---
schema: 1
type: term
title: _logit_
description: >-
  _logit_ — in this channel's captured material — a raw, unnormalized score a
  model computes for each possible next token, before those scores are converted
  into probabilities (e.g., by softmax). It is the pre-probability value behind
  a model's word choices.
canonical: 'https://liquidgold.cc/channel/everyinc/explore/term/logit-4f6367af1b66'
markdown: 'https://liquidgold.cc/channel/everyinc/explore/term/logit-4f6367af1b66.md'
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \_logit\_

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/logit-4f6367af1b66

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

_logit_ — in this channel's captured material — a raw, unnormalized score a model computes for each possible next token, before those scores are converted into probabilities (e.g., by softmax). It is the pre-probability value behind a model's word choices.

**_logit_** appears in this channel's captured material in the context of how a model turns internal numbers into word choices. Before a language model produces its next word, it computes a raw score — a _logit_ — for every token in its vocabulary; only after those scores are transformed (typically via softmax) do they become the familiar probabilities behind sampling. In this channel's material, _logit_ functions as that raw, pre-probability score: the intermediate value you would point to when explaining that a model's output is not magic but a ranked set of numbers that get normalized into probabilities. It belongs to the same family of mechanical, inside-the-model vocabulary as _token_, _probability_, and _sampling_, and it is useful for a reader who wants to understand what is actually happening under the hood when a model generates text — rather than treating the output as an opaque oracle.