for completion